ArticleFrontiers in public health2026
Development and temporal prospective validation of an interpretable machine learning model for identifying depressive symptoms in patients with acute exacerbation of chronic obstructive pulmonary disease.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To develop and temporally prospectively validate an interpretable model for identifying concurrent depressive symptoms in hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods: This study was conducted in two stages: retrospective development and prospective validation. In the development stage, 304 AECOPD patients admitted to the Fourth Affiliated Hospital of Anhui Medical University from September 2023 to February 2025 were retrospectively enrolled and randomly divided into a training set ( Results: LASSO regression screened four candidate predictors: physical function (PHYS), mental health (PSYCH), environmental support (ENVIR), and quality of life. The selected variables were entered into an unpenalized multivariable logistic regression model as the final clinical tool. The final logistic regression model achieved an Conclusion: A four-feature interpretable model was developed to identify AECOPD patients with concurrent depressive symptoms during hospitalization. The model demonstrated satisfactory discriminative performance and may assist early risk stratification; however, multicenter external validation, local recalibration, and decision-impact analysis are required before routine clinical implementation.
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