Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation

Objectives: To assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone. Methods: A retrospective matched case-control stud...

Full description

Bibliographic Details
Main Authors: Zhou, Huaqiong, Albrecht, Matthew, Roberts, Pamela A., Porter, Paul, Della, Philip
Format: Journal Article
Language:English
Published: CSIRO PUBLISHING 2021
Subjects:
Science & Technology
Life Sciences & Biomedicine
Health Care Sciences & Services
Health Policy & Services
administrative data
clinical information
discharge planning
discharge summary
follow-up plan
machine learning
medical records
paediatric hospital readmissions
paediatric unplanned readmissions
retrospective analysis
social history
social predictors
written discharge documentation
Australia
Case-Control Studies
Child
Documentation
Humans
Machine Learning
Medical Records
Patient Discharge
Patient Readmission
Retrospective Studies
Risk Factors
Western Australia
Online Access:http://purl.org/au-research/grants/arc/LP140100563
http://hdl.handle.net/20.500.11937/90897

Internet

http://purl.org/au-research/grants/arc/LP140100563
http://hdl.handle.net/20.500.11937/90897

Similar Items