Machine learning-guided intervention trials to predict treatment response at an individual patient level: an important second step following randomized clinical trials

Affiliations

¹ Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Laboratory of Molecular Psychiatry and Bipolar Disorder Program, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil. ivescp1@gmail.com.
² UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Comment

Ives Cavalcante Passos et al. Mol Psychiatry. 2020 Apr.

. 2020 Apr;25(4):701-702.

doi: 10.1038/s41380-018-0250-y.

¹ Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Laboratory of Molecular Psychiatry and Bipolar Disorder Program, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil. ivescp1@gmail.com.
² UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.

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Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity.
Cao B, Cho RY, Chen D, Xiu M, Wang L, Soares JC, Zhang XY. Cao B, et al. Mol Psychiatry. 2020 Apr;25(4):906-913. doi: 10.1038/s41380-018-0106-5. Epub 2018 Jun 19. Mol Psychiatry. 2020. PMID: 29921920

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