Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study

• Main Authors: Yi, Seung Eun, Harish, Vinyas, Gutierrez, Jahir, Ravaut, Mathieu, Kornas, Kathy, Watson, Tristan, Poutanen, Tomi, Ghassemi, Marzyeh, Volkovs, Maksims, Rosella, Laura C.
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Administrative Health Data; Ambulatory Care
• Online Access: https://hdl.handle.net/10356/163265
• Institution: Nanyang Technological University

Objective: To predict older adults’ risk of avoidable hospitalisation related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada. Design, setting and participants: A retrospective cohort study was conducted on a large cohort...