Clinical questionnaire filling based on question answering framework.
Background: Electronic Health Records (EHR) are the foundation of much medical research. However, analyzing the text data of EHRs directly is an challenging task. Therefore, physicians often use questionnaires to first convert text data to structured data. Filling in these questionnaires requires a...
| Publicado en: | International Journal of Medical Informatics Vol. 141 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146537471&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146537471 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Sep2020 vid: 141 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 146537471 146537471 NLM32590229 146537471 10.1016/j.ijmedinf.2020.104225 NLM32590229 146537471 ppct: 1 formats: tig: atl: Clinical questionnaire filling based on question answering framework. aug: au: Ren, Jiangtao Liu, Naiyin Wu, Xiaojing affil: School of Data and Computer Science, Guangdong Province Key Lab of Computational Science, Sun Yat-sen University, Guangdong 510275, People's Republic of China sug: subj: Information Retrieval Algorithms Human ab: Background: Electronic Health Records (EHR) are the foundation of much medical research. However, analyzing the text data of EHRs directly is an challenging task. Therefore, physicians often use questionnaires to first convert text data to structured data. Filling in these questionnaires requires a considerable amount of time and medical knowledge. It is a significant task to develop an algorithm to make computers fill out these questionnaires automatically.Objective: This research aims to build a deep learning model that can automatically complete questionnaires with given medical text.Methods: This task is a part of Information Extraction (IE), but it differs from the existing tasks of medical IE. Because of the questions in questionnaires are closed-end type, which refers to making a selection among given options, we could treat this task as a classification problem. However, conventional classification algorithms are resource-consuming when filling out one entire questionnaire with one model. They also could not use the question information to guide the questionnaire filling task. To handle these issues, we propose a neural network model based on question answering (QA) framework in this paper. With this framework, our neural network can fill out one complete questionnaire using only one model.Results: We perform experiments on three real-world Chinese medical datasets and related clinical questionnaires. Our model respectively achieves F1 scores of 0.9273, 0.8834, and 0.9846. The results outperform several baseline models.Conclusion: The strong performance of our QA model will allow us to build a system which can help physicians to fill out questionnaires and convert text data to structured data. This system can reduce the workload of physicians. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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