Identifying prospective frequent readmitters for hospital to home intervention using machine learning

Identifying prospective frequent readmitters for hospital to home intervention using machine learning

Introduction: Singapore is a developed country with a population of 5.6 million and an acute-centric healthcare system. While the acute hospitals deliver extensive care, patients with multiple care needs face challenges in managing their conditions post-discharge, due to various reasons, such as wea...

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Bibliographic Details
Main Authors: Andy Ta, Han Leong Goh, Chuen Seng Tan, Yan Sun, Khin Chaw Yu Aung, Zsin Woon Teoh, Kelvin Bryan Tan, Zheng Yi Lau, John Arputhan Abisheganaden, Kheng Hock Le, Sweet Fun Wong, Wai Leng Chow, Pranav Vinod Kumar, Zi Chao Choong, Cheryl Ng, Christine Ang, Mervin Chan, Daryl Lim, Cheng Ooi Low
Format: Article
Language:English
Published: Ubiquity Press 2019-08-01
Series:International Journal of Integrated Care
Subjects:
multiple readmission
electronic health record
machine learning
community-centric intervention
digital health
risk prediction
risk stratification
Online Access:https://www.ijic.org/articles/4977

Internet

https://www.ijic.org/articles/4977

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