Machine learning for precision psychiatry: opportunities and challenges
The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit from complex patterns in brain, behavior, and gen...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2018
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| In: |
Biological psychiatry. Cognitive neuroscience and neuroimaging
Year: 2017, Jahrgang: 3, Heft: 3, Pages: 223-230 |
| ISSN: | 2451-9030 |
| DOI: | 10.1016/j.bpsc.2017.11.007 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.bpsc.2017.11.007 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S2451902217302069 
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| Verfasserangaben: | Danilo Bzdok and Andreas Meyer-Lindenberg |
MARC
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| 520 | |a The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit from complex patterns in brain, behavior, and genes using methods from machine learning (e.g., support vector machines, modern neural-network algorithms, cross-validation procedures). Combining these analysis techniques with a wealth of data from consortia and repositories has the potential to advance a biologically grounded redefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroups of psychiatric patients can better predict treatment outcomes than DSM/ICD diagnoses can. In a new era of evidence-based psychiatry tailored to single patients, objectively measurable endophenotypes could allow for early disease detection, individualized treatment selection, and dosage adjustment to reduce the burden of disease. This primer aims to introduce clinicians and researchers to the opportunities and challenges in bringing machine intelligence into psychiatric practice. | ||
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