BMC gastroenterology · 2025
Leveraging machine learning for precision medicine: a predictive model for cognitive impairment in cholestasis patients.
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Fang Caixia, Zhang Lina, Xu Lanlan, He Yongsheng, Zhang Xuerong, Xing Xiaojuan
Аннотация
BACKGROUND: Cholestasis, characterized by impaired bile flow, impacts cognitive function through systemic mechanisms, including inflammation and metabolic dysregulation. Despite its significance, targeted predictive models for cognitive impairment in cholestasis remain underexplored. This study addresses this gap by developing a machine learning-based predictive model tailored to this population.
METHODS: Clinical and biochemical data from Qingyang People's Hospital (2021-2023) were used to train and validate models for predicting cognitive impairment (MoCA ≤ 17). Recursive feature elimination identified critical predictors, while LightGBM and other machine learning models were evaluated. SHAP analysis enhanced model interpretability, and clinical utility was assessed through decision curve analysis (DCA).
RESULTS: LightGBM outperformed other models with an AUC of 0.7955 on the testing dataset. Age, plasma D-dimer, and albumin were key predictors. SHAP analysis revealed non-linear interactions among features, demonstrating the model's clinical alignment. DCA confirmed its utility in improving patient stratification.
CONCLUSION: The developed LightGBM-based model effectively predicts cognitive impairment in cholestasis patients, providing actionable insights for early intervention. Integrating this tool into clinical workflows can enhance precision medicine and improve outcomes in this high-risk population.
METHODS: Clinical and biochemical data from Qingyang People's Hospital (2021-2023) were used to train and validate models for predicting cognitive impairment (MoCA ≤ 17). Recursive feature elimination identified critical predictors, while LightGBM and other machine learning models were evaluated. SHAP analysis enhanced model interpretability, and clinical utility was assessed through decision curve analysis (DCA).
RESULTS: LightGBM outperformed other models with an AUC of 0.7955 on the testing dataset. Age, plasma D-dimer, and albumin were key predictors. SHAP analysis revealed non-linear interactions among features, demonstrating the model's clinical alignment. DCA confirmed its utility in improving patient stratification.
CONCLUSION: The developed LightGBM-based model effectively predicts cognitive impairment in cholestasis patients, providing actionable insights for early intervention. Integrating this tool into clinical workflows can enhance precision medicine and improve outcomes in this high-risk population.