The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given.Ĭonclusions and Relevance Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). A subsequent step is the validation of the model to accurately test its generalizability (theme 2). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. Observations The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. Importance Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC).
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