BMC Medical Informatics and Decision Making最新文献

{"title":"Multitask learning of longitudinal circulating biomarkers and clinical outcomes: identification of optimal machine-learning and deep-learning models.","authors":"Min Yuan, Shixin Su, Haolun Ding, Yaning Yang, Manish Gupta, Xu Steven Xu","doi":"10.1186/s12911-026-03671-2","DOIUrl":"10.1186/s12911-026-03671-2","url":null,"abstract":"<p><strong>Background: </strong>Many circulating biomarkers are assessed at different time intervals during clinical studies. Despite of the success of standard joint models in predicting clinical outcomes using low-dimensional longitudinal data (1-2 biomarkers), significant computational challenges are encountered when applying these techniques to high-dimensional biomarker datasets. Modern machine- or deep-learning models show potential for multiple biomarker processes, but systematic evaluations and applications to high-dimensional data in the clinical settings have yet to be reported. We aimed to enhance the scalability of joint modeling and provide guidance on optimal approaches for high-dimensional biomarker data and outcomes.</p><p><strong>Methods: </strong>We evaluated multiple deep-learning and machine-learning models using 24 clinical biomarkers and survival data from the SQUIRE trial, a phase 3 randomized clinical trial investigating necitumumab and standard gemcitabine/cisplatin treatment in patients with squamous non-small-cell lung cancer (NSCLC).</p><p><strong>Results: </strong>Overall, we confirmed that longitudinal models enabled more accurate prediction of patients' survival compared to those solely based on baseline information. Coupling multivariate functional principal component analysis (MFPCA) with Cox regression (MFPCA-Cox) provided the highest predictive discrimination and accuracy for the NSCLC patients with AUC values of 0.7 - >0.8 at various landmark time points and prediction timeframes, outperforming recent advanced Transformer and convolutional neural network deep-learning algorithms (TransformerJM and Match-Net, respectively).</p><p><strong>Conclusions: </strong>In conclusion, we identified that MFPCA-Cox represents a robust and versatile joint modeling algorithm for high-dimensional biomarker longitudinal data with irregular and missing data, capturing complex relationships within the data, yielding accurate predictions for both longitudinal biomarkers and survival outcomes, and gaining insights into the underlying dynamics.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov (NCT00981058; first posted on September 22, 2009).</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13584259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148359684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.","authors":"Renzhi Tan, Yongchen Li, Diman Mai, Jingwen Liu, Yushuang Wei, Chao Wang, Zengnan Mo","doi":"10.1186/s12911-026-03620-z","DOIUrl":"10.1186/s12911-026-03620-z","url":null,"abstract":"<p><strong>Background: </strong>Sepsis-associated acute kidney injury (SA-AKI) is a common and severe complication in intensive care unit (ICU) patients, with high incidence and mortality. Conventional diagnostic indicators, such as serum creatinine and urine output, have limitations in early identification. Existing machine learning (ML)-based prediction models are mostly single-center and lack interpretability, highlighting the clinical need for reliable and applicable early prediction tools.</p><p><strong>Methods: </strong>Data were collected from the Medical Information Mart for Intensive Care IV (MIMIC-IV), eICU Collaborative Research Database (eICU-CRD), and Bobai County People's Hospital ICU (BB-ICU). Eligible patients were those meeting the Sepsis-3.0 criteria, with SA-AKI as the primary outcome. Ten core variables were selected from 83 indicators measured within 24 h of ICU admission through a four-step screening process. Six ML algorithms were optimized using grid search and five-fold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The optimal model was interpreted via SHAP, externally validated, and translated into a web-based calculator using the Shiny framework.</p><p><strong>Results: </strong>The incidence of SA-AKI was 45.1% in MIMIC-IV, 28.1% in eICU-CRD, and 34.1% in BB-ICU. The XGBoost model performed best, with an internal area under the curve (AUC) of 0.72 and external validation AUCs of 0.82 (eICU-CRD) and 0.84 (BB-ICU). The XGBoost model also showed balanced classification metrics (sensitivity 0.73, specificity 0.60), a calibration curve close to the diagonal, and the highest net benefit in DCA. SHAP analysis identified partial thromboplastin time (PTT), creatinine, 24-hour urine output, and SAPSII as the key predictive factors.</p><p><strong>Conclusions: </strong>The ML model developed in this study demonstrates good discriminative ability, calibration, and clinical applicability. SHAP improves the model's interpretability, and the web-based tool supports early SA-AKI risk stratification. This study provides a technical basis for addressing the clinical challenge of early SA-AKI prediction.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13587423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148359695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trajectory analysis of sleep disorders and anxiety-depression in female breast cancer patients undergoing chemotherapy: based on group-based Multi-Trajectory Model and machine learning.","authors":"Jiang Yuan, Yinqing Yang, Yuan Li, Tumalisi Kayishaer, Jie Chu, Bingmei Zhu, Zhizhen Tao, Jinshu Han, Xinyu Zhao, Xiuying Hu, Chao Tian, Yi Yang","doi":"10.1186/s12911-026-03672-1","DOIUrl":"10.1186/s12911-026-03672-1","url":null,"abstract":"<p><strong>Background: </strong>Sleep disorders and anxiety-depression symptoms can significantly impair the quality of life and treatment adherence among breast cancer patients undergoing chemotherapy, while also increasing the risk of disease recurrence and mortality. Early identification of the progression trajectory and underlying causes of these symptoms is essential for providing personalized interventions for deteriorating cases.</p><p><strong>Objective: </strong>This study aims to analyze the trajectories of sleep disorders and anxiety-depression symptoms in breast cancer patients during chemotherapy using a group-based Multi-Trajectory Model (GBMTM) and to identify influencing factors through machine learning.</p><p><strong>Methods: </strong>Prospective data from 150 breast cancer patients undergoing four cycles of chemotherapy were collected. A GBMTM was employed to identify distinct trajectories, with 70 predictive variables included in the analysis. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the optimal subset of variables. Seven machine learning models were compared, and the optimal model was chosen for result interpretation using SHAP (SHapley Additive exPlanations) analysis.</p><p><strong>Results: </strong>Among breast cancer patients undergoing chemotherapy, two distinct trajectories of sleep disorders and anxiety-depression symptoms were identified: the \"stable group\" (74.7%) and the \"deteriorating group\" (25.3%). SHAP analysis revealed that high levels of C-reactive protein, high uric acid, high hemoglobin, smoking, presence of invasive cancer with ductal carcinoma in situ, prior chemotherapy, being married, regular menstrual cycles in the past, and lack of family history were significant risk factors associated with the \"deteriorating group.\"</p><p><strong>Conclusion: </strong>Our findings indicate two distinct trajectories of sleep disorders and anxiety-depression symptoms in breast cancer patients undergoing chemotherapy, with the majority falling into the \"stable group\". The identified risk factors associated with the \"deteriorating group\" should be closely monitored to facilitate early intervention and improve patient outcomes.</p><p><strong>Trial registration: </strong>The trial was registered on Chinese Clinical Trial Registry (Trial ID: ChiCTR2300077667 on 15 November 2023; available at https://www.chictr.org.cn/ ).</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13587310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148359284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning analysis of 12-lead electrocardiograms for bloodstream infection prediction: a multi-center validation study.","authors":"Po-Huang Chen, Shang-Yang Li, Dung-Jang Tsai, Yan-Ru Wang, Shiue-Wei Lai, Ping-Ying Chang, Jia-Hong Chen, Ming-Shen Dai, Chin-Sheng Lin, Chin Lin, Cho-Hao Lee","doi":"10.1186/s12911-026-03657-0","DOIUrl":"10.1186/s12911-026-03657-0","url":null,"abstract":"<p><strong>Background: </strong>Prompt diagnosis of bloodstream infections (BSIs) is critical for antimicrobial stewardship but hindered by blood culture delays of 48 h or more. We developed and validated a deep learning model to predict BSI risk from standard 12-lead electrocardiograms (ECGs).</p><p><strong>Methods: </strong>This study adhered to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines. In this retrospective, multi-center study, we trained a deep learning model to predict clinically significant BSIs (excluding common contaminants) using ECGs from adult emergency department (ED) patients at an academic medical center in Taiwan (April 2012 to March 2022; total N = 163,826) and validated it in three independent cohorts: internal (n = 22,205), external (n = 22,709), and MIMIC-IV (n = 66,937). For the MIMIC-IV cohort, BSI was identified using the derived suspicion of infection framework with a positive blood culture flag.</p><p><strong>Results: </strong>The model achieved AUCs of 0.821 (95% CI 0.804-0.838), 0.800 (0.782-0.818), and 0.756 (0.743-0.768) across the three test cohorts, respectively. At a high-sensitivity rule-out threshold the model classified 19-25% of patients as low risk (negative predictive value [NPV] 99.8%); at the Youden point, the sensitivity and specificity were 73.2% and 76.8% in the internal test set, with an NPV of 99.2%. The model showed superior discrimination compared with XGBoost and logistic regression baselines. Decision-curve analysis showed net benefit over heart rate alone and the default treat-all and treat-none strategies across the clinically relevant low-threshold range. Exploratory analyses indicated that the ECG-derived risk signal was elevated across Gram-positive, Gram-negative, and fungal infections; pathogen-specific prediction will require a dedicated model (Supplementary Figure S3). The primary model stratified 90-day mortality risk among infected patients (adjusted HR 3.43 [1.93-6.09] for high- vs. low-risk).</p><p><strong>Conclusions: </strong>Deep learning analysis of standard 12-lead ECGs obtained at ED triage can predict clinically significant BSI with discrimination superior to conventional ECG parameters. The model discriminated best (highest AUC) in patients with low clinical suspicion (qSOFA = 0), although its incremental value in this low-prevalence subgroup is modest. Prospective validation is needed before clinical implementation.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13579753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148350746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrospective external validation of the Mayo Delirium Prediction tool in a Swiss cohort of medical and surgical inpatients.","authors":"Michela Venturini, Nayeli Schmutz Gelsomino, Sandeep R Pagali, Martin Zozman, Felix Buddeberg, Mary-Anne Kedda, Marius Möller, Simone Pascale Wildhaber, Reto Stocker, Benjamin T Dodsworth","doi":"10.1186/s12911-026-03660-5","DOIUrl":"10.1186/s12911-026-03660-5","url":null,"abstract":"<p><strong>Background: </strong>Delirium is a frequent complication in hospitalised patients, associated with long-term cognitive impairment, prolonged hospital stays, and increased mortality. Early risk assessment is essential for implementing preventive strategies. The Mayo Delirium Prediction (MDP) tool, developed using data from a large academic hospital in the United States, includes separate models for medical and surgical patients. The MDP is available in its original and recalibrated versions. However, its performance in non-US population remains unknown. The objective of this study is to externally validate the MDP tool in a cohort of hospitalised patients from a Swiss private hospital.</p><p><strong>Methods: </strong>This retrospective validation study used routinely collected clinical data from adult medical and surgical inpatients admitted to a Swiss hospital in May and June 2023. Delirium diagnosis was based on the Delirium Observation Screening Scale (DOSS). Predictive performance was assessed using the Area Under the Receiver Operating Characteristic curve (AUROC) and calibration plots. Additional sensitivity analyses were performed to assess the model's robustness and mitigate possible biases in the evaluation.</p><p><strong>Results: </strong>The original medical MDP tool achieved the best predictive performance in the external validation cohort of 947 patients, with an AUROC of 0·87 (95% CI: 0·85-0·90). The original surgical MDP tool showed relatively lower performance in the external validation cohort of 1212 patients, with an AUROC of 0·73 (95% CI: 0·69-0·78). Performance remained acceptable across all sensitivity analyses, with only a modest reduction in AUROC. The original MDP tool consistently outperformed recalibrated versions across all scenarios.</p><p><strong>Conclusions: </strong>The MDP tool, particularly the medical model, maintained strong predictive performance in an independent setting. These findings support its generalisability and potential integration into routine clinical workflows for delirium prevention.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13579724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148350761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable machine learning model integrating peripheral blood lncRNAs and clinical variables for phase classification in chronic myeloid leukemia.","authors":"Yuanyuan Bai, Mengting Zheng, Hongshuang Li, Chenlei Song, Bingxin Huang, Meiyun Cai, Zhanguo Chen","doi":"10.1186/s12911-026-03656-1","DOIUrl":"10.1186/s12911-026-03656-1","url":null,"abstract":"<p><strong>Background: </strong>Accurate disease phase assessment remains clinically important in chronic myeloid leukemia (CML), as progression to accelerated or blast phase (AP/BP) is associated with therapeutic resistance and poor prognosis. We aimed to develop an interpretable machine learning (ML) framework integrating peripheral blood long non-coding RNA (lncRNA) expression and clinical variables for disease phase assessment in CML.</p><p><strong>Methods: </strong>Using peripheral blood from 305 treatment-naïve CML patients (85 AP/BP; 220 chronic phase) and 90 healthy controls, we identified a progression-associated ten-lncRNA signature via PCR array and real-time quantitative PCR (RT-qPCR) validation. Feature selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression yielded eight key predictors. Five ML models were trained (n = 214), validated in an independent holdout test cohort (n = 91), and evaluated using the area under the ROC curve (AUC), calibration, and decision curve analysis. Interpretability was achieved using SHapley Additive exPlanations (SHAP).</p><p><strong>Results: </strong>The final model integrated three lncRNAs (CCDC26, SNHG5, FENDRR) and five clinical variables. Among the evaluated algorithms, XGBoost demonstrated the most favorable overall performance, achieving AUCs of 0.9658 and 0.9656 in the training and independent holdout test cohorts, respectively.</p><p><strong>Conclusions: </strong>We developed an interpretable ML framework integrating peripheral blood lncRNAs and clinical variables to support disease phase assessment in CML. The proposed model demonstrated favorable performance within this single-center cohort and may provide complementary information for clinical evaluation. Further multicenter and longitudinal studies are required before broader clinical application.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13536685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148350782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of large language models in mitral valve surgery patient education: a comparative analysis.","authors":"Banu Bahriye Akdag, Mehmet Senel Bademci, Ihsan Peker, Guven Okay Karaca, Cagrı Kandemir, Barcin Özcem, Huseyin Durmaz, Meryem Cakır, Irem Ozcetin, Hidayet Onur Selcuk","doi":"10.1186/s12911-026-03662-3","DOIUrl":"10.1186/s12911-026-03662-3","url":null,"abstract":"<p><strong>Background: </strong>Large language models (LLMs), a form of artificial intelligence, are increasingly being utilized in healthcare to support patient education and information delivery. The aim of this study was to perform a comparative analysis of five different LLMs (i.e., ChatGPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Pro Preview, DeepSeek-V3, and Microsoft Copilot) in terms of accuracy, completeness, and readability, based on their responses to frequently asked questions in preoperative patient education for mitral valve surgery (MVS).</p><p><strong>Methods: </strong>A standardized questionnaire comprising seven frequently asked questions by patients prior to MVS was developed. Prompting procedures and model parameters were fully reported to support reproducibility. These questions were presented to each LLM in an identical manner. The responses were evaluated by two academic experts in cardiac surgery using structured assessment criteria across three main dimensions: accuracy, completeness, and readability. For the readability analysis, the Simplified Measure of Gobbledygook (SMOG) Index and the Flesch-Kincaid Grade Level (FKGL) scale were utilized.</p><p><strong>Results: </strong>The ChatGPT-4o and Gemini 2.5 Pro Preview models received statistically significantly higher scores than Claude 3.7 Sonnet and Microsoft Copilot for both accuracy (median 5 for ChatGPT-4o and Gemini 2.5 Pro Preview vs. 4 for Claude 3.7 Sonnet and Microsoft Copilot, p < 0.001) and completeness (median 5 for Gemini 2.5 Pro Preview vs. 3 for Claude 3.7 Sonnet, p < 0.001). Claude 3.7 Sonnet achieved the highest readability scores, with significantly lower SMOG (10.90 for Claude 3.7 Sonnet vs. 12.24 for ChatGPT-4o, p = 0.006) and FKGL (8.0 for Claude 3.7 Sonnet vs. 9.04 for ChatGPT-4o, p = 0.004) scores, indicating simpler and more comprehensible sentence structures. Significant differences were observed among the evaluated models across all three assessment dimensions (p < 0.001 for all comparisons).</p><p><strong>Conclusions: </strong>The LLMs represent valuable supplementary tools in patient education processes. However, their implementation in clinical practice must be carefully evaluated, particularly with regard to accuracy and completeness. This study highlights the potential applicability of ChatGPT-4o and Claude 3.7 Sonnet models for preoperative patient education in MVS, while emphasizing that all LLMs should be used under the supervision and guidance of healthcare professionals. For LLMs to be reliably utilized in the medical field, improvement in medical accuracy and standardization are essential.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13579825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148350725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical subphenotypes in septic patients with new-onset atrial fibrillation: validation and parsimonious classifier model development.","authors":"Lin Zhang, Jiangjing Li, Xiaolin Shang, Yongfei Liu, Zhiyang Chen, Hongxia Xiang, Heming Zhang","doi":"10.1186/s12911-026-03652-5","DOIUrl":"10.1186/s12911-026-03652-5","url":null,"abstract":"<p><strong>Background: </strong>The substantial heterogeneity among septic patients with new‑onset atrial fibrillation (NOAF) complicates intensive care unit (ICU) management, and the absence of a parsimonious subphenotype classifier has impeded the implementation of personalized therapeutic strategies. In this study, we aim to identify the subphenotype of septic patients with NOAF, develop a concise classifier model, and reveal the differential efficacy of heart-rate control therapies across these subphenotypes.</p><p><strong>Methods: </strong>This retrospective study utilized data from the Medical Information Mart in Intensive Care (MIMIC)-IV and MIMIC-III database. Consensus clustering based on hierarchical clustering was employed for subphenotype derivation. Nine supervised classifiers were employed to construct the subphenotype classifier, including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), partial least squares (PLS), neural network (NN), naïve Bayes (NB), linear discriminant analysis (LDA), least absolute shrinkage and selection operator (LASSO), and adaptive boosting (AdaBoost.M1). Subphenotype-specific propensity score-derived stabilized inverse probability of treatment weights was used to weight comparisons of intensive care unit and hospital lengths of stay and discharge dispositions across subphenotypes.</p><p><strong>Results: </strong>Among 1535 septic patients with NOAF, three distinct subphenotypes emerged: Phenotype A was the mildest subgroup; Phenotype B exhibited pronounced metabolic acidosis, highest anion gap, severe renal impairment, and elevated severity scores; Phenotype C showed intermediate laboratory values and score profiles with notable hyperchloremia and inflammatory markers. A parsimonious model established by SVM showed the best effectiveness. HR-control therapy was associated with subphenotype-specific reductions in hospital and ICU lengths of stay and more favorable discharge dispositions in Phenotype A, while more favorable survival outcomes were observed across all three subphenotypes in IPTW-weighted analyses.</p><p><strong>Conclusion: </strong>Cluster analysis revealed three clinically relevant subphenotypes among septic patients with NOAF, each demonstrating distinct clinical outcomes and heterogeneous responses to heart rate control therapy.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576258/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148350835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and external validation of an interpretable machine learning model for predicting prolonged postoperative ICU length of stay in coronary artery bypass grafting patients using MIMIC-IV 3.1 and eICU-CRD 2.0.","authors":"Dayan Liu, Pengyu Lu, Yulan Meng, Xianglong Liu, Wei Huang","doi":"10.1186/s12911-026-03659-y","DOIUrl":"10.1186/s12911-026-03659-y","url":null,"abstract":"<p><strong>Background: </strong>Prolonged postoperative intensive care unit (ICU) length of stay (LOS) after coronary artery bypass grafting (CABG) drives resource use yet remains difficult to anticipate at the 24-hour ICU evaluation moment when extended monitoring beyond 72 h is first considered. We developed and externally validated an interpretable machine-learning decision-support calculator for this deployment moment.</p><p><strong>Methods: </strong>Adult CABG patients were identified from MIMIC-IV 3.1 (n = 6,919; 7:3 stratified split for development) and eICU-CRD 2.0 (n = 5,972; external validation). The outcome was prolonged ICU LOS (> 3 days). Elastic Net plus Boruta selected eight bedside features collected within the first 24 h of ICU admission: 24-hour fluid intake, Charlson Comorbidity Index (CCI), Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score II (SAPS-II), Glasgow Coma Scale (GCS), vasopressor use, congestive heart failure, and atrial fibrillation. Nine machine-learning algorithms were compared by 10-fold cross-validation paired t-tests with Bonferroni and Benjamini-Hochberg correction. Calibration metrics included Hosmer-Lemeshow test, Integrated Calibration Index (ICI), expected-to-observed ratio, intercept, and slope (full Methods). SHapley Additive exPlanations (SHAP) provided per-patient feature attribution.</p><p><strong>Results: </strong>CatBoost was selected as the deployed model. On the MIMIC-IV internal test set (n = 2,076), the area under the receiver-operating-characteristic curve (AUC) was 0.7739 (95% confidence interval 0.7379-0.8099), Hosmer-Lemeshow p = 0.224, calibration slope 0.973. On the eICU-CRD external cohort, AUC was 0.6452 (95% CI 0.6311-0.6602), calibration slope 0.998, ICI 0.023. At the prevalence-anchored threshold of 0.30, sensitivity was 0.55, specificity 0.65, positive predictive value 0.40, and negative predictive value 0.77. Decision Curve Analysis showed positive net benefit over treat-all and treat-none across t = 0.20-0.40. SHAP top-3 features were 24-hour fluid intake, CCI, and atrial fibrillation.</p><p><strong>Conclusions: </strong>The model provides decision support at the post-CABG 24-hour ICU evaluation moment with modest discrimination and well-calibrated probabilities at deployment. The deployed online calculator renders per-patient feature contributions transparent at the bedside via SHAP; prospective validation is required before clinical deployment.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13579721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148337778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data analysis tool for identifying multidimensional health profiles associated with frailty in older adults.","authors":"Alondra Sánchez-Salinas, Anilu Franco-Arcega, Manuel A Ojeda-Misses, Lilian E Bosques-Brugada, Christian J Flores-Pérez, Aldo Márquez-Grajales","doi":"10.1186/s12911-026-03645-4","DOIUrl":"10.1186/s12911-026-03645-4","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Frailty in older adults is a multidimensional condition associated with physical, psychological, and sociodemographic factors that may affect functionality and quality of life during aging. The analysis of these factors often requires the management of heterogeneous information, which can represent a challenge for specialists during evaluation and follow-up processes. In this context, computational tools and data-driven approaches may support the organization and exploratory analysis of multidimensional health information. This study presents the development of a web-based computational system designed to support specialists in the collection, management, and analysis of health-related data from older adults in the State of Hidalgo, México.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Method: &lt;/strong&gt;A web platform was developed to register and organize sociodemographic, psychological, and physical information from older adults. The system integrated questionnaires, physical measurements, statistical analysis tools, and computational intelligence techniques within a single environment. Data from 210 participants were collected and analyzed using exploratory statistical methods, feature selection techniques, and K-means clustering to identify patterns associated with functional and health-related characteristics. Internal validation metrics, including the Silhouette coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index, were used to support cluster evaluation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The proposed system facilitated the structured storage, visualization, and exploratory analysis of multidimensional health information. The clustering analysis identified two differentiated groups characterized by distinct physical and psychological profiles. Differences were observed in variables such as muscular strength, calf circumference, body mass index, gratitude, and functional self-perception. The interpretation of the obtained profiles considered the demographic composition of the groups, particularly the predominance of women in one cluster, which may influence physical measurements associated with aging. Additionally, the usability assessment conducted with specialists using the SUS instrument indicated an acceptable level of usability for exploratory and research support purposes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The results suggest that integrating computational intelligence and statistical analysis techniques into a unified platform may support the exploratory assessment of multidimensional factors related to aging and frailty in older adults. The proposed system does not aim to establish clinical diagnoses, but rather to provide specialists with an additional resource for organizing and interpreting complex health-related information. This approach may contribute to the development of data-driven tools to support gerontological research and comprehensive evaluation processes in older populations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Trial registration: ","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13573356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148337723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}