计算精神病学：抑郁症研究和临床应用的新视角

doi:10.3724/SP.J.1042.2020.00111

摘要/Abstract

摘要：

抑郁症是一种复杂而异质的精神疾病, 给全球带来沉重的疾病负担。尽管基于症状学的诊断方法已被广泛应用于各领域, 但这种方法并不利于病理机制的探讨。另外, 该诊断方法预测效度较低, 导致其难以准确评估和比较各种治疗方案的疗效。计算精神病学方法则能通过理论驱动和数据驱动两种互补的方法解决上述问题, 从而提高对抑郁症的认识、预防和治疗。理论驱动方法基于经验知识或假设, 利用计算建模方法对数据进行多水平分析; 数据驱动方法则基于机器学习算法分析高维数据, 提高抑郁症诊断和预测的准确性, 进而提高治疗的精准度。理论驱动和数据驱动方法的发展与结合, 以及人才和资源的整合, 将会更有效地推进抑郁症的防治。

关键词: 抑郁症, 计算精神病学, 计算模型, 机器学习, 诊断, 治疗

Abstract:

Depression, a complex and heterogeneous mental disorder, leads to great global burdens of disease. Although diagnosis based on nosology is broadly used in several domains, it is still unable to direct the exploration of pathological mechanism of depression. In addition, several treatments developed by this diagnosis have poor outcomes due to its low prediction validity. Computational approaches to psychiatry remedy those limitations and help to improve understanding, prediction and treatment for depression by two complementary approaches: data-driven and theory-driven. Theory-driven approaches apply models to multiple levels of analysis from the prior knowledge or hypojournal of depression. Data-driven approaches, however, adopt machine-learning methods to analyze high-dimensional data to improve the diagnostic and predictive accuracies of depression, and eventually, promote the treatment effects. With the development and combination of these two approaches as well as the integration of resources, it is promising to cure depression and prevent it from occurrence.

Key words: depression, computational psychiatry, computational models, machine learning, diagnosis, treatment

中图分类号:

R395

区健新, 吴寅, 刘金婷, 李红. (2020). 计算精神病学：抑郁症研究和临床应用的新视角. 心理科学进展 , 28(1), 111-127.

OU Jianxin, WU Yin, LIU Jinting, LI Hong. (2020). Computational psychiatry: A new perspective on research and clinical applications in depression. Advances in Psychological Science, 28(1), 111-127.

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