The promise of a model-based psychiatry: building computational models of mental ill health.
The promise of a model-based psychiatry: building computational models of mental ill health.
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DOI：
10.1016/s2589-7500(22)00152-2
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发表时间：
2022-11
影响因子：
30.8
通讯作者：
Koutsouleris, Nikolaos
中科院分区：
文献类型：
--
作者：
Hauser, Tobias U.;Skvortsova, Vasilisa;De Choudhury, Munmun;Koutsouleris, Nikolaos
计算模型具有巨大的潜力，可以彻底改变精神病学研究和临床实践。这些模型现在被用于多个子领域，包括计算精神病学和精确精神病学。他们的目标各不相同，从了解疾病的潜在机制到获得可靠的分类和个性化预测。新工具和数据源（例如，数字数据，游戏化和社交媒体）的快速增长需要了解精神病学中不同建模方法的限制和优势。在本系列论文中，我们对精神病学中使用的计算模型的范围进行了批判性的研究，并评估了它们在不同目的和数据来源下的优缺点。我们描述了机制驱动和机制不可知的计算模型，并讨论了如何解释模型是至关重要的临床翻译。基于这些评估，我们提供了关于如何建立临床上有用的计算模型的建议。
Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.