Patient similarity for precision medicine: A systematic review.

Autor: Parimbelli E; Telfer School of Management, University of Ottawa, Ottawa, Canada; Interdepartmental Centre for Health Technologies, University of Pavia, Italy. Electronic address: enea.parimbelli@gmail.com., Marini S; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA; Interdepartmental Centre for Health Technologies, University of Pavia, Italy., Sacchi L; Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy; Interdepartmental Centre for Health Technologies, University of Pavia, Italy., Bellazzi R; Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy; Interdepartmental Centre for Health Technologies, University of Pavia, Italy; RCCS ICS Maugeri, Pavia, Italy.
Jazyk: angličtina
Zdroj: Journal of biomedical informatics [J Biomed Inform] 2018 Jul; Vol. 83, pp. 87-96. Date of Electronic Publication: 2018 Jun 01.
DOI: 10.1016/j.jbi.2018.06.001
Abstrakt: Evidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting. Defining a patient similarity measure is thus an essential step to allow stratification of patients into clinically-meaningful subgroups. The present review investigates the use of patient similarity as a tool to enable precision medicine. 279 articles were analyzed along four dimensions: data types considered, clinical domains of application, data analysis methods, and translational stage of findings. Cancer-related research employing molecular profiling and standard data analysis techniques such as clustering constitute the majority of the retrieved studies. Chronic and psychiatric diseases follow as the second most represented clinical domains. Interestingly, almost one quarter of the studies analyzed presented a novel methodology, with the most advanced employing data integration strategies and being portable to different clinical domains. Integration of such techniques into decision support systems constitutes and interesting trend for future research.
(Copyright © 2018 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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