scholarly journals Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

2019 ◽  
pp. 70 ◽  
Author(s):  
Peilin Li ◽  
Zhenming Yuan ◽  
Wenbo Tu ◽  
Kai Yu ◽  
Dongxin Lu
Keyword(s):  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Medical Knowledge ◽  
Knowledge Extraction

scholarly journals A Novel Deep Learning–Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study

10.2196/27008 ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. e27008
Author(s):  
Li-Hung Yao ◽  
Ka-Chun Leung ◽  
Chu-Lin Tsai ◽  
Chien-Hua Huang ◽  
Li-Chen Fu
Keyword(s):  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Severity Index ◽  
University Hospital ◽  
Emergency Severity Index ◽  
National Hospital ◽  
Data Set ◽  
Care Survey ◽  
Ambulatory Medical Care

Background Emergency department (ED) crowding has resulted in delayed patient treatment and has become a universal health care problem. Although a triage system, such as the 5-level emergency severity index, somewhat improves the process of ED treatment, it still heavily relies on the nurse’s subjective judgment and triages too many patients to emergency severity index level 3 in current practice. Hence, a system that can help clinicians accurately triage a patient’s condition is imperative. Objective This study aims to develop a deep learning–based triage system using patients’ ED electronic medical records to predict clinical outcomes after ED treatments. Methods We conducted a retrospective study using data from an open data set from the National Hospital Ambulatory Medical Care Survey from 2012 to 2016 and data from a local data set from the National Taiwan University Hospital from 2009 to 2015. In this study, we transformed structured data into text form and used convolutional neural networks combined with recurrent neural networks and attention mechanisms to accomplish the classification task. We evaluated our performance using area under the receiver operating characteristic curve (AUROC). Results A total of 118,602 patients from the National Hospital Ambulatory Medical Care Survey were included in this study for predicting hospitalization, and the accuracy and AUROC were 0.83 and 0.87, respectively. On the other hand, an external experiment was to use our own data set from the National Taiwan University Hospital that included 745,441 patients, where the accuracy and AUROC were similar, that is, 0.83 and 0.88, respectively. Moreover, to effectively evaluate the prediction quality of our proposed system, we also applied the model to other clinical outcomes, including mortality and admission to the intensive care unit, and the results showed that our proposed method was approximately 3% to 5% higher in accuracy than other conventional methods. Conclusions Our proposed method achieved better performance than the traditional method, and its implementation is relatively easy, it includes commonly used variables, and it is better suited for real-world clinical settings. It is our future work to validate our novel deep learning–based triage algorithm with prospective clinical trials, and we hope to use it to guide resource allocation in a busy ED once the validation succeeds.

scholarly journals Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Lejun Gong ◽  
Zhifei Zhang ◽  
Shiqi Chen
Keyword(s):  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Clinical Entity ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Recognition Model ◽  
Named Entity ◽  
Model Based

Background. Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious missing sentence components, and unclear entity boundary. Moreover, the corpus of Chinese electronic medical records is difficult to obtain. Methods. Aiming at these characteristics of Chinese electronic medical records, this study proposed a Chinese clinical entity recognition model based on deep learning pretraining. The model used word embedding from domain corpus and fine-tuning of entity recognition model pretrained by relevant corpus. Then BiLSTM and Transformer are, respectively, used as feature extractors to identify four types of clinical entities including diseases, symptoms, drugs, and operations from the text of Chinese electronic medical records. Results. 75.06% Macro-P, 76.40% Macro-R, and 75.72% Macro-F1 aiming at test dataset could be achieved. These experiments show that the Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition effect. Conclusions. These experiments show that the proposed Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition performance.

scholarly journals Coreference Resolution in Vietnamese Electronic Medical Records

2018 ◽  
Vol 34 (2) ◽  
Author(s):  
Hung D. Nguyen ◽  
Tru H. Cao
Keyword(s):  
Support Vector Machine ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Research Work ◽  
Medical Knowledge ◽  
Support Vector ◽  
Coreference Resolution ◽  
Bag Of Words ◽  
Clinical Text ◽  
Lexical Similarity

Electronic medical records (EMR) have emerged as an important source of data for research in medicine andinformation technology, as they contain much of valuable human medical knowledge in healthcare and patienttreatment. This paper tackles the problem of coreference resolution in Vietnamese EMRs. Unlike in English ones,in Vietnamese clinical texts, verbs are often used to describe disease symptoms. So we first define rules to annotateverbs as mentions and consider coreference between verbs and other noun or adjective mentions possible. Thenwe propose a support vector machine classifier on bag-of-words vector representation of mentions that takes intoaccount the special characteristics of Vietnamese language to resolve their coreference. The achieved F1 scoreon our dataset of real Vietnamese EMRs provided by a hospital in Ho Chi Minh city is 91.4%. To the best of ourknowledge, this is the first research work in coreference resolution on Vietnamese clinical texts.Keywords: Clinical text, support vector machine, bag-of-words vector, lexical similarity, unrestricted coreference

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