Patient journey through cases of depression from claims database using machine learning algorithms.
Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification and prediction using the machine learning approach can be applied to them. However, the methodology used for the analysis of real-world data has been...
Main Authors: | Yoshitake Kitanishi, Masakazu Fujiwara, Bruce Binkowitz |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2021-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0247059 |
Similar Items
-
Machine learning algorithms for claims data‐based prediction of in‐hospital mortality in patients with heart failure
by: Sebastian König, et al.
Published: (2021-08-01) -
Validation of algorithms to identify osteoporotic hip fractures in the claim database
by: Young-Kyun Lee, et al.
Published: (2020-10-01) -
Burden of treatment-resistant depression in Medicare: A retrospective claims database analysis.
by: Dominic Pilon, et al.
Published: (2019-01-01) -
Correction: Burden of treatment-resistant depression in Medicare: A retrospective claims database analysis.
by: Dominic Pilon, et al.
Published: (2021-01-01) -
Characterization of treatment resistant depression episodes in a cohort of patients from a US commercial claims database.
by: Nicole Kubitz, et al.
Published: (2013-01-01)