Abstract
Research in biomedical field requires technical infrastructures to deal with heterogeneous and multi-sourced big data. Biomedical informatics primarily use data in electronic health to better understand how diseases spread or to gather new insights from patient history. One of the prominent use cases of electronic health records is identification of patient cohort (group) with specific disease or some common characteristics, so that useful inferences may be drawn via these records. This paper proposes a methodology for identification and analysis of cohorts for patients having congestive heart failure problem among obesity patients. This may help doctors and medical researchers in predicting outcomes, survival analysis of patients, clinical trials, and other types of retroactive studies. cTAKES tool was used to apply natural language processing technique in order to identify patients belonging to a particular cohort. All clinical terms were identified and were mapped to its matching terms in the UMLS Metathesaurus. Also, negated statements were detected and removed from the final cohort. The method is reasonably automated and achieves accuracy, precision, recall, and F-score values of 0.970, 0.972, 0.958, and 0.965, respectively. Results were compared against the experts annotations. Additionally, manual review of clinical records was performed for further validation.
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Baliyan, N., Johar, A., Bhardwaj, P. (2021). Identification of Congestive Heart Failure Patients Through Natural Language Processing. In: Verma, G.K., Soni, B., Bourennane, S., Ramos, A.C.B. (eds) Data Science. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-1681-5_26
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