论文标题
精确精神病学的机器学习现代视图
Modern Views of Machine Learning for Precision Psychiatry
论文作者
论文摘要
鉴于NIMH的研究领域标准（RDOC），功能性神经影像的出现，新颖的技术和方法为开发精确和个性化的预后和精神障碍的诊断提供了新的机会。机器学习（ML）和人工智能（AI）技术在精确精神病学的新时代起着日益关键的作用。将ML/AI与神经调节技术相结合，可以在临床实践和有效的治疗治疗中提供可解释的解决方案。先进的可穿戴和移动技术还要求ML/AI在移动心理健康中的数字表型中的新作用。在这篇综述中，我们通过在精神病学实践中结合神经影像学，神经调节和高级移动技术，对ML方法和应用进行全面综述。此外，我们回顾了ML在精神病学中ML在分子表型和跨物种生物标记鉴定中的作用。我们进一步以封闭的环境方式讨论了可解释的AI（XAI）和因果关系测试，并突出了多媒体信息提取和多模式数据融合中的ML潜力。最后，我们讨论了精神病学方面的概念和实践挑战，并在未来的研究中突出了ML机会。
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.
