With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so ...With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.展开更多
Background Well child visits(WCV)are fundamental to preventive primary care.We examined trends in WCV attendance during the COVID-19 pandemic and characterised variation by patient and provider characteristics.Methods...Background Well child visits(WCV)are fundamental to preventive primary care.We examined trends in WCV attendance during the COVID-19 pandemic and characterised variation by patient and provider characteristics.Methods Deidentified electronic medical records from two academic practice-based research networks in Ontario were used to create age-specific cohorts of children under age six attending WCVs from 2015 to 2022.Patients’residential postal codes were linked to neighbourhood-level measures to estimate socioeconomic status.Monthly visit rates were modelled using segmented linear regression with autoregressive residuals.Changes associated with COVID-19 were assessed using level change and trend change of monthly visit rates.Findings For the 53256 included children,WCV attendance increased from 2015 to 2020 for cohorts aged 15 months and younger and was stable for 18-month,2–3-year and 4–6-year visits.The COVID-19 pandemic was associated with decreased WCV attendance in all ages except ages 1–2 weeks,1 month,12 months,15 months and 18 months,in whom attendance was unchanged.The rate of change in WCV attendance rates pre-COVID-19 compared with post-COVID-19 was unchanged,with the exception of increased rate of change for the 1–2 weeks and 2–3 years old cohorts.Lower attendance rates were observed in children residing in neighbourhoods with the highest material deprivation,rural regions and those whose family physicians were men or older than 65 years.Interpretation Prepandemic gains in WCV attendance were stable or improved after the initial reductions observed at the pandemic onset,suggesting that WCVs were prioritised by family physicians and families.Targeted strategies are needed to improve WCV attendance for vulnerable groups.展开更多
End-stage renal disease(ESRD)significantly impacts patients’quality of life and poses substantial socioeconomic burdens.Dietary interventions are crucial for managing ESRD,yet high-quality evidence and analysis speci...End-stage renal disease(ESRD)significantly impacts patients’quality of life and poses substantial socioeconomic burdens.Dietary interventions are crucial for managing ESRD,yet high-quality evidence and analysis specifically linking diet to mortality outcomes is scarce.Methods:We conducted a comprehensive study involving 656 peritoneal dialysis(PD)patients over 12 years,with an average follow-up every 3 months.Dietary intake was meticulously recorded using a 3-day dietary record method,integrated with detailed health records and outcomes.We employed a 2-stage model to evaluate nonlinear relationships between dietary nutrients and mortality risk,accounting for various confounding factors.Findings:Our analysis revealed that 14 out of 26 nutritional elements lack guidelines for ESRD and PD patients,with 13 showing significant associations with mortality.For example,while guidelines suggest a dietary protein intake of 1.0 to 1.2 g/kg/d,our findings indicate an optimal range of 0.88 to 1.13 g/kg/d.Similarly,the recommended dietary energy intake of 25 to 35 kcal/kg/d was refined to 26 to 42 kcal/kg/d.We identified that 69% of dietary intake-outcome relationships are nonlinear,especially in patients with poor health status.Interpretations:Our study provides detailed dietary intake thresholds that correlate with improved prognosis in ESRD patients,enhancing current guidelines.The findings highlight the importance of personalized nutritional management and underscore the nonlinear nature of nutrient–disease relationships,particularly in severely ill patients.This approach can refine dietary recommendations and improve patient care in ESRD.展开更多
Predicting mortality risk in the Intensive Care Unit(ICU)using Electronic Medical Records(EMR)is crucial for identifying patients in need of immediate attention.However,the incompleteness and the variability of EMR fe...Predicting mortality risk in the Intensive Care Unit(ICU)using Electronic Medical Records(EMR)is crucial for identifying patients in need of immediate attention.However,the incompleteness and the variability of EMR features for each patient make mortality prediction challenging.This study proposes a multimodal representation learning framework based on a novel personalized graph-based fusion approach to address these challenges.The proposed approach involves constructing patient-specific modality aggregation graphs to provide information about the features associated with each patient from incomplete multimodal data,enabling the effective and explainable fusion of the incomplete features.Modality-specific encoders are employed to encode each modality feature separately.To tackle the variability and incompleteness of input features among patients,a novel personalized graph-based fusion method is proposed to fuse patient-specific multimodal feature representations based on the constructed modality aggregation graphs.Furthermore,a MultiModal Gated Contrastive Representation Learning(MMGCRL)method is proposed to facilitate capturing adequate complementary information from multimodal representations and improve model performance.We evaluate the proposed framework using the large-scale ICU dataset,MIMIC-III.Experimental results demonstrate its effectiveness in mortality prediction,outperforming several state-of-the-art methods.展开更多
Background Electronic medical records(EMR)can be utilized to understand the impact of the disruption in care provision caused by the pandemic.We aimed to develop and validate an algorithm to identify persons with epil...Background Electronic medical records(EMR)can be utilized to understand the impact of the disruption in care provision caused by the pandemic.We aimed to develop and validate an algorithm to identify persons with epilepsy(PWE)from our EMR and to use it to explore the effect of the pandemic on outpatient service utilization.Methods EMRs from the neurology specialty,covering the period from January 2018 to December 2023,were used.An algorithm was developed using an iterative approach to identify PWE with a critical lower bound of 0.91 for negative predictive value.Manual internal validation was performed.Outpatient visit data were extracted and modeled as a time series using the autoregressive integrated moving average model.All statistical analyses were performed using STATA version 14.2(Statacorp,USA).Results Four iterations resulted in an algorithm,with a negative predictive value 0.98(95%CI:0.95–0.99),positive predictive value of 0.98(95%CI:0.85–0.99),and an F-score accuracy of 0.96,which identified 4474 PWE.The outpatient service utilization was abruptly reduced by the pandemic,with a change of-902.1(95%CI:-936.55 to-867.70),and the recovery has also been slow,with a decrease of-5.51(95%CI:-7.00 to-4.02).Model predictions aligned closely with actual visits with median error of-3.5%.Conclusions We developed an algorithm for identifying people with epilepsy with good accuracy.Similar methods can be adapted for use in other resource-limited settings and for other diseases.The COVID pandemic appears to have caused a lasting reduction of service utilization among PWE.展开更多
The China Conference on Knowledge Graph and Semantic Computing(CCKS)2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records.Two annotated data...The China Conference on Knowledge Graph and Semantic Computing(CCKS)2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records.Two annotated data sets and some other additional resources for these two subtasks were provided for participators.This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results.The pre-trained language models are widely applied in this evaluation task.Data argumentation and external resources are also helpful.展开更多
Background:This study addresses the challenge of enhancing Retrieval Augmented Generation(RAG)search engines for electronic medical records(EMR)by learning users’distinct search semantics.The specific aim is to devel...Background:This study addresses the challenge of enhancing Retrieval Augmented Generation(RAG)search engines for electronic medical records(EMR)by learning users’distinct search semantics.The specific aim is to develop a learning-to-rank system that improves the accuracy and relevance of search results to support RAG-based search engines.Methods:Given a prompt or search query,the system first asks the user to label a few randomly selected doc-uments,which contain some keywords,as relevant to the prompt or not.The system then identifies relevant sentences and adjusts word similarities by updating a medical semantic embedding.New documents are ranked by the number of relevant sentences identified by the weighted embedding.Only the top-ranked documents and sentences are provided to a Large-Language-Model(LLM)to generate answers for further review.Findings:To evaluate our approach,four medical researchers labeled documents based on their relevance to specific diseases.We measured the information retrieval performance of our approach and two baseline methods.Results show that our approach achieved at least a 0.60 Precision-at-10(P@10)score with only ten positive labels,outperforming the baseline methods.In our pilot study,we demonstrate that the learned semantic preference can transfer to the analysis of unseen datasets,boosting the accuracy of an RAG model in extracting and explaining cancer progression diagnoses from 0.14 to 0.50.Interpretation:This study demonstrates that a customized learning-to-rank method can enhance state-of-the-art natural language models,such as LLMs,by quickly adapting to users’semantics.This approach supports EMR document retrieval and helps RAG models generate clinically meaningful answers to specific questions,under-scoring the potential of user-tailored learning-to-rank methods in clinical practice.展开更多
Objective： To obtain fundamental information for the standardization of herbal medicine in Korea. Methods： We analyzed the herbal medicine prescription data of patients at the Pusan National University Korean Medici...Objective： To obtain fundamental information for the standardization of herbal medicine in Korea. Methods： We analyzed the herbal medicine prescription data of patients at the Pusan National University Korean Medicine Hospital from March 2010 to February 2013. We used the Dongui-Bogam （Dong Yi Bao Jian） to classify prescribed herbal medicines. Results： The study revealed that the most frequently prescribed herbal medicine was ‘Liuwei Dihuang Pill （LWDHP, 六味地黄丸）＇ which was used for invigorating ‘Shen （Kidndy）-yin＇. ‘LWDHP＇ was most frequently prescribed to male patients aged 50-59, 60-69, 70-79 and 80-89 years, and ‘Xionggui Tiaoxue Decoction （XGTXD, 芎归调血饮）＇ was most frequently prescribed to female patients aged 30-39 and 40-49 years. According to the International Classification of Diseases （ICD） codes,‘Diseases of the musculoskeletal system and connective tissue＇ showed the highest prevalence. ‘LWDHP＇ and ＇XGTXD＇ was the most frequently prescribed in categories 5 and 3, respectively. Based on the percentage of prescriptions for each sex, ‘Ziyin Jianghuo Decoction （滋阴降火汤）＇ was prescribed to mainly male patients, and ‘XGTXD＇ with ‘Guima Geban Decoction （桂麻各半汤）＇ were prescribed to mainly female patients. Conclusion： This study analysis successfully determined the frequency of a variety of herbal medicines, and many restorative herbal medicines were identified and frequently administered.展开更多
The development of hospital information has been carried out for nearly 50 years, and originally started Le hospital information system （HIS）1 So far HIS isas the hospital information system （HIS）J So far HIS is t...The development of hospital information has been carried out for nearly 50 years, and originally started Le hospital information system （HIS）1 So far HIS isas the hospital information system （HIS）J So far HIS is the most widely and deeply used management system for hospitals in China.2 ＂General function standard for hospital information system＂ issued by China＇s Ministry of Health in 2002 defined that ＂The hospital information system refers to using of computer hardware and software technology, network communications technology, and other modem technology to comprehensively manage personnel, logistics, and finance in various departments in hospital. Gather, store, treat, extract, transport, aggregate,and process data in various stages of the medical activities, so that provide comprehensive and automatic information management and service to the hospital.＂展开更多
Recently,numerous estimation issues have been solved due to the developments in data-driven artificial neural networks(ANN)and graph neural networks(GNN).The primary limitation of previous methodologies has been the d...Recently,numerous estimation issues have been solved due to the developments in data-driven artificial neural networks(ANN)and graph neural networks(GNN).The primary limitation of previous methodologies has been the dependence on data that can be structured in a grid format.However,physiological recordings often exhibit irregular and unordered patterns,posing a significant challenge in conceptualising them as matrices.As a result,GNNs which comprise interactive nodes connected by edges whose weights are defined by anatomical junctions or temporal relationships have received a lot of consideration by leveraging implicit data that exists in a biological system.Additionally,our study incorporates a structural GNN to effectively differentiate between different degrees of infection in both the left and right hemispheres of the brain.Subsequently,demographic data are included,and a multi-task learning architecture is devised,integrating classification and regression tasks.The trials used an authentic dataset,including 800 brain x-ray pictures,consisting of 560 instances classified as moderate cases and 240 instances classified as severe cases.Based on empirical evidence,our methodology demonstrates superior performance in classification,surpassing other comparison methods with a notable achievement of 92.27%in terms of area under the curve as well as a correlation coefficient of 0.62.展开更多
Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, whic...Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.展开更多
Background： The usage of modem technology in healthcare record system is now a must throughout the world. However, many doctors and nurses has been reporting facing numerous challenges and obstacles in the implementa...Background： The usage of modem technology in healthcare record system is now a must throughout the world. However, many doctors and nurses has been reporting facing numerous challenges and obstacles in the implementation. The aim of the present study is to determine the prevalence of depression, anxiety and stress among doctors and nurses who utilize EMR （electronic medical record） and its associated factor. Methods： A comparative cross-sectional study was conducted ~om January till April 2012 among doctors and nurses in two public tertiary hospitals in Johor in which one of them uses EMR and the other one still using the MMR （manual medical record） system. Data was collected using self-administered validated Malay version of DASS-21 （Depression, Anxiety, and Stress Scales-21） items questionnaire. It comprises of socio-demographic and occupational characteristics. Findings： There were 130 respondents with a response rate of 91% for EMR and 123 respondents with a response rate of 86% for MMR. The mean （SD） age of respondents in EMR and MMR groups were 34.7 （9.42） and 29.7 （6.15） respectively. The mean （SD） duration of respondents using EMR was 46.1 （35.83） months. The prevalence of depression, anxiety and stress among respondents using EMR were 6.9%, 25.4% and 12.3%. There were no significant difference between the study groups related to the depression, anxiety and stress scores. In multivariable analysis, the significant factors associated with depression among respondents using EMR was age （OR 1.10, 95% CI 1.02, 1.19）. The significant factors associated with stress among respondents using EMR was marital status （OR 3.33, 95% CI 1.10, 10.09） and borderline significant was computer skill course （OR 2.94, 95% CI 0.98, 8.78）. Conclusion： The prevalence of depression, anxiety and stress of those who uses EMR were within acceptable range. Age, marital status and computer skill are the identified factor associated with the depression and stress level which need to be considered in its implementation.展开更多
Introduction: Today, information technology is considered as an important national development principle in each country which is applied in different fields. Health care as a whole and the hospitals could be regarded...Introduction: Today, information technology is considered as an important national development principle in each country which is applied in different fields. Health care as a whole and the hospitals could be regarded as a field and organizations with most remarkable IT applications respectively. Although different benchmarks and frameworks have been developed to assess different aspects of Hospital Information Systems (HISs) by various researchers, there is not any suitable reference model yet to benchmark HIS in the world. Electronic Medical Record Adoption Model (EMRAM) has been currently presented and is globally well-known to benchmark the rate of HIS utilization in the hospitals. Notwithstanding, this model has not been introduced in Iran so far. Methods: This research was carried out based on an applied descriptive method in three private hospitals of Isfahan—one of the most important provinces of Iran—in the year 2015. The purpose of this study was to investigate IT utilization stage in three selected private hospitals. Conclusion: The findings revealed that HIS is not at the center of concern in studied hospitals and is in the first maturity stage in accordance with EMRAM. However, hospital managers are enforced and under the pressure of different beneficiaries including insurance companies to improve their HIS. Therefore, it could be concluded that these types of hospitals are still far away from desirable conditions and need to enhance their IT utilization stage significantly.展开更多
1|DEVELOPMENT AND ADOPTION OF EHR IN THE UNITED STATES At present,health-care systems in the United States face enormous challenges in providing quality care,characterized by safe,effective,efficient,patientcentered,t...1|DEVELOPMENT AND ADOPTION OF EHR IN THE UNITED STATES At present,health-care systems in the United States face enormous challenges in providing quality care,characterized by safe,effective,efficient,patientcentered,timely,and equitable care while containing health-care costs[1,2].To understand and address patients'increasingly complicated health-care needs,we need safe access to quality information that is characterized by integrity,reliability,and accuracy[3],and establish mutually beneficial relationships among a multidisciplinary team of professionals[4].Traditional paper-based clinical workflow produces many issues such as illegible handwriting,inconvenient access,the possibility of computational prescribing errors,inadequate patient hand-offs,and drug administration errors.These problems can lead to medical errors,omissions,and duplications and,ultimately,poor patient outcomes and compromised quality of care[2].展开更多
Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laborat...Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records.展开更多
Rationale: Medical treatment on short-term primary care medical service trips (MSTs) is generally symptom-based and supplemented by point-of-care testing. This pilot study contributes to the effective planning for suc...Rationale: Medical treatment on short-term primary care medical service trips (MSTs) is generally symptom-based and supplemented by point-of-care testing. This pilot study contributes to the effective planning for such austere settings based on predicted symptomology. Objective: We aimed to prospectively document the epidemiology of patients seen during two low-resource clinics on a MST in Honduras and apply predefined case definitions adapted from guidelines used by international healthcare organizations (e.g. World Health Organization). Methods: An observational design was used to track the epidemiology during two clinics on an MST in Limon, Honduras in March 2015. The QuickChart mobile electronic medical record (EMR) application was piloted to document diagnoses according to predefined case definitions. Results: The most commonly diagnosed syndromes were upper respiratory complaints (20.19%), nonspecific abdominal complaints (20.19%), general pain (15.38%), hypertension (9.62%), pruritus (6.73%), and asthma/ COPD (4.81%). The case definitions accounted for 94% of all complaints and diagnoses on the brigade. Discussion: The distribution of common patient diagnoses on this MST was similar to that which had been reported elsewhere. The use of broader symptom-based case definitions for epidemiologic surveillance could also facilitate the syndromic management of patients seen on MSTs, and improve the consistency of treatment offered. Conclusion: Case definitions for common syndromes on primary care MSTs may be a feasible method of standardizing patient management. Preliminary use of the QuickChart EMR was acceptable for documentation of epidemiology in the field. Further study is necessary to investigate the reliability of syndromic diagnostic criteria between different clinicians and in a variety of MST settings.展开更多
The purpose of this paper is to discuss the development of medical informatization in the era of big data.Through literature review and theoretical analysis,the development of medical informatization in the era of big...The purpose of this paper is to discuss the development of medical informatization in the era of big data.Through literature review and theoretical analysis,the development of medical informatization in the era of big data is deeply discussed.The results show that medical informatization has developed rapidly in the era of big data,and its role in clinical decision-making,scientific research,teaching,and management has become increasingly prominent.The development of medical informatization in the era of big data has important purposes and methods,which can produce important results and conclusions and provide strong support for the development of the medical field.展开更多
Celiac disease(CD)is a common autoimmune disorder where gluten ingestion triggers an immune response,damaging the small intestine in genetically pre-disposed individuals.Affecting around 1%of the global population,CD ...Celiac disease(CD)is a common autoimmune disorder where gluten ingestion triggers an immune response,damaging the small intestine in genetically pre-disposed individuals.Affecting around 1%of the global population,CD presents with diverse symptoms,including gastrointestinal issues like diarrhea and extraintestinal conditions such as anemia and osteoporosis,often complicating diagnosis.Advances in serology,histology,and genetic testing,such as HLA-DQ2/DQ8 analysis,have improved diagnostic accuracy.Precision medicine is transforming CD management by integrating genetic,clinical,and lifestyle data to enable risk prediction,personalized therapies,and improved outcomes.Tools like machine learning enhance early diagnosis,dietary management,and drug discovery,while electronic medical records support comprehensive patient pro-filing and disease monitoring.These technologies facilitate personalized health-care delivery tailored to individual patient profiles.展开更多
Background:Stroke is the second leading cause of death and third leading cause of disability worldwide and is the leading cause of death and disability among adults in China,with its incidence rate continuing to rise....Background:Stroke is the second leading cause of death and third leading cause of disability worldwide and is the leading cause of death and disability among adults in China,with its incidence rate continuing to rise.In China,the average age of firsttime stroke patients is 66.4 years,and the intravenous thrombolysis rate using recombinant tissue plasminogen activator within 3 h of onset is only 16%.Given this fact,there is a pressing need for real‐time predictive tools,particularly for elderly individuals at home,that can provide early warnings for potential strokes.Methods:We collected continuous monitoring data from nonintrusive smart beds and multimodal temporal data from electronic medical records at the National Center for Neurological Disorders.The data included smart bed monitoring indicators,laboratory tests,nurse observations,and static data as potential predictors,with stroke as the outcome.We applied feature representation and feature selection techniques and then input the predictors into machine learning models.Additionally,deep learning models were used after preprocessing the irregular temporal data.Finally,we evaluated the performance of the stroke prediction models and assessed the importance of the features.We used continuously updated vital signs and clinical data during hospitalization to generate timely stroke risk alerts during the same period of admission.Results:A total of 37,041 samples were analyzed,of which 7020 patients were diagnosed with stroke.When only the smart bed features were used for prediction,the model achieved an area under the receiver operating characteristic curve(AUROC)of 0.59−0.63,with an accuracy ranging from 60%−65%.Among the four artificial intelligence algorithms,the random forest model demonstrated the best performance.After all the available features were incorporated,the AUROC increased to 0.94,and the accuracy improved to 92%.Conclusions:In this study,the occurrence of stroke was successfully identified by integrating multimodal temporal data from electronic medical records.Noncontact monitoring of respiration and heart rate offers a promising approach for daily stroke surveillance in home‐based populations,particularly for elderly individuals living alone.展开更多
Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from l...Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.展开更多
基金This research was supported by the National Natural Science Foundation of China under Grant(No.42050102)the Postgraduate Education Reform Project of Jiangsu Province under Grant(No.SJCX22_0343)Also,this research was supported by Dou Wanchun Expert Workstation of Yunnan Province(No.202205AF150013).
文摘With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.
文摘Background Well child visits(WCV)are fundamental to preventive primary care.We examined trends in WCV attendance during the COVID-19 pandemic and characterised variation by patient and provider characteristics.Methods Deidentified electronic medical records from two academic practice-based research networks in Ontario were used to create age-specific cohorts of children under age six attending WCVs from 2015 to 2022.Patients’residential postal codes were linked to neighbourhood-level measures to estimate socioeconomic status.Monthly visit rates were modelled using segmented linear regression with autoregressive residuals.Changes associated with COVID-19 were assessed using level change and trend change of monthly visit rates.Findings For the 53256 included children,WCV attendance increased from 2015 to 2020 for cohorts aged 15 months and younger and was stable for 18-month,2–3-year and 4–6-year visits.The COVID-19 pandemic was associated with decreased WCV attendance in all ages except ages 1–2 weeks,1 month,12 months,15 months and 18 months,in whom attendance was unchanged.The rate of change in WCV attendance rates pre-COVID-19 compared with post-COVID-19 was unchanged,with the exception of increased rate of change for the 1–2 weeks and 2–3 years old cohorts.Lower attendance rates were observed in children residing in neighbourhoods with the highest material deprivation,rural regions and those whose family physicians were men or older than 65 years.Interpretation Prepandemic gains in WCV attendance were stable or improved after the initial reductions observed at the pandemic onset,suggesting that WCVs were prioritised by family physicians and families.Targeted strategies are needed to improve WCV attendance for vulnerable groups.
基金supported by the National Natural Science Foundation of China(62402017 and 82470774)Xuzhou Scientific Technological Projects(KC23143)+2 种基金the Clinical Cohort Construction Program of Peking University Third Hospital(No.BYSYDL2023004)Peking University Medicine plus X Pilot Program-Key Technologies R&D Project(2024YXXLHGG007)the receipt of studentship awards from the Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme in Health Data Science(Grant Ref:218529/Z/19/Z).
文摘End-stage renal disease(ESRD)significantly impacts patients’quality of life and poses substantial socioeconomic burdens.Dietary interventions are crucial for managing ESRD,yet high-quality evidence and analysis specifically linking diet to mortality outcomes is scarce.Methods:We conducted a comprehensive study involving 656 peritoneal dialysis(PD)patients over 12 years,with an average follow-up every 3 months.Dietary intake was meticulously recorded using a 3-day dietary record method,integrated with detailed health records and outcomes.We employed a 2-stage model to evaluate nonlinear relationships between dietary nutrients and mortality risk,accounting for various confounding factors.Findings:Our analysis revealed that 14 out of 26 nutritional elements lack guidelines for ESRD and PD patients,with 13 showing significant associations with mortality.For example,while guidelines suggest a dietary protein intake of 1.0 to 1.2 g/kg/d,our findings indicate an optimal range of 0.88 to 1.13 g/kg/d.Similarly,the recommended dietary energy intake of 25 to 35 kcal/kg/d was refined to 26 to 42 kcal/kg/d.We identified that 69% of dietary intake-outcome relationships are nonlinear,especially in patients with poor health status.Interpretations:Our study provides detailed dietary intake thresholds that correlate with improved prognosis in ESRD patients,enhancing current guidelines.The findings highlight the importance of personalized nutritional management and underscore the nonlinear nature of nutrient–disease relationships,particularly in severely ill patients.This approach can refine dietary recommendations and improve patient care in ESRD.
基金supported by the National Natural Science Foundation of China(No.U24A20256)and the Science and Technology Major Project of Changsha(No.kh2402004).
文摘Predicting mortality risk in the Intensive Care Unit(ICU)using Electronic Medical Records(EMR)is crucial for identifying patients in need of immediate attention.However,the incompleteness and the variability of EMR features for each patient make mortality prediction challenging.This study proposes a multimodal representation learning framework based on a novel personalized graph-based fusion approach to address these challenges.The proposed approach involves constructing patient-specific modality aggregation graphs to provide information about the features associated with each patient from incomplete multimodal data,enabling the effective and explainable fusion of the incomplete features.Modality-specific encoders are employed to encode each modality feature separately.To tackle the variability and incompleteness of input features among patients,a novel personalized graph-based fusion method is proposed to fuse patient-specific multimodal feature representations based on the constructed modality aggregation graphs.Furthermore,a MultiModal Gated Contrastive Representation Learning(MMGCRL)method is proposed to facilitate capturing adequate complementary information from multimodal representations and improve model performance.We evaluate the proposed framework using the large-scale ICU dataset,MIMIC-III.Experimental results demonstrate its effectiveness in mortality prediction,outperforming several state-of-the-art methods.
基金supported by the Indian Council of Medical Research,New Delhi,India.(RFC No.ECD/Adhoc/87/2021–2022,2022).
文摘Background Electronic medical records(EMR)can be utilized to understand the impact of the disruption in care provision caused by the pandemic.We aimed to develop and validate an algorithm to identify persons with epilepsy(PWE)from our EMR and to use it to explore the effect of the pandemic on outpatient service utilization.Methods EMRs from the neurology specialty,covering the period from January 2018 to December 2023,were used.An algorithm was developed using an iterative approach to identify PWE with a critical lower bound of 0.91 for negative predictive value.Manual internal validation was performed.Outpatient visit data were extracted and modeled as a time series using the autoregressive integrated moving average model.All statistical analyses were performed using STATA version 14.2(Statacorp,USA).Results Four iterations resulted in an algorithm,with a negative predictive value 0.98(95%CI:0.95–0.99),positive predictive value of 0.98(95%CI:0.85–0.99),and an F-score accuracy of 0.96,which identified 4474 PWE.The outpatient service utilization was abruptly reduced by the pandemic,with a change of-902.1(95%CI:-936.55 to-867.70),and the recovery has also been slow,with a decrease of-5.51(95%CI:-7.00 to-4.02).Model predictions aligned closely with actual visits with median error of-3.5%.Conclusions We developed an algorithm for identifying people with epilepsy with good accuracy.Similar methods can be adapted for use in other resource-limited settings and for other diseases.The COVID pandemic appears to have caused a lasting reduction of service utilization among PWE.
文摘The China Conference on Knowledge Graph and Semantic Computing(CCKS)2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records.Two annotated data sets and some other additional resources for these two subtasks were provided for participators.This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results.The pre-trained language models are widely applied in this evaluation task.Data argumentation and external resources are also helpful.
基金Crowd Sourcing Labels from Electronic Medical Records to Enable Biomedical Research Award Number:1 UH2 CA203708-01.
文摘Background:This study addresses the challenge of enhancing Retrieval Augmented Generation(RAG)search engines for electronic medical records(EMR)by learning users’distinct search semantics.The specific aim is to develop a learning-to-rank system that improves the accuracy and relevance of search results to support RAG-based search engines.Methods:Given a prompt or search query,the system first asks the user to label a few randomly selected doc-uments,which contain some keywords,as relevant to the prompt or not.The system then identifies relevant sentences and adjusts word similarities by updating a medical semantic embedding.New documents are ranked by the number of relevant sentences identified by the weighted embedding.Only the top-ranked documents and sentences are provided to a Large-Language-Model(LLM)to generate answers for further review.Findings:To evaluate our approach,four medical researchers labeled documents based on their relevance to specific diseases.We measured the information retrieval performance of our approach and two baseline methods.Results show that our approach achieved at least a 0.60 Precision-at-10(P@10)score with only ten positive labels,outperforming the baseline methods.In our pilot study,we demonstrate that the learned semantic preference can transfer to the analysis of unseen datasets,boosting the accuracy of an RAG model in extracting and explaining cancer progression diagnoses from 0.14 to 0.50.Interpretation:This study demonstrates that a customized learning-to-rank method can enhance state-of-the-art natural language models,such as LLMs,by quickly adapting to users’semantics.This approach supports EMR document retrieval and helps RAG models generate clinically meaningful answers to specific questions,under-scoring the potential of user-tailored learning-to-rank methods in clinical practice.
基金Supported by a grant to Korean Medical Science Research Center for Healthy Aging from the National Research Foundation of Korean government(No.2014R1A5A2009936)
文摘Objective： To obtain fundamental information for the standardization of herbal medicine in Korea. Methods： We analyzed the herbal medicine prescription data of patients at the Pusan National University Korean Medicine Hospital from March 2010 to February 2013. We used the Dongui-Bogam （Dong Yi Bao Jian） to classify prescribed herbal medicines. Results： The study revealed that the most frequently prescribed herbal medicine was ‘Liuwei Dihuang Pill （LWDHP, 六味地黄丸）＇ which was used for invigorating ‘Shen （Kidndy）-yin＇. ‘LWDHP＇ was most frequently prescribed to male patients aged 50-59, 60-69, 70-79 and 80-89 years, and ‘Xionggui Tiaoxue Decoction （XGTXD, 芎归调血饮）＇ was most frequently prescribed to female patients aged 30-39 and 40-49 years. According to the International Classification of Diseases （ICD） codes,‘Diseases of the musculoskeletal system and connective tissue＇ showed the highest prevalence. ‘LWDHP＇ and ＇XGTXD＇ was the most frequently prescribed in categories 5 and 3, respectively. Based on the percentage of prescriptions for each sex, ‘Ziyin Jianghuo Decoction （滋阴降火汤）＇ was prescribed to mainly male patients, and ‘XGTXD＇ with ‘Guima Geban Decoction （桂麻各半汤）＇ were prescribed to mainly female patients. Conclusion： This study analysis successfully determined the frequency of a variety of herbal medicines, and many restorative herbal medicines were identified and frequently administered.
文摘The development of hospital information has been carried out for nearly 50 years, and originally started Le hospital information system （HIS）1 So far HIS isas the hospital information system （HIS）J So far HIS is the most widely and deeply used management system for hospitals in China.2 ＂General function standard for hospital information system＂ issued by China＇s Ministry of Health in 2002 defined that ＂The hospital information system refers to using of computer hardware and software technology, network communications technology, and other modem technology to comprehensively manage personnel, logistics, and finance in various departments in hospital. Gather, store, treat, extract, transport, aggregate,and process data in various stages of the medical activities, so that provide comprehensive and automatic information management and service to the hospital.＂
文摘Recently,numerous estimation issues have been solved due to the developments in data-driven artificial neural networks(ANN)and graph neural networks(GNN).The primary limitation of previous methodologies has been the dependence on data that can be structured in a grid format.However,physiological recordings often exhibit irregular and unordered patterns,posing a significant challenge in conceptualising them as matrices.As a result,GNNs which comprise interactive nodes connected by edges whose weights are defined by anatomical junctions or temporal relationships have received a lot of consideration by leveraging implicit data that exists in a biological system.Additionally,our study incorporates a structural GNN to effectively differentiate between different degrees of infection in both the left and right hemispheres of the brain.Subsequently,demographic data are included,and a multi-task learning architecture is devised,integrating classification and regression tasks.The trials used an authentic dataset,including 800 brain x-ray pictures,consisting of 560 instances classified as moderate cases and 240 instances classified as severe cases.Based on empirical evidence,our methodology demonstrates superior performance in classification,surpassing other comparison methods with a notable achievement of 92.27%in terms of area under the curve as well as a correlation coefficient of 0.62.
基金the Artificial Intelligence Innovation and Development Project of Shanghai Municipal Commission of Economy and Information (No. 2019-RGZN-01081)。
文摘Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.
文摘Background： The usage of modem technology in healthcare record system is now a must throughout the world. However, many doctors and nurses has been reporting facing numerous challenges and obstacles in the implementation. The aim of the present study is to determine the prevalence of depression, anxiety and stress among doctors and nurses who utilize EMR （electronic medical record） and its associated factor. Methods： A comparative cross-sectional study was conducted ~om January till April 2012 among doctors and nurses in two public tertiary hospitals in Johor in which one of them uses EMR and the other one still using the MMR （manual medical record） system. Data was collected using self-administered validated Malay version of DASS-21 （Depression, Anxiety, and Stress Scales-21） items questionnaire. It comprises of socio-demographic and occupational characteristics. Findings： There were 130 respondents with a response rate of 91% for EMR and 123 respondents with a response rate of 86% for MMR. The mean （SD） age of respondents in EMR and MMR groups were 34.7 （9.42） and 29.7 （6.15） respectively. The mean （SD） duration of respondents using EMR was 46.1 （35.83） months. The prevalence of depression, anxiety and stress among respondents using EMR were 6.9%, 25.4% and 12.3%. There were no significant difference between the study groups related to the depression, anxiety and stress scores. In multivariable analysis, the significant factors associated with depression among respondents using EMR was age （OR 1.10, 95% CI 1.02, 1.19）. The significant factors associated with stress among respondents using EMR was marital status （OR 3.33, 95% CI 1.10, 10.09） and borderline significant was computer skill course （OR 2.94, 95% CI 0.98, 8.78）. Conclusion： The prevalence of depression, anxiety and stress of those who uses EMR were within acceptable range. Age, marital status and computer skill are the identified factor associated with the depression and stress level which need to be considered in its implementation.
文摘Introduction: Today, information technology is considered as an important national development principle in each country which is applied in different fields. Health care as a whole and the hospitals could be regarded as a field and organizations with most remarkable IT applications respectively. Although different benchmarks and frameworks have been developed to assess different aspects of Hospital Information Systems (HISs) by various researchers, there is not any suitable reference model yet to benchmark HIS in the world. Electronic Medical Record Adoption Model (EMRAM) has been currently presented and is globally well-known to benchmark the rate of HIS utilization in the hospitals. Notwithstanding, this model has not been introduced in Iran so far. Methods: This research was carried out based on an applied descriptive method in three private hospitals of Isfahan—one of the most important provinces of Iran—in the year 2015. The purpose of this study was to investigate IT utilization stage in three selected private hospitals. Conclusion: The findings revealed that HIS is not at the center of concern in studied hospitals and is in the first maturity stage in accordance with EMRAM. However, hospital managers are enforced and under the pressure of different beneficiaries including insurance companies to improve their HIS. Therefore, it could be concluded that these types of hospitals are still far away from desirable conditions and need to enhance their IT utilization stage significantly.
基金funded by the Organized Research and Creative Activities(ORCA)Program at the University of Houston-Downtown(PI:Song Ge)。
文摘1|DEVELOPMENT AND ADOPTION OF EHR IN THE UNITED STATES At present,health-care systems in the United States face enormous challenges in providing quality care,characterized by safe,effective,efficient,patientcentered,timely,and equitable care while containing health-care costs[1,2].To understand and address patients'increasingly complicated health-care needs,we need safe access to quality information that is characterized by integrity,reliability,and accuracy[3],and establish mutually beneficial relationships among a multidisciplinary team of professionals[4].Traditional paper-based clinical workflow produces many issues such as illegible handwriting,inconvenient access,the possibility of computational prescribing errors,inadequate patient hand-offs,and drug administration errors.These problems can lead to medical errors,omissions,and duplications and,ultimately,poor patient outcomes and compromised quality of care[2].
文摘Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records.
文摘Rationale: Medical treatment on short-term primary care medical service trips (MSTs) is generally symptom-based and supplemented by point-of-care testing. This pilot study contributes to the effective planning for such austere settings based on predicted symptomology. Objective: We aimed to prospectively document the epidemiology of patients seen during two low-resource clinics on a MST in Honduras and apply predefined case definitions adapted from guidelines used by international healthcare organizations (e.g. World Health Organization). Methods: An observational design was used to track the epidemiology during two clinics on an MST in Limon, Honduras in March 2015. The QuickChart mobile electronic medical record (EMR) application was piloted to document diagnoses according to predefined case definitions. Results: The most commonly diagnosed syndromes were upper respiratory complaints (20.19%), nonspecific abdominal complaints (20.19%), general pain (15.38%), hypertension (9.62%), pruritus (6.73%), and asthma/ COPD (4.81%). The case definitions accounted for 94% of all complaints and diagnoses on the brigade. Discussion: The distribution of common patient diagnoses on this MST was similar to that which had been reported elsewhere. The use of broader symptom-based case definitions for epidemiologic surveillance could also facilitate the syndromic management of patients seen on MSTs, and improve the consistency of treatment offered. Conclusion: Case definitions for common syndromes on primary care MSTs may be a feasible method of standardizing patient management. Preliminary use of the QuickChart EMR was acceptable for documentation of epidemiology in the field. Further study is necessary to investigate the reliability of syndromic diagnostic criteria between different clinicians and in a variety of MST settings.
文摘The purpose of this paper is to discuss the development of medical informatization in the era of big data.Through literature review and theoretical analysis,the development of medical informatization in the era of big data is deeply discussed.The results show that medical informatization has developed rapidly in the era of big data,and its role in clinical decision-making,scientific research,teaching,and management has become increasingly prominent.The development of medical informatization in the era of big data has important purposes and methods,which can produce important results and conclusions and provide strong support for the development of the medical field.
文摘Celiac disease(CD)is a common autoimmune disorder where gluten ingestion triggers an immune response,damaging the small intestine in genetically pre-disposed individuals.Affecting around 1%of the global population,CD presents with diverse symptoms,including gastrointestinal issues like diarrhea and extraintestinal conditions such as anemia and osteoporosis,often complicating diagnosis.Advances in serology,histology,and genetic testing,such as HLA-DQ2/DQ8 analysis,have improved diagnostic accuracy.Precision medicine is transforming CD management by integrating genetic,clinical,and lifestyle data to enable risk prediction,personalized therapies,and improved outcomes.Tools like machine learning enhance early diagnosis,dietary management,and drug discovery,while electronic medical records support comprehensive patient pro-filing and disease monitoring.These technologies facilitate personalized health-care delivery tailored to individual patient profiles.
基金supported by the National Natural Science Foundation of China(72204169,82425101,82271516,81801187)Noncommunicable Chronic Diseases‐National Science and Technology Major Project(2023ZD0504800,2023ZD0504801,2023ZD0504802,2023ZD0504803,2023ZD0504804)+2 种基金Beijing Municipal Science&Technology Commission(Z231100004823036)Capital's Funds for Health Improvement and Research(2022‐2‐2045)National Key R&D Program of China(2024YFC3044800,2022YFF1501500,2022YFF1501501,2022YFF1501502,2022YFF1501503,2022YFF1501504,2022YFF1501505).
文摘Background:Stroke is the second leading cause of death and third leading cause of disability worldwide and is the leading cause of death and disability among adults in China,with its incidence rate continuing to rise.In China,the average age of firsttime stroke patients is 66.4 years,and the intravenous thrombolysis rate using recombinant tissue plasminogen activator within 3 h of onset is only 16%.Given this fact,there is a pressing need for real‐time predictive tools,particularly for elderly individuals at home,that can provide early warnings for potential strokes.Methods:We collected continuous monitoring data from nonintrusive smart beds and multimodal temporal data from electronic medical records at the National Center for Neurological Disorders.The data included smart bed monitoring indicators,laboratory tests,nurse observations,and static data as potential predictors,with stroke as the outcome.We applied feature representation and feature selection techniques and then input the predictors into machine learning models.Additionally,deep learning models were used after preprocessing the irregular temporal data.Finally,we evaluated the performance of the stroke prediction models and assessed the importance of the features.We used continuously updated vital signs and clinical data during hospitalization to generate timely stroke risk alerts during the same period of admission.Results:A total of 37,041 samples were analyzed,of which 7020 patients were diagnosed with stroke.When only the smart bed features were used for prediction,the model achieved an area under the receiver operating characteristic curve(AUROC)of 0.59−0.63,with an accuracy ranging from 60%−65%.Among the four artificial intelligence algorithms,the random forest model demonstrated the best performance.After all the available features were incorporated,the AUROC increased to 0.94,and the accuracy improved to 92%.Conclusions:In this study,the occurrence of stroke was successfully identified by integrating multimodal temporal data from electronic medical records.Noncontact monitoring of respiration and heart rate offers a promising approach for daily stroke surveillance in home‐based populations,particularly for elderly individuals living alone.
基金the Special Project of the Shanghai Municipal Commission of Economy and Information Technology for Promoting High-Quality Industrial Development(No.2024-GZL-RGZN-02011)the Shanghai City Digital Transformation Project(No.202301002)the Project of Shanghai Shenkang Hospital Development Center(No.SHDC22023214)。
文摘Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.
