The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can si...The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can simultaneously process multi-modality data such as medical images and medical reports.These models can not only recognize images,but also understand the semantic relationship between images and texts,effectively realize the integration of medical information,and provide strong support for clinical decision-making and disease diagnosis.The visual-language large model has good performance for specific medical tasks,and also shows strong potential and high intelligence in the general task models.This paper provides a comprehensive review of the visual-language large model in the field of medical health.Specifically,this paper first introduces the basic theoretical basis and technical principles.Then,this paper introduces the specific application scenarios in the field of medical health,including modality fusion,semi-supervised learning,weakly supervised learning,unsupervised learning,cross-domain model and general models.Finally,the challenges including insufficient data,interpretability,and practical deployment are discussed.According to the existing challenges,four potential future development directions are given.展开更多
Objective:To explore the application effect of Case-based Learning(CBL)combined with Problem-Based Learning(PBL)in the teaching of medical imaging to undergraduate students majoring in clinical medicine.Methods:Underg...Objective:To explore the application effect of Case-based Learning(CBL)combined with Problem-Based Learning(PBL)in the teaching of medical imaging to undergraduate students majoring in clinical medicine.Methods:Undergraduates of clinical medicine majoring in the School of Clinical Medicine of Hebei University were selected as the research subjects and divided into the experimental group(CBL combined with PBL teaching mode)and the control group(traditional teaching mode),and the teaching effect was evaluated by the examination results and questionnaires.Results:The test scores of the experimental group were significantly better than those of the control group(P<0.05),and the satisfaction of the students in the experimental group reached more than 90%.Conclusion:CBL combined with PBL teaching mode can effectively improve the teaching quality of medical imaging in clinical medicine specialty.展开更多
Objective:To analyze the effectiveness of the matrix medical administration model in enhancing medical safety management.Method:A total of 39 medical incidents in the hospital from September 2020 to September 2022 wer...Objective:To analyze the effectiveness of the matrix medical administration model in enhancing medical safety management.Method:A total of 39 medical incidents in the hospital from September 2020 to September 2022 were selected as the reference group,implementing conventional medical administration.Another 39 medical incidents from October 2022 to October 2024 were chosen as the experimental group,adopting the matrix medical administration model.The practical indicators such as causes of medical disputes,dispute compensation,medical injury appraisal results,and diagnosis and treatment quality indicators were compared between the two groups.Results:In the experimental group,the primary reasons for medical disputes were patient-related,and most disputes resulted in no compensation.After medical injury appraisal,most cases were not considered medical injuries.The comparison between the two groups was statistically significant(P<0.05).The diagnosis and treatment quality indicators of the experimental group were superior to those of the reference group(P<0.05).Conclusion:The matrix medical administration model can reduce medical disputes caused by hospital factors,decrease the proportion of compensation and the incidence of medical injuries,and improve the quality of diagnosis and treatment,demonstrating high management effectiveness.展开更多
Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and...Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.展开更多
Higher vocational medical education plays a crucial role in the cultivation of outstanding medical talents.Based on the CIPP evaluation model(Context,Input,Process,Product),this paper conducts a systematic analysis of...Higher vocational medical education plays a crucial role in the cultivation of outstanding medical talents.Based on the CIPP evaluation model(Context,Input,Process,Product),this paper conducts a systematic analysis of the development of higher vocational medical education,explores the achievements and challenges faced at each stage from multiple dimensions,and puts forward improvement suggestions.This study uses the CIPP model to carry out a systematic review of medical education programs and analyzes their application value from both theoretical and practical perspectives.展开更多
Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved inform...Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved information retrieval efficiency to a certain extent,they exhibit significant limitations in adaptability and accuracy when dealing with procurement documents characterized by diverse formats and a high degree of unstructured content.The emergence of Large Language Models(LLMs)offers new possibilities for efficient procurement information processing and extraction.Methods:This study collected procurement transaction documents from public procurement websites,and proposed a procurement Information Extraction(IE)method based on LLMs.Unlike traditional approaches,this study systematically explores the applicability of LLMs in both structured and unstructured entities in procurement documents,addressing the challenges posed by format variability and content complexity.Furthermore,an optimized prompt framework tailored for procurement document extraction tasks is developed to enhance the accuracy and robustness of IE.The aim is to process and extract key information from medical device procurement quickly and accurately,meeting stakeholders'demands for precision and timeliness in information retrieval.Results:Experimental results demonstrate that,compared to traditional methods,the proposed approach achieves an F1 Score of 0.9698,representing a 4.85%improvement over the best baseline model.Moreover,both recall and precision rates are close to 97%,significantly outperforming other models and exhibiting exceptional overall recognition capabilities.Notably,further analysis reveals that the proposed method consistently maintains high performance across both structured and unstructured entities in procurement documents while balancing recall and precision effectively,demonstrating its adaptability in handling varying document formats.The results of ablation experiments validate the effectiveness of the proposed prompting strategy.Conclusion:Additionally,this study explores the challenges and potential improvements of the proposed method in IE tasks and provides insights into its feasibility for real-world deployment and application directions,further clarifying its adaptability and value.This method not only exhibits significant advantages in medical device procurement but also holds promise for providing new approaches to information processing and decision support in various domains.展开更多
Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ...Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.展开更多
This paper describes how to use the Unified Modeling Language (UML) to modeling software processes in medical insurance MIS, and compares UML Modeling method with classic PO(Process Oriented) Modeling method. It indi...This paper describes how to use the Unified Modeling Language (UML) to modeling software processes in medical insurance MIS, and compares UML Modeling method with classic PO(Process Oriented) Modeling method. It indicates that the whole performance of application system model described by UML is much better than the one described by PO.展开更多
activities.Ex-periments on a synthetic log of the non-secondary hy-pertension MTP and empirical findings demonstrate the effectiveness of our approach.The results show that the process mining in our approach framework...activities.Ex-periments on a synthetic log of the non-secondary hy-pertension MTP and empirical findings demonstrate the effectiveness of our approach.The results show that the process mining in our approach framework can automatically generate more accurate MTP mod-els,and the subprocess models based on treatment pat-terns make the models easy to understand.展开更多
The medical herbs should be dried directly after harvesting, otherwise, insects and fungi spoilage them. Conventional drying methods such as open sun drying and conventional fuel dryers are not suitable, since they co...The medical herbs should be dried directly after harvesting, otherwise, insects and fungi spoilage them. Conventional drying methods such as open sun drying and conventional fuel dryers are not suitable, since they contaminate the herbs, decrease the drying efficiency and at the same time increase the drying cost. Therefore, the recent trend is toward the harnessing the renewable energy to dry the medical herbs. This paper presents a new controlled drying system, which uses a solar collector and an electric heater to heat the drying air, and a PV-WIND hybrid system to supply the required electric energy to the dryer electric load. This paper also presents the dynamic modeling, simulation, and control of the suggested thermal system (i.e., the solar thermal system and the electrical heater). Moreover, this paper, exhibits the different results of the thermal system, to check the effectiveness of this system that fulfills the requirements of the drying operation. The system results are found to satisfy the ultimate goals of the drying operation.展开更多
Based on the analysis of the current college Medical English teaching and the study of the blendedteaching model,the author practiced the model in the real teaching.In the process,the author verified the usefulness an...Based on the analysis of the current college Medical English teaching and the study of the blendedteaching model,the author practiced the model in the real teaching.In the process,the author verified the usefulness and effectiveness of the Blended-teaching Model,which can break the limitation of the classroom teaching.The students could contact with each other and also with the teacher conveniently whatever in-class and offclass in the learning process.展开更多
Practicing dialogue-scene simulation in medical English can integrate effectively learning resources, activate the class atmosphere, enrich contents of courses, train language skills and raise students' cooperated...Practicing dialogue-scene simulation in medical English can integrate effectively learning resources, activate the class atmosphere, enrich contents of courses, train language skills and raise students' cooperated consciousness.Furthermore,practicing dialogue-scene simulation can promote reform of teaching estimation-model, teaching quality and efficiency. In a word, practicing dialogue-scene simulation deserves further exploration and promotion.展开更多
This study explored and reviewed the logistic regression (LR) model, a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, with emphasis on m...This study explored and reviewed the logistic regression (LR) model, a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, with emphasis on medical research. Thirty seven research articles published between 2000 and 2018 which employed logistic regression as the main statistical tool as well as six text books on logistic regression were reviewed. Logistic regression concepts such as odds, odds ratio, logit transformation, logistic curve, assumption, selecting dependent and independent variables, model fitting, reporting and interpreting were presented. Upon perusing the literature, considerable deficiencies were found in both the use and reporting of LR. For many studies, the ratio of the number of outcome events to predictor variables (events per variable) was sufficiently small to call into question the accuracy of the regression model. Also, most studies did not report on validation analysis, regression diagnostics or goodness-of-fit measures;measures which authenticate the robustness of the LR model. Here, we demonstrate a good example of the application of the LR model using data obtained on a cohort of pregnant women and the factors that influence their decision to opt for caesarean delivery or vaginal birth. It is recommended that researchers should be more rigorous and pay greater attention to guidelines concerning the use and reporting of LR models.展开更多
Thermogravimetric study of medical transfusion tube （MTT） waste containing polyvinyl chloride （PVC） was carried out using the thermogravimetric analyser （TGA） with N2, at different heating rates of 5, 10, 20, 30...Thermogravimetric study of medical transfusion tube （MTT） waste containing polyvinyl chloride （PVC） was carried out using the thermogravimetric analyser （TGA） with N2, at different heating rates of 5, 10, 20, 30, 50 ℃/min. The purpose is to obtain pyrolysis characteristics and kinetic parameters of medical waste. The experimental results indicate that the pyrolysis behavior of the MTT sample is in agreement with its main ingredient of PVC, appearing two stair stepping stages. The influence of the additives in MTT on pyrolysis behavior was also revealed, which could improve MTT pyrolysis at lower temperature in the first stage, and cause obvious unsmoothness and asymmetry of the second DTG peak. Four n-order kinetic models of Coats-Redfern, Ozawa, Kissinger and Freeman-carroll were used to get the kinetic parameters. Furthermore, a novel ＂two-step four-reaction model＂ was established to simulate the whole continuous process. The different methods and the kinetic parameters thus obtained were discussed and compared with each other in literatures. The reasons of deviation among kinetic values were tried to be elucidated. The new established model could more satisfactorily describe the pyrolysis process of MTT, being more mechanistic and conveniently serving for the engineering.展开更多
With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming a...With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.展开更多
Objective: This study assesses the quality of artificial intelligence chatbots in responding to standardized obstetrics and gynecology questions. Methods: Using ChatGPT-3.5, ChatGPT-4.0, Bard, and Claude to respond to...Objective: This study assesses the quality of artificial intelligence chatbots in responding to standardized obstetrics and gynecology questions. Methods: Using ChatGPT-3.5, ChatGPT-4.0, Bard, and Claude to respond to 20 standardized multiple choice questions on October 7, 2023, responses and correctness were recorded. A logistic regression model assessed the relationship between question character count and accuracy. For each incorrect question, an independent error analysis was undertaken. Results: ChatGPT-4.0 scored a 100% across both obstetrics and gynecology questions. ChatGPT-3.5 scored a 95% overall, earning an 85.7% in obstetrics and a 100% in gynecology. Claude scored a 90% overall, earning a 100% in obstetrics and an 84.6% in gynecology. Bard scored a 77.8% overall, earning an 83.3% in obstetrics and a 75% in gynecology and would not respond to two questions. There was no statistical significance between character count and accuracy. Conclusions: ChatGPT-3.5 and ChatGPT-4.0 excelled in both obstetrics and gynecology while Claude performed well in obstetrics but possessed minor weaknesses in gynecology. Bard comparatively performed the worst and had the most limitations, leading to our support of the other artificial intelligence chatbots as preferred study tools. Our findings support the use of chatbots as a supplement, not a substitute for clinician-based learning or historically successful educational tools.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
Background:An urban medical group in Dapeng New District was established in 2017 with the objective of enhancing outcomes for common diseases and reinforcing primary care by integrating high‐level hospitals with prim...Background:An urban medical group in Dapeng New District was established in 2017 with the objective of enhancing outcomes for common diseases and reinforcing primary care by integrating high‐level hospitals with primary health services.This study aimed to evaluate the performance of the urban medical group using the triangular value chain framework.Methods:The evaluation was conducted using the Donabedian model,focusing on three key dimensions:safety and quality,accessibility,and affordability.Longitudinal data were collected from 2016 to 2022 through government annual reports,the medical insurance bureau,and hospital information systems.Preprogram and postprogram outcome measurements were compared to assess differences and trends,providing a clear picture of the program's effectiveness.Results:Accessibility improved significantly,with the number of hospital beds per 1000 residents increasing from 2.62 in 2017 to 3.76 in 2022.The availability of general practitioners(GPs)also rose markedly,from 0 per 10,000 residents in 2017 to 6.27 in 2022.Regarding safety and quality,the proportion of complex medical procedures conducted within the New District expanded substantially,from 7.35%in 2017 to 38.11%in 2021.Additionally,there was an enhancement in the standardized management rate of chronic diseases.Affordability assessments showed that the proportion of medical income derived from the medical insurance fund increased by nearly 22.81 percentage points between 2012 and 2021.By 2021,75.02%of medical patients were covered by medical insurance,representing an increase of approximately 44 percentage points from 31.19%in 2012.Conclusions:The implementation of the urban medical group in Dapeng New District has led to substantial improvements in healthcare accessibility,safety and quality,and affordability.Future initiatives will focus on advancing the“Dapeng Mode”to generate exemplary healthcare outcomes and minimize disparities in basic health services and health status between urban and rural populations.The reform agenda includes piloting payment reforms and innovative payment models within the Dapeng group,complemented by a health assessment and performance incentive system aimed at encouraging healthcare institutions to prioritize health management.展开更多
The rapid advancement of artificial intelligence technology is driving transformative changes in medical diagnosis,treatment,and management systems through large-scale deep learning models-a process that brings both g...The rapid advancement of artificial intelligence technology is driving transformative changes in medical diagnosis,treatment,and management systems through large-scale deep learning models-a process that brings both groundbreaking opportunities and multifaceted challenges.This study focuses on the medical and healthcare applications of large-scale deep learning architectures,conducting a comprehensive survey to categorize and analyze their diverse uses.The survey results reveal that current applications of large models in healthcare encompass medical data management,healthcare services,medical devices,and preventive medicine,among others.Concurrently,large models demonstrate significant advantages in the medical domain,especially in high-precision diagnosis and prediction,data analysis and knowledge discovery,and enhancing operational efficiency.Nevertheless,we identify several challenges that need urgent attention,including improving the interpretability of large models,strengthening privacy protection,and addressing issues related to handling incomplete data.This research is dedicated to systematically elucidating the deep collaborative mechanisms between artificial intelligence and the healthcare field,providing theoretical references and practical guidance for both academia and industry.展开更多
This study systematically analyzes the genre structure and linguistic features of 42 English abstracts from six internationally renowned medical journals,based on the revised CARS model proposed by Swales.The research...This study systematically analyzes the genre structure and linguistic features of 42 English abstracts from six internationally renowned medical journals,based on the revised CARS model proposed by Swales.The research findings indicate that medical abstracts typically follow a three-step structure:“Establishing a Research Territory-Establishing a Research Niche-Occupying the Niche”,where the steps“Research Purpose”and“Research Results”are the most frequently utilized,forming the core content of the abstracts.Within the sequence of moves,81%conform to conventional patterns,while a minority of samples exhibit unconventional structures such as inversion,cycling,and repetition.In terms of linguistic features,the present simple tense and active voice are predominantly used,reflecting the universality of the research and the author’s agency;conversely,the simple past tense and passive voice are primarily employed to describe research methods and processes.This study reveals the writing conventions of medical abstracts,providing empirical evidence and genre reference for non-native scholars in the preparation and publication of their work in international journals.展开更多
基金The Natural Science Foundation of Hebei Province(F2024501044).
文摘The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can simultaneously process multi-modality data such as medical images and medical reports.These models can not only recognize images,but also understand the semantic relationship between images and texts,effectively realize the integration of medical information,and provide strong support for clinical decision-making and disease diagnosis.The visual-language large model has good performance for specific medical tasks,and also shows strong potential and high intelligence in the general task models.This paper provides a comprehensive review of the visual-language large model in the field of medical health.Specifically,this paper first introduces the basic theoretical basis and technical principles.Then,this paper introduces the specific application scenarios in the field of medical health,including modality fusion,semi-supervised learning,weakly supervised learning,unsupervised learning,cross-domain model and general models.Finally,the challenges including insufficient data,interpretability,and practical deployment are discussed.According to the existing challenges,four potential future development directions are given.
基金The Fourth Batch of Practice Teaching-Reform Project of Affiliated Hospital/Clinical Medical College of Hebei University,Research on the Application of CBL Combined With PBL Teaching Mode in the Teaching of Medical Imaging for Undergraduates of Clinical Majors(Project No.:2023P002).
文摘Objective:To explore the application effect of Case-based Learning(CBL)combined with Problem-Based Learning(PBL)in the teaching of medical imaging to undergraduate students majoring in clinical medicine.Methods:Undergraduates of clinical medicine majoring in the School of Clinical Medicine of Hebei University were selected as the research subjects and divided into the experimental group(CBL combined with PBL teaching mode)and the control group(traditional teaching mode),and the teaching effect was evaluated by the examination results and questionnaires.Results:The test scores of the experimental group were significantly better than those of the control group(P<0.05),and the satisfaction of the students in the experimental group reached more than 90%.Conclusion:CBL combined with PBL teaching mode can effectively improve the teaching quality of medical imaging in clinical medicine specialty.
文摘Objective:To analyze the effectiveness of the matrix medical administration model in enhancing medical safety management.Method:A total of 39 medical incidents in the hospital from September 2020 to September 2022 were selected as the reference group,implementing conventional medical administration.Another 39 medical incidents from October 2022 to October 2024 were chosen as the experimental group,adopting the matrix medical administration model.The practical indicators such as causes of medical disputes,dispute compensation,medical injury appraisal results,and diagnosis and treatment quality indicators were compared between the two groups.Results:In the experimental group,the primary reasons for medical disputes were patient-related,and most disputes resulted in no compensation.After medical injury appraisal,most cases were not considered medical injuries.The comparison between the two groups was statistically significant(P<0.05).The diagnosis and treatment quality indicators of the experimental group were superior to those of the reference group(P<0.05).Conclusion:The matrix medical administration model can reduce medical disputes caused by hospital factors,decrease the proportion of compensation and the incidence of medical injuries,and improve the quality of diagnosis and treatment,demonstrating high management effectiveness.
基金supported by the Innovation Fund for Medical Sciences of the Chinese Academy of Medical Sciences(2021-I2M-1-033)the National Key Research and Development Program of China(2022YFF0711900).
文摘Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.
基金Scientific Research Project of Huanggang Polytechnic College(Project No.:2024C2012118)Teaching Research Project of Huanggang Polytechnic College(Project No.:2021C2041109)+1 种基金Scientific Research Project of Hubei Provincial Department of Education(Project No.:B2024453)Youth Project of Shandong Provincial Natural Science Foundation(Project No.:ZR2021QH200)。
文摘Higher vocational medical education plays a crucial role in the cultivation of outstanding medical talents.Based on the CIPP evaluation model(Context,Input,Process,Product),this paper conducts a systematic analysis of the development of higher vocational medical education,explores the achievements and challenges faced at each stage from multiple dimensions,and puts forward improvement suggestions.This study uses the CIPP model to carry out a systematic review of medical education programs and analyzes their application value from both theoretical and practical perspectives.
文摘Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved information retrieval efficiency to a certain extent,they exhibit significant limitations in adaptability and accuracy when dealing with procurement documents characterized by diverse formats and a high degree of unstructured content.The emergence of Large Language Models(LLMs)offers new possibilities for efficient procurement information processing and extraction.Methods:This study collected procurement transaction documents from public procurement websites,and proposed a procurement Information Extraction(IE)method based on LLMs.Unlike traditional approaches,this study systematically explores the applicability of LLMs in both structured and unstructured entities in procurement documents,addressing the challenges posed by format variability and content complexity.Furthermore,an optimized prompt framework tailored for procurement document extraction tasks is developed to enhance the accuracy and robustness of IE.The aim is to process and extract key information from medical device procurement quickly and accurately,meeting stakeholders'demands for precision and timeliness in information retrieval.Results:Experimental results demonstrate that,compared to traditional methods,the proposed approach achieves an F1 Score of 0.9698,representing a 4.85%improvement over the best baseline model.Moreover,both recall and precision rates are close to 97%,significantly outperforming other models and exhibiting exceptional overall recognition capabilities.Notably,further analysis reveals that the proposed method consistently maintains high performance across both structured and unstructured entities in procurement documents while balancing recall and precision effectively,demonstrating its adaptability in handling varying document formats.The results of ablation experiments validate the effectiveness of the proposed prompting strategy.Conclusion:Additionally,this study explores the challenges and potential improvements of the proposed method in IE tasks and provides insights into its feasibility for real-world deployment and application directions,further clarifying its adaptability and value.This method not only exhibits significant advantages in medical device procurement but also holds promise for providing new approaches to information processing and decision support in various domains.
基金supported by Natural Science Foundation Programme of Gansu Province(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Science and Technology Plan Key Research and Development Program Project(No.24YFFA024).
文摘Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.
基金Supported by the National Natureal Science Foundation of China (6 98730 36 )
文摘This paper describes how to use the Unified Modeling Language (UML) to modeling software processes in medical insurance MIS, and compares UML Modeling method with classic PO(Process Oriented) Modeling method. It indicates that the whole performance of application system model described by UML is much better than the one described by PO.
基金Chinese National Key Research and Development Program(No.2017YFB1400604).
文摘activities.Ex-periments on a synthetic log of the non-secondary hy-pertension MTP and empirical findings demonstrate the effectiveness of our approach.The results show that the process mining in our approach framework can automatically generate more accurate MTP mod-els,and the subprocess models based on treatment pat-terns make the models easy to understand.
文摘The medical herbs should be dried directly after harvesting, otherwise, insects and fungi spoilage them. Conventional drying methods such as open sun drying and conventional fuel dryers are not suitable, since they contaminate the herbs, decrease the drying efficiency and at the same time increase the drying cost. Therefore, the recent trend is toward the harnessing the renewable energy to dry the medical herbs. This paper presents a new controlled drying system, which uses a solar collector and an electric heater to heat the drying air, and a PV-WIND hybrid system to supply the required electric energy to the dryer electric load. This paper also presents the dynamic modeling, simulation, and control of the suggested thermal system (i.e., the solar thermal system and the electrical heater). Moreover, this paper, exhibits the different results of the thermal system, to check the effectiveness of this system that fulfills the requirements of the drying operation. The system results are found to satisfy the ultimate goals of the drying operation.
文摘Based on the analysis of the current college Medical English teaching and the study of the blendedteaching model,the author practiced the model in the real teaching.In the process,the author verified the usefulness and effectiveness of the Blended-teaching Model,which can break the limitation of the classroom teaching.The students could contact with each other and also with the teacher conveniently whatever in-class and offclass in the learning process.
文摘Practicing dialogue-scene simulation in medical English can integrate effectively learning resources, activate the class atmosphere, enrich contents of courses, train language skills and raise students' cooperated consciousness.Furthermore,practicing dialogue-scene simulation can promote reform of teaching estimation-model, teaching quality and efficiency. In a word, practicing dialogue-scene simulation deserves further exploration and promotion.
文摘This study explored and reviewed the logistic regression (LR) model, a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, with emphasis on medical research. Thirty seven research articles published between 2000 and 2018 which employed logistic regression as the main statistical tool as well as six text books on logistic regression were reviewed. Logistic regression concepts such as odds, odds ratio, logit transformation, logistic curve, assumption, selecting dependent and independent variables, model fitting, reporting and interpreting were presented. Upon perusing the literature, considerable deficiencies were found in both the use and reporting of LR. For many studies, the ratio of the number of outcome events to predictor variables (events per variable) was sufficiently small to call into question the accuracy of the regression model. Also, most studies did not report on validation analysis, regression diagnostics or goodness-of-fit measures;measures which authenticate the robustness of the LR model. Here, we demonstrate a good example of the application of the LR model using data obtained on a cohort of pregnant women and the factors that influence their decision to opt for caesarean delivery or vaginal birth. It is recommended that researchers should be more rigorous and pay greater attention to guidelines concerning the use and reporting of LR models.
基金Project(50378062) supported by the National Natural Science Foundation of ChinaProject(09JCYBJC08100) supported by the Natural Science Foundation of Tianjin City,China
文摘Thermogravimetric study of medical transfusion tube （MTT） waste containing polyvinyl chloride （PVC） was carried out using the thermogravimetric analyser （TGA） with N2, at different heating rates of 5, 10, 20, 30, 50 ℃/min. The purpose is to obtain pyrolysis characteristics and kinetic parameters of medical waste. The experimental results indicate that the pyrolysis behavior of the MTT sample is in agreement with its main ingredient of PVC, appearing two stair stepping stages. The influence of the additives in MTT on pyrolysis behavior was also revealed, which could improve MTT pyrolysis at lower temperature in the first stage, and cause obvious unsmoothness and asymmetry of the second DTG peak. Four n-order kinetic models of Coats-Redfern, Ozawa, Kissinger and Freeman-carroll were used to get the kinetic parameters. Furthermore, a novel ＂two-step four-reaction model＂ was established to simulate the whole continuous process. The different methods and the kinetic parameters thus obtained were discussed and compared with each other in literatures. The reasons of deviation among kinetic values were tried to be elucidated. The new established model could more satisfactorily describe the pyrolysis process of MTT, being more mechanistic and conveniently serving for the engineering.
文摘With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.
文摘Objective: This study assesses the quality of artificial intelligence chatbots in responding to standardized obstetrics and gynecology questions. Methods: Using ChatGPT-3.5, ChatGPT-4.0, Bard, and Claude to respond to 20 standardized multiple choice questions on October 7, 2023, responses and correctness were recorded. A logistic regression model assessed the relationship between question character count and accuracy. For each incorrect question, an independent error analysis was undertaken. Results: ChatGPT-4.0 scored a 100% across both obstetrics and gynecology questions. ChatGPT-3.5 scored a 95% overall, earning an 85.7% in obstetrics and a 100% in gynecology. Claude scored a 90% overall, earning a 100% in obstetrics and an 84.6% in gynecology. Bard scored a 77.8% overall, earning an 83.3% in obstetrics and a 75% in gynecology and would not respond to two questions. There was no statistical significance between character count and accuracy. Conclusions: ChatGPT-3.5 and ChatGPT-4.0 excelled in both obstetrics and gynecology while Claude performed well in obstetrics but possessed minor weaknesses in gynecology. Bard comparatively performed the worst and had the most limitations, leading to our support of the other artificial intelligence chatbots as preferred study tools. Our findings support the use of chatbots as a supplement, not a substitute for clinician-based learning or historically successful educational tools.
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.
文摘Background:An urban medical group in Dapeng New District was established in 2017 with the objective of enhancing outcomes for common diseases and reinforcing primary care by integrating high‐level hospitals with primary health services.This study aimed to evaluate the performance of the urban medical group using the triangular value chain framework.Methods:The evaluation was conducted using the Donabedian model,focusing on three key dimensions:safety and quality,accessibility,and affordability.Longitudinal data were collected from 2016 to 2022 through government annual reports,the medical insurance bureau,and hospital information systems.Preprogram and postprogram outcome measurements were compared to assess differences and trends,providing a clear picture of the program's effectiveness.Results:Accessibility improved significantly,with the number of hospital beds per 1000 residents increasing from 2.62 in 2017 to 3.76 in 2022.The availability of general practitioners(GPs)also rose markedly,from 0 per 10,000 residents in 2017 to 6.27 in 2022.Regarding safety and quality,the proportion of complex medical procedures conducted within the New District expanded substantially,from 7.35%in 2017 to 38.11%in 2021.Additionally,there was an enhancement in the standardized management rate of chronic diseases.Affordability assessments showed that the proportion of medical income derived from the medical insurance fund increased by nearly 22.81 percentage points between 2012 and 2021.By 2021,75.02%of medical patients were covered by medical insurance,representing an increase of approximately 44 percentage points from 31.19%in 2012.Conclusions:The implementation of the urban medical group in Dapeng New District has led to substantial improvements in healthcare accessibility,safety and quality,and affordability.Future initiatives will focus on advancing the“Dapeng Mode”to generate exemplary healthcare outcomes and minimize disparities in basic health services and health status between urban and rural populations.The reform agenda includes piloting payment reforms and innovative payment models within the Dapeng group,complemented by a health assessment and performance incentive system aimed at encouraging healthcare institutions to prioritize health management.
基金funded by the National Natural Science Foundation of China(Grant No.62272236)the Natural Science Foundation of Jiangsu Province(Grant No.BK20201136).
文摘The rapid advancement of artificial intelligence technology is driving transformative changes in medical diagnosis,treatment,and management systems through large-scale deep learning models-a process that brings both groundbreaking opportunities and multifaceted challenges.This study focuses on the medical and healthcare applications of large-scale deep learning architectures,conducting a comprehensive survey to categorize and analyze their diverse uses.The survey results reveal that current applications of large models in healthcare encompass medical data management,healthcare services,medical devices,and preventive medicine,among others.Concurrently,large models demonstrate significant advantages in the medical domain,especially in high-precision diagnosis and prediction,data analysis and knowledge discovery,and enhancing operational efficiency.Nevertheless,we identify several challenges that need urgent attention,including improving the interpretability of large models,strengthening privacy protection,and addressing issues related to handling incomplete data.This research is dedicated to systematically elucidating the deep collaborative mechanisms between artificial intelligence and the healthcare field,providing theoretical references and practical guidance for both academia and industry.
文摘This study systematically analyzes the genre structure and linguistic features of 42 English abstracts from six internationally renowned medical journals,based on the revised CARS model proposed by Swales.The research findings indicate that medical abstracts typically follow a three-step structure:“Establishing a Research Territory-Establishing a Research Niche-Occupying the Niche”,where the steps“Research Purpose”and“Research Results”are the most frequently utilized,forming the core content of the abstracts.Within the sequence of moves,81%conform to conventional patterns,while a minority of samples exhibit unconventional structures such as inversion,cycling,and repetition.In terms of linguistic features,the present simple tense and active voice are predominantly used,reflecting the universality of the research and the author’s agency;conversely,the simple past tense and passive voice are primarily employed to describe research methods and processes.This study reveals the writing conventions of medical abstracts,providing empirical evidence and genre reference for non-native scholars in the preparation and publication of their work in international journals.
