Precision psychiatry is an ermerging field that aims to provide
individualized approaches to mental health care. Multivariate analysis and
machine learning are used to create outcome prediction models based on clinical
data such as demographics, symptom assessments, genetic information, and brain
imaging. While much emphasis has been placed on technical innovation, the
complex and varied nature of mental health presents significant challenges to
the successful implementation of these models. From this perspective, I review
ten challenges in the field of precision psychiatry, including the need for
studies on real-world populations and realistic clinical outcome definitions,
consideration of treatment-related factors such as placebo effects and
non-adherence to prescriptions. Fairness, prospective validation in comparison
to current practice and implementation studies of prediction models are other
key issues that are currently understudied. A shift is proposed from
retrospective studies based on linear and static concepts of disease towards
prospective research that considers the importance of contextual factors and
the dynamic and complex nature of mental health.

精准精神病学是一种个体化的心理保健方法，其中包括多元分析和机器学习等技术，该论文重点探讨了精准精神病学面临的十项挑战，包括临床结果定义、安慰剂效应和预测模型实施的公平性等问题，提出了从静态概念转向考虑精神健康动态和相关因素的前瞻性研究。

精准精神病学：预测可预测性

Precision psychiatry: predicting predictability

In light of the NIMH's Research Domain Criteria (RDoC), the advent of
functional neuroimaging, novel technologies and methods provide new
opportunities to develop precise and personalized prognosis and diagnosis of
mental disorders. Machine learning (ML) and artificial intelligence (AI)
technologies are playing an increasingly critical role in the new era of
precision psychiatry. Combining ML/AI with neuromodulation technologies can
potentially provide explainable solutions in clinical practice and effective
therapeutic treatment. Advanced wearable and mobile technologies also call for
the new role of ML/AI for digital phenotyping in mobile mental health. In this
review, we provide a comprehensive review of the ML methodologies and
applications by combining neuroimaging, neuromodulation, and advanced mobile
technologies in psychiatry practice. Additionally, we review the role of ML in
molecular phenotyping and cross-species biomarker identification in precision
psychiatry. We further discuss explainable AI (XAI) and causality testing in a
closed-human-in-the-loop manner, and highlight the ML potential in multimedia
information extraction and multimodal data fusion. Finally, we discuss
conceptual and practical challenges in precision psychiatry and highlight ML
opportunities in future research.

通过将机器学习和神经调节技术与先进的可穿戴和移动技术相结合，提供了广泛的机会来在精确精神病学中提供解释性方案和有效的治疗方法。