Author:
Raza Shaina,Schwartz Brian
Abstract
AbstractBackgroundExtracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data.ObjectiveThis study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.MethodsThe proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.ResultsThe named entity recognition implementation in the NLP layer achieves a performance gain of about 1–3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1–8% better). A thorough examination reveals the disease’s presence and symptoms prevalence in patients.ConclusionsA similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.
Funder
Canadian Institutes of Health Research
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Health Policy,Computer Science Applications
Cited by
15 articles.
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