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A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1-Data From Wearable Devices.
Lee, Woojung; Schwartz, Naomi; Bansal, Aasthaa; Khor, Sara; Hammarlund, Noah; Basu, Anirban; Devine, Beth.
Affiliation
  • Lee W; The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA. Electronic address: wwlwj08@uw.edu.
  • Schwartz N; The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
  • Bansal A; The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
  • Khor S; The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
  • Hammarlund N; The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA; Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA.
  • Basu A; The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
  • Devine B; The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
Value Health ; 26(2): 292-299, 2023 02.
Article in En | MEDLINE | ID: mdl-36115806
ABSTRACT

OBJECTIVES:

With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to wearable data in HEOR.

METHODS:

We identified studies published in PubMed between January 2016 and March 2021. Studies that included at least 1 HEOR-related Medical Subject Headings term, applied an ML, and used wearable data were eligible for inclusion. Two reviewers abstracted information including ML application types and data on which ML was applied and analyzed them using descriptive analyses.

RESULTS:

A total of 148 studies were identified from PubMed, among which 32 studies met the inclusion criteria. There has been an increase over time in the number of ML studies using wearable data. ML has been more frequently used for monitoring events in real time (78%) than to predict future events (22%). There has been a wide range of outcomes examined, ranging from general physical or mental health (24%) to more disease-specific outcomes (eg, disease incidence [19%] and progression [13%]) and treatment-related outcomes (eg, treatment adherence [9%] and outcomes [9%]). Data for ML models were more often derived from wearable devices with specific medical purposes (60%) than those without (40%).

CONCLUSION:

There has been a wide range of applications of ML to wearable data. Both medical and nonmedical wearable devices have been used as a data source, showing the potential for providing rich data for ML studies in HEOR.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Economics, Medical / Wearable Electronic Devices Type of study: Health_economic_evaluation / Prognostic_studies / Systematic_reviews Limits: Humans Language: En Journal: Value Health Journal subject: FARMACOLOGIA Year: 2023 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Economics, Medical / Wearable Electronic Devices Type of study: Health_economic_evaluation / Prognostic_studies / Systematic_reviews Limits: Humans Language: En Journal: Value Health Journal subject: FARMACOLOGIA Year: 2023 Type: Article