Ontology-driven weak supervision for clinical entity classification in electronic health records
| العنوان: | Ontology-driven weak supervision for clinical entity classification in electronic health records |
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| المؤلفون: | Jose D. Posada, Ethan Steinberg, Scott L. Fleming, Saelig Khattar, Jason A. Fries, Nigam H. Shah, Alison Callahan |
| المصدر: | Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021) ArXiv Nature Communications |
| بيانات النشر: | Nature Portfolio, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, 0301 basic medicine, Computer Science - Machine Learning, Computer science, Science, Datasets as Topic, General Physics and Astronomy, Expert Systems, Temporality, Information needs, Ontology (information science), computer.software_genre, Article, General Biochemistry, Genetics and Molecular Biology, Machine Learning (cs.LG), Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Health care, Electronic Health Records, Humans, 030212 general & internal medicine, Data Curation, Natural Language Processing, Computer Science - Computation and Language, Multidisciplinary, SARS-CoV-2, business.industry, Event (computing), COVID-19, General Chemistry, Data science, Expert system, 3. Good health, Data processing, 030104 developmental biology, business, Literature mining, Computation and Language (cs.CL), computer, Agile software development |
| الوصف: | In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove’s ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors. In the electronic health record, using clinical notes to identify entities such as disorders and their temporality can inform many important analyses. Here, the authors present a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. |
| اللغة: | English |
| تدمد: | 2041-1723 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a53ae453b99740b308c4800540fee283 https://doaj.org/article/2f95d01741cc4c879cc47cb28144da3f |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....a53ae453b99740b308c4800540fee283 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 20411723 |
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