Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes
© 2021, The Author(s). Machine learning has been suggested as a means of identifying individuals at greatest risk for hospital readmission, including psychiatric readmission. We sought to compare the performance of predictive models that use interpretable representations derived via topic modeling t...
Main Authors: | Boag, William, Kovaleva, Olga, McCoy, Thomas H, Rumshisky, Anna, Szolovits, Peter, Perlis, Roy H |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | English |
Published: |
Springer Science and Business Media LLC
2021
|
Online Access: | https://hdl.handle.net/1721.1/134104 |
Similar Items
-
Predicting early psychiatric readmission with natural language processing of narrative discharge summaries
by: Castro, V M, et al.
Published: (2017) -
Development and validation of a prediction rule for psychiatric hospital readmissions of patients with a diagnosis of psychosis
by: Vazquez Montes, M, et al.
Published: (2017) -
From hard to reach to how to reach: A systematic review of the literature on hard-to-reach families
by: Boag-Munroe, G, et al.
Published: (2012) -
From hard to reach to how to reach: A systematic review of the literature on hard-to-reach families
by: Evangelou, M, et al.
Published: (2010) -
Prescriptive analytics for reducing 30-day hospital readmissions after general surgery
by: Bertsimas, Dimitris J, et al.
Published: (2021)