{"title":"Application of machine learning for the diagnosis and prognosis of sepsis-induced acute respiratory distress syndrome: a systematic review and meta-analysis.","authors":"Mingcheng Dai, Ruo Wu, Kangshuai Zhou, Zhangling Xu, Yifan Shao, Wenzhen Zhou, Dian Zhang, Mingquan Chen","doi":"10.1186/s12911-026-03356-w","DOIUrl":"https://doi.org/10.1186/s12911-026-03356-w","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Explainable machine learning for depression risk prediction in adults with obesity: development of an online tool.","authors":"Yong Xie, YuJia Huo, Chunyu Zhang, Jinyu He, Jian Feng","doi":"10.1186/s12911-026-03359-7","DOIUrl":"https://doi.org/10.1186/s12911-026-03359-7","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-07DOI: 10.1186/s12911-026-03349-9
Kunal Kumar, Muhammad Ashad Kabir, Luke Donnan, Sayed Ahmed
{"title":"A narrative review of clinical decision support systems in offloading footwear for diabetic foot ulcers.","authors":"Kunal Kumar, Muhammad Ashad Kabir, Luke Donnan, Sayed Ahmed","doi":"10.1186/s12911-026-03349-9","DOIUrl":"https://doi.org/10.1186/s12911-026-03349-9","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Sepsis-Induced coagulopathy (SIC) is not only a common complication in the development process of sepsis but also related to poor prognosis of sepsis. We aimed to establish a machine learning (ML) model to predict the 28-day mortality risk of patients with SIC.
Methods: We collected data for model training from the Medical Information Mart for Intensive Care IV Database version 2.2 to establish the model. We extracted patient data from the First Affiliated Hospital of Wenzhou Medical University for the model's external validation. We used Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression analysis to identify predictive factors for a 28-day mortality risk. Then, we built prognostic prediction models for SIC patients using multiple ML classification models. We evaluated predictive performance using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). We used Shapley Additive Explanations (SHAP) to interpret the models.
Results: We selected seventeen variables for model development, and the XGBoost model performed the best. The area under the curve (AUC) (95% CI) of the test set reached 0.840 (0.810-0.870), with an accuracy of 0.807, sensitivity of 0.836, and specificity of 0.798. The model also demonstrated excellent predictive performance in external validation, with an AUC (95% CI) of 0.864 (0.794-0.934).
Conclusion: We constructed an XGBoost model and provided model interpretability using the SHAP. This model provides a basis for assessing the 28-day mortality risk of patients with SIC, aiding in clinical decision support and the formulation of personalized treatment strategies.
{"title":"Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy.","authors":"Jinmei Wu, Xianwei Zhang, Chenglong Liang, Baoxin Wang, Xiangyuan Ruan, Yihua Dong, Xueyang Xu, Jingye Pan","doi":"10.1186/s12911-026-03363-x","DOIUrl":"https://doi.org/10.1186/s12911-026-03363-x","url":null,"abstract":"<p><strong>Background: </strong>Sepsis-Induced coagulopathy (SIC) is not only a common complication in the development process of sepsis but also related to poor prognosis of sepsis. We aimed to establish a machine learning (ML) model to predict the 28-day mortality risk of patients with SIC.</p><p><strong>Methods: </strong>We collected data for model training from the Medical Information Mart for Intensive Care IV Database version 2.2 to establish the model. We extracted patient data from the First Affiliated Hospital of Wenzhou Medical University for the model's external validation. We used Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression analysis to identify predictive factors for a 28-day mortality risk. Then, we built prognostic prediction models for SIC patients using multiple ML classification models. We evaluated predictive performance using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). We used Shapley Additive Explanations (SHAP) to interpret the models.</p><p><strong>Results: </strong>We selected seventeen variables for model development, and the XGBoost model performed the best. The area under the curve (AUC) (95% CI) of the test set reached 0.840 (0.810-0.870), with an accuracy of 0.807, sensitivity of 0.836, and specificity of 0.798. The model also demonstrated excellent predictive performance in external validation, with an AUC (95% CI) of 0.864 (0.794-0.934).</p><p><strong>Conclusion: </strong>We constructed an XGBoost model and provided model interpretability using the SHAP. This model provides a basis for assessing the 28-day mortality risk of patients with SIC, aiding in clinical decision support and the formulation of personalized treatment strategies.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146118006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-04DOI: 10.1186/s12911-026-03368-6
Jahanpour Alipour, Abolfazl Payandeh, Mohammad Hosein Hayavi-Haghighi
{"title":"Factors associated with clinical coders' intention to use the international classification of diseases 11th revision (ICD-11): a cross-sectional study in Iran.","authors":"Jahanpour Alipour, Abolfazl Payandeh, Mohammad Hosein Hayavi-Haghighi","doi":"10.1186/s12911-026-03368-6","DOIUrl":"https://doi.org/10.1186/s12911-026-03368-6","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146117985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Establishing a prediction model for the therapeutic outcomes of short-term and long-term orthokeratology treatment: using baseline data and changes in AL as dynamic variables.","authors":"Zixun Wang, Xiaoxue Hu, Feng Chang, Xiaoling Zhang, Boxuan Sun, Rui Li, Weiping Lin, Ruihua Wei","doi":"10.1186/s12911-026-03339-x","DOIUrl":"https://doi.org/10.1186/s12911-026-03339-x","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Interpretable machine learning based decision-making system for lung adenocarcinoma metastasis: a population-based study with exploration of multi-classification models.","authors":"Jian Xu, Shuo Chen, Chang Zhao, Miao He, Feng Luo, Xintian Cai, Jiantao Wang, Zhendong Ding, TieWa Zhang","doi":"10.1186/s12911-026-03341-3","DOIUrl":"https://doi.org/10.1186/s12911-026-03341-3","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1186/s12911-026-03344-0
V S Athukorala, W M K S Ilmini
{"title":"Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction.","authors":"V S Athukorala, W M K S Ilmini","doi":"10.1186/s12911-026-03344-0","DOIUrl":"https://doi.org/10.1186/s12911-026-03344-0","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Textbook-level medical knowledge in large language models: comparative evaluation using Japanese National Medical Examination.","authors":"Mingxin Liu, Tsuyoshi Okuhara, Zhehao Dai, Minghong Zhao, Wenqiang Yin, Hiroko Okada, Emi Furukawa, Takahiro Kiuchi","doi":"10.1186/s12911-026-03370-y","DOIUrl":"https://doi.org/10.1186/s12911-026-03370-y","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Construction and validation of a predictive model for benefits of tunnel-type mediastinal lymph node dissection in lung cancer patients based on the SEER database.","authors":"Weijie Deng, Shili Ding, Zhenxing Cai, Zhimin Zheng","doi":"10.1186/s12911-026-03347-x","DOIUrl":"https://doi.org/10.1186/s12911-026-03347-x","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146104041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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