Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Aachen, Germany; Parietal team, INRIA, Neurospin, Gif-sur-Yvette, France.
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Mar;3(3):223-230. doi: 10.1016/j.bpsc.2017.11.007. Epub 2017 Dec 6.
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.
精神疾病的本质仍然是一个谜。传统的疾病类别越来越被怀疑不能代表精神障碍的根本原因。然而，精神病学家和研究人员现在有机会利用机器学习（例如支持向量机、现代神经网络算法、交叉验证程序）从大脑、行为和基因中的复杂模式中受益。将这些分析技术与来自财团和存储库的大量数据相结合，有可能对主要精神障碍进行基于生物学的重新定义。越来越多的证据表明，与 DSM/ICD 诊断相比，基于数据的精神科患者亚组可以更好地预测治疗结果。在针对个体患者的循证精神病学新时代，客观可衡量的内表型可以实现早期疾病检测、个体化治疗选择和剂量调整，从而减轻疾病负担。本入门指南旨在向临床医生和研究人员介绍将机器智能引入精神科实践的机会和挑战。
