ArticleTranslational psychiatry2023
Development and validation of a machine learning model using electronic health records to predict trauma- and stressor-related psychiatric disorders after hospitalization with sepsis.
Article in Translational psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- The broad structure of psychopathology in the All of Us Research Program.Psychological medicine · 2025Article
- Neuropsychiatric symptoms in the context of hemodynamic disruption during septic shock.World journal of psychiatry · 2025Article
- A systematic review of machine learning findings in PTSD and their relationships with theoretical models.Nature. Mental health · 2025Article
- Towards accurate screening and prevention for PTSD (2-ASAP): protocol of a longitudinal prospective cohort study.BMC psychiatry · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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Abstract
A significant minority of individuals develop trauma- and stressor-related disorders (TSRD) after surviving sepsis, a life-threatening immune response to infections. Accurate prediction of risk for TSRD can facilitate targeted early intervention strategies, but many existing models rely on research measures that are impractical to incorporate to standard emergency department workflows. To increase the feasibility of implementation, we developed models that predict TSRD in the year after survival from sepsis using only electronic health records from the hospitalization (n = 217,122 hospitalizations from 2012-2015). The optimal model was evaluated in a temporally independent prospective test sample (n = 128,783 hospitalizations from 2016-2017), where patients in the highest-risk decile accounted for nearly one-third of TSRD cases. Our approach demonstrates that risk for TSRD after sepsis can be stratified without additional assessment burden on clinicians and patients, which increases the likelihood of model implementation in hospital settings.
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