BACKGROUND with the widespread application of computer network systems in the medical field,the plan-do-check-action(PDCA)and the international classification of diseases tenth edition(ICD-10)coding system have also a...BACKGROUND with the widespread application of computer network systems in the medical field,the plan-do-check-action(PDCA)and the international classification of diseases tenth edition(ICD-10)coding system have also achieved favorable results in clinical medical record management.However,research on their combined application is relatively lacking.Objective:it was to explore the impact of network systems and PDCA management mode on ICD-10 encoding.Material and Method:a retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.AIM To study the adoption of network and PDCA in the ICD-10.METHODS A retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.RESULTS In the 3,6,12,18,and 24 months of PDCA cycle management mode,the coding accuracy and medical record completion rate were higher,and the coding time was lower in the observation group as against the controls(P<0.05).The satisfaction of coders(80.22%vs 53.45%)and patients(84.89%vs 51.72%)in the observation group was markedly higher as against the controls(P<0.05).CONCLUSION The combination of computer networks and PDCA can improve the accuracy,efficiency,completion rate,and satisfaction of ICD-10 coding.展开更多
Assigning standardized International Classification of Disease(ICD)codes to Electronic Medical Records(EMR)is crucial for enhancing the efficiency and accuracy of medical coding processes.However,existing methods face...Assigning standardized International Classification of Disease(ICD)codes to Electronic Medical Records(EMR)is crucial for enhancing the efficiency and accuracy of medical coding processes.However,existing methods face challenges in effectively capturing,integrating,and amalgamating specialized medical knowledge from complex textual data.In this study,we propose GoM-ICD,an advanced automatic ICD coding framework that integrates multiple gap schemes with a Mixture of Experts(MoE)architecture.GoM-ICD is designed to address the extreme multilabel text classification in ICD coding.It segments and reassembles text to facilitate seamless information exchange across different chunks,employing various segmentation methods derived from different gap schemes.Then the model-level MoE consolidates the predictions of these methods to enhance the prediction performance.Specifically,the segmented text is input to a Pretrained Language Model(PLM)to extract textual features.Next,a Bidirectional Long Short-Term Memory network(BiLSTM)is employed to capture long-term contextual information from the textual features.Finally,a text-level MoE,followed by a label-level MoE,enables precise attention matching between text and labels,thereby improving the fidelity of the coding process.The three levels of MoE leverage the collective insights of diverse expert models,effectively processing multi-dimensional text features and unifying model-level insights from various gap schemes.Extensive experimental results demonstrate that GoM-ICD achieves the state-of-the-art performance in automatic ICD coding tasks,reaching micro-F1 of 0.617,0.620,and 0.613 on datasets MIMIC III full,MIMIC-III clean,and MIMIC-IV ICD-10,respectively.The source code can be obtained from https://github.com/CSUBioGroup/GoM-ICD.展开更多
The International Classification of Diseases(ICD)is an international standard and tool for epidemiological in-vestigation,health management,and clinical diagnosis with a fundamental role in intelligent medical care.Th...The International Classification of Diseases(ICD)is an international standard and tool for epidemiological in-vestigation,health management,and clinical diagnosis with a fundamental role in intelligent medical care.The assignment of ICD codes to health-related documents has become a focus of academic research,and numerous studies have developed the process of ICD coding from manual to automated work.In this survey,we review the developmental history of this task in recent decades in depth,from the rules-based stage,through the traditional machine learning stage,to the neural-network-based stage.Various methods have been introduced to solve this problem by using different techniques,and we report a performance comparison of different methods on the pub-licly available Medical Information Mart for Intensive Care dataset.Next,we summarize four major challenges of this task:(1)the large label space,(2)the unbalanced label distribution,(3)the long text of documents,and(4)the interpretability of coding.Various solutions that have been proposed to solve these problems are analyzed.Further,we discuss the applications of ICD coding,from mortality statistics to payments based on disease-related groups and hospital performance management.In addition,we discuss different ways of considering and evaluat-ing this task,and how it has been transformed into a learnable problem.We also provide details of the commonly used datasets.Overall,this survey aims to provide a reference and possible prospective directions for follow-up research work.展开更多
文摘BACKGROUND with the widespread application of computer network systems in the medical field,the plan-do-check-action(PDCA)and the international classification of diseases tenth edition(ICD-10)coding system have also achieved favorable results in clinical medical record management.However,research on their combined application is relatively lacking.Objective:it was to explore the impact of network systems and PDCA management mode on ICD-10 encoding.Material and Method:a retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.AIM To study the adoption of network and PDCA in the ICD-10.METHODS A retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.RESULTS In the 3,6,12,18,and 24 months of PDCA cycle management mode,the coding accuracy and medical record completion rate were higher,and the coding time was lower in the observation group as against the controls(P<0.05).The satisfaction of coders(80.22%vs 53.45%)and patients(84.89%vs 51.72%)in the observation group was markedly higher as against the controls(P<0.05).CONCLUSION The combination of computer networks and PDCA can improve the accuracy,efficiency,completion rate,and satisfaction of ICD-10 coding.
文摘Assigning standardized International Classification of Disease(ICD)codes to Electronic Medical Records(EMR)is crucial for enhancing the efficiency and accuracy of medical coding processes.However,existing methods face challenges in effectively capturing,integrating,and amalgamating specialized medical knowledge from complex textual data.In this study,we propose GoM-ICD,an advanced automatic ICD coding framework that integrates multiple gap schemes with a Mixture of Experts(MoE)architecture.GoM-ICD is designed to address the extreme multilabel text classification in ICD coding.It segments and reassembles text to facilitate seamless information exchange across different chunks,employing various segmentation methods derived from different gap schemes.Then the model-level MoE consolidates the predictions of these methods to enhance the prediction performance.Specifically,the segmented text is input to a Pretrained Language Model(PLM)to extract textual features.Next,a Bidirectional Long Short-Term Memory network(BiLSTM)is employed to capture long-term contextual information from the textual features.Finally,a text-level MoE,followed by a label-level MoE,enables precise attention matching between text and labels,thereby improving the fidelity of the coding process.The three levels of MoE leverage the collective insights of diverse expert models,effectively processing multi-dimensional text features and unifying model-level insights from various gap schemes.Extensive experimental results demonstrate that GoM-ICD achieves the state-of-the-art performance in automatic ICD coding tasks,reaching micro-F1 of 0.617,0.620,and 0.613 on datasets MIMIC III full,MIMIC-III clean,and MIMIC-IV ICD-10,respectively.The source code can be obtained from https://github.com/CSUBioGroup/GoM-ICD.
基金Beijing Municipal Natural Science Foundation(Grant No.M22012)BUPT Excellent Ph.D.Students Foundation(Grant No.CX2021122).
文摘The International Classification of Diseases(ICD)is an international standard and tool for epidemiological in-vestigation,health management,and clinical diagnosis with a fundamental role in intelligent medical care.The assignment of ICD codes to health-related documents has become a focus of academic research,and numerous studies have developed the process of ICD coding from manual to automated work.In this survey,we review the developmental history of this task in recent decades in depth,from the rules-based stage,through the traditional machine learning stage,to the neural-network-based stage.Various methods have been introduced to solve this problem by using different techniques,and we report a performance comparison of different methods on the pub-licly available Medical Information Mart for Intensive Care dataset.Next,we summarize four major challenges of this task:(1)the large label space,(2)the unbalanced label distribution,(3)the long text of documents,and(4)the interpretability of coding.Various solutions that have been proposed to solve these problems are analyzed.Further,we discuss the applications of ICD coding,from mortality statistics to payments based on disease-related groups and hospital performance management.In addition,we discuss different ways of considering and evaluat-ing this task,and how it has been transformed into a learnable problem.We also provide details of the commonly used datasets.Overall,this survey aims to provide a reference and possible prospective directions for follow-up research work.
