Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Germany; and Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; and Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK.
Br J Psychiatry. 2022 Apr;220(4):175-178. doi: 10.1192/bjp.2022.23.
Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.
在风险评估、诊断和精准处方等领域，大量基于概念验证的机器学习研究为开发精神障碍的个性化护理提供了支持。这些研究大多主要使用临床数据，但模型可能受益于额外的神经影像学、血液和遗传数据，以提高准确性。综合使用多模态模型可能为治疗患者的分层提供潜力。机器学习的临床应用受到缺乏更广泛的通用性的阻碍，主要集中在精神病和痴呆症上。所有诊断组的研究都应努力测试机器学习模型的稳健性，这是临床实施的必要的第一步，然后再进行前瞻性临床验证。模型需要超越临床医生的启发式方法，才能在常规决策中有用且安全。临床医生、研究人员和患者参与数字化和“大数据”方法对于生成和获取大规模、纵向、前瞻性数据至关重要，这些数据是将精准精神病学应用于现实世界精神保健所需的。
