Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank
Machine learning (ML) holds promise for precision psychiatry, but its predictive performance is unclear. We assessed whether ML provided added value over logistic regression for prediction of schizophrenia, and compared models built using polygenic risk scores (PRS) or clinical/demographic factors.
Gespeichert in:
| Autor*in: |
Bracher-Smith, Matthew [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
|---|
| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Idiopathic Environmental Intolerance: A Treatment Model - Van den Bergh, Omer ELSEVIER, 2021, an international multidisciplinary journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:246 ; year:2022 ; pages:156-164 ; extent:9 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.schres.2022.06.006 |
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Machine learning (ML) holds promise for precision psychiatry, but its predictive performance is unclear. We assessed whether ML provided added value over logistic regression for prediction of schizophrenia, and compared models built using polygenic risk scores (PRS) or clinical/demographic factors. |
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Machine learning (ML) holds promise for precision psychiatry, but its predictive performance is unclear. We assessed whether ML provided added value over logistic regression for prediction of schizophrenia, and compared models built using polygenic risk scores (PRS) or clinical/demographic factors. |
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