| Literature DB >> 32974930 |
Laleh G Melstrom1, Andrei S Rodin2, Lorenzo A Rossi3, Paul Fu4, Yuman Fong1, Virginia Sun1,5.
Abstract
In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine-learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.Entities:
Keywords: artificial intelligence; biometrics; machine learning in surgery; patient-reported outcomes; telehealth
Mesh:
Year: 2020 PMID: 32974930 PMCID: PMC7945992 DOI: 10.1002/jso.26232
Source DB: PubMed Journal: J Surg Oncol ISSN: 0022-4790 Impact factor: 3.454