Predicting Future High-Cost Schizophrenia Patients Using High-Dimensional Administrative Data
Bibliographic Data
| ID | 15518632 |
|---|---|
| Authors | Yajuan Wang (0000-0002-8830-7255, IBM (United States)), Vijay Iyengar (IBM Research - Thomas J. Watson Research Center), Jianying Hu (0000-0001-7753-886X, IBM Research - Thomas J. Watson Research Center), David Kho, Erin Falconer (corresponding author), John P Docherty (0000-0002-7400-8326), Gigi Y Yuen (IBM (United States)) |
| Year | 2017 |
| Volume | 8 |
| Pages | 114-114 |
| Publication date | 2017-06-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2017.00114 |
| PMID | 28713293 |
| OpenAlex | W2726866717 |
| Language | EN |
| References cited | 12 |
Using advanced feature selection leveraging detailed health care, medication utilization features, and supervised machine learning methods improved the ability to predict and identify future high-cost patients with schizophrenia when compared with the CMS-HCC model
Cohort · Diagnosis of schizophrenia · Health care · Machine learning · Medicaid · Psychiatry · Psychosis · Random forest · Receiver operating characteristic · Regression analysis · Schizophrenia (object-oriented programming · Chronic Disease Management Strategies · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Medicine · Schizophrenia research and treatment · Internal Medicine
| Citation velocity | historical |
|---|---|
| Highly cited | No |