Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activitie...Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks,a GloVe word embedding model was used to vectorize words.In the NER task,a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task,the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset,the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks,where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover,the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios,laying the foundation for the subsequent work in the EMR field.展开更多
A mobile medical information system (MMIS) is an integrated application (app) of traditional hospital information systems (HIS) which comprise a picture archiving and communications system (PACS), laboratory informati...A mobile medical information system (MMIS) is an integrated application (app) of traditional hospital information systems (HIS) which comprise a picture archiving and communications system (PACS), laboratory information system (LIS), pharmaceutical management information system (PMIS), radiology information system (RIS), and nursing information system (NIS). A dynamic resource allocation table is critical for optimizing the performance to the mobile system, including the doctors, nurses, or other relevant health workers. We have designed a smart dynamic resource allocation model by using the C4.5 algorithm and cumulative distribution for optimizing the weight of resource allocated for the five major attributes in a cooperation communications system. Weka is used in this study. The class of concept is the performance of the app, optimal or suboptimal. Three generations of optimization of the weight in accordance with the optimizing rate are shown.展开更多
为探讨改进麻醉信息管理系统(AIM S)对提高麻醉文书质量的作用,该文通过对AIM S的4大体系细节功能的改进,随机抽取改进前和改进后的麻醉文书各6000份,对麻醉文书的合格量、合格率、缺陷项、缺陷率进行数据的统计分析,并探讨AI M S细节...为探讨改进麻醉信息管理系统(AIM S)对提高麻醉文书质量的作用,该文通过对AIM S的4大体系细节功能的改进,随机抽取改进前和改进后的麻醉文书各6000份,对麻醉文书的合格量、合格率、缺陷项、缺陷率进行数据的统计分析,并探讨AI M S细节功能的改进对麻醉文书的质量及麻醉医生文书工作时间的影响。结果证明,改进后的麻醉文书合格率较改进前升高34.9%,4大体系均有明显改善,9项缺陷率均显著下降(P<0.05);麻醉医生文书工作时间显著减少(P<0.05)。说明改进的AIMS明显提高麻醉文书的质量,显著降低了缺陷率;大大减少麻醉医生的工作时间,提高质控效率;麻醉信息系统细节功能的改进促进了麻醉质量管理更加的实时、完整且精确。展开更多
基金Supported by the Zhejiang Provincial Natural Science Foundation(No.LQ16H180004)~~
文摘Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks,a GloVe word embedding model was used to vectorize words.In the NER task,a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task,the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset,the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks,where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover,the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios,laying the foundation for the subsequent work in the EMR field.
文摘A mobile medical information system (MMIS) is an integrated application (app) of traditional hospital information systems (HIS) which comprise a picture archiving and communications system (PACS), laboratory information system (LIS), pharmaceutical management information system (PMIS), radiology information system (RIS), and nursing information system (NIS). A dynamic resource allocation table is critical for optimizing the performance to the mobile system, including the doctors, nurses, or other relevant health workers. We have designed a smart dynamic resource allocation model by using the C4.5 algorithm and cumulative distribution for optimizing the weight of resource allocated for the five major attributes in a cooperation communications system. Weka is used in this study. The class of concept is the performance of the app, optimal or suboptimal. Three generations of optimization of the weight in accordance with the optimizing rate are shown.
文摘为探讨改进麻醉信息管理系统(AIM S)对提高麻醉文书质量的作用,该文通过对AIM S的4大体系细节功能的改进,随机抽取改进前和改进后的麻醉文书各6000份,对麻醉文书的合格量、合格率、缺陷项、缺陷率进行数据的统计分析,并探讨AI M S细节功能的改进对麻醉文书的质量及麻醉医生文书工作时间的影响。结果证明,改进后的麻醉文书合格率较改进前升高34.9%,4大体系均有明显改善,9项缺陷率均显著下降(P<0.05);麻醉医生文书工作时间显著减少(P<0.05)。说明改进的AIMS明显提高麻醉文书的质量,显著降低了缺陷率;大大减少麻醉医生的工作时间,提高质控效率;麻醉信息系统细节功能的改进促进了麻醉质量管理更加的实时、完整且精确。
