{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Song Z"],"funding":["Natural Science Foundation of Fujian Province","Medical Innovation Project of Fujian Province","Basic scientific research projects of public welfare scientific research institutes in Fujian Province"],"pagination":["2868"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12369033"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["To create and verify a machine learning model that integrates social determinants of health (SDoH) for assessing post-stroke depression (PSD) and examining the association between SDoH and disease outcomes. Data were acquired from the National Health and Nutrition Examination Survey. Logistic regression was employed to analyse the association between SDoH and PSD, whereas Cox regression was utilized to assess the correlation between SDoH and all-cause mortality in PSD. The Boruta algorithm was employed for feature selection, and four machine learning models were constructed (CatBoost, Logistic, Multilayer Perceptron, and Random Forest) to evaluate the predictive effectiveness, calibration, and clinical applicability of these ML models. SHAP values were computed to ascertain the predictive "],"journal":["BMC public health"],"pubmed_title":["Machine learning and SHAP values explain the association between social determinants of health and post-stroke depression."],"pmcid":["PMC12369033"],"funding_grant_id":["2023J02024","2024R1012002","2022CXA047"],"pubmed_authors":["Li W","He Y","Weng J","Han Y","Wang Y","Song Z","Xu Y"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning and SHAP values explain the association between social determinants of health and post-stroke depression.","description":"To create and verify a machine learning model that integrates social determinants of health (SDoH) for assessing post-stroke depression (PSD) and examining the association between SDoH and disease outcomes. Data were acquired from the National Health and Nutrition Examination Survey. Logistic regression was employed to analyse the association between SDoH and PSD, whereas Cox regression was utilized to assess the correlation between SDoH and all-cause mortality in PSD. The Boruta algorithm was employed for feature selection, and four machine learning models were constructed (CatBoost, Logistic, Multilayer Perceptron, and Random Forest) to evaluate the predictive effectiveness, calibration, and clinical applicability of these ML models. SHAP values were computed to ascertain the predictive ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-05-07T05:05:24.56Z","creation":"2026-04-07T22:34:15.712Z"},"accession":"S-EPMC12369033","cross_references":{"pubmed":["40841950"],"doi":["10.1186/s12889-025-24220-y"]}}