Personalized treatment options for chronic diseases using precision cohort analytics

Abstract To support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction...

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Autores principales:	Kenney Ng, Uri Kartoun, Harry Stavropoulos, John A. Zambrano, Paul C. Tang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/732350a900b846b3b8aacc67b2c0466f
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spelling	oai:doaj.org-article:732350a900b846b3b8aacc67b2c0466f2021-12-02T15:23:01ZPersonalized treatment options for chronic diseases using precision cohort analytics10.1038/s41598-021-80967-52045-2322https://doaj.org/article/732350a900b846b3b8aacc67b2c0466f2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-80967-5https://doaj.org/toc/2045-2322Abstract To support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.Kenney NgUri KartounHarry StavropoulosJohn A. ZambranoPaul C. TangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kenney Ng Uri Kartoun Harry Stavropoulos John A. Zambrano Paul C. Tang Personalized treatment options for chronic diseases using precision cohort analytics
description	Abstract To support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.
format	article
author	Kenney Ng Uri Kartoun Harry Stavropoulos John A. Zambrano Paul C. Tang
author_facet	Kenney Ng Uri Kartoun Harry Stavropoulos John A. Zambrano Paul C. Tang
author_sort	Kenney Ng
title	Personalized treatment options for chronic diseases using precision cohort analytics
title_short	Personalized treatment options for chronic diseases using precision cohort analytics
title_full	Personalized treatment options for chronic diseases using precision cohort analytics
title_fullStr	Personalized treatment options for chronic diseases using precision cohort analytics
title_full_unstemmed	Personalized treatment options for chronic diseases using precision cohort analytics
title_sort	personalized treatment options for chronic diseases using precision cohort analytics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/732350a900b846b3b8aacc67b2c0466f
work_keys_str_mv	AT kenneyng personalizedtreatmentoptionsforchronicdiseasesusingprecisioncohortanalytics AT urikartoun personalizedtreatmentoptionsforchronicdiseasesusingprecisioncohortanalytics AT harrystavropoulos personalizedtreatmentoptionsforchronicdiseasesusingprecisioncohortanalytics AT johnazambrano personalizedtreatmentoptionsforchronicdiseasesusingprecisioncohortanalytics AT paulctang personalizedtreatmentoptionsforchronicdiseasesusingprecisioncohortanalytics
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Personalized treatment options for chronic diseases using precision cohort analytics

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