Prediction of early mortality in acute pancreatitis using arterial base excess: an international multicenter cohort study.

Rapid quantitative urinary chloride sensing with conductivity correction for cardiac patient management.

Plasma-based near-infrared spectroscopy combined with aquaphotomics for colorectal cancer screening.

Raman spectroscopy combined with stacking ensemble modeling for tracing the geographical origin of beef.

In the absence of biomarkers, the true biological footprint of migraine remains incompletely understood. It could perhaps be best characterized using machine learning models of multimodal data. The aim of this study was to (1) develop diagnostic models of migraine using multimodal data and (2) identify data-driven migraine phenotypes. This was a cross-sectional machine learning analysis of demographics, self-reported clinical and headache data, and genome-wide genotype data from the Trøndelag Health Study (data collected 1995-1997 and 2006-2008). All participants who were genotyped and completed the headache questionnaire were included. First, predictive machine learning models were developed using genotype data and general clinical data (excluding headache data) to diagnose individuals with migraine vs headache-free controls. Models were optimized on a training set and evaluated on a held-out test set, scored with the area under the receiver operating characteristic curve (AUC). Second, unsupervised models were trained on the headache data and the most predictive features from the diagnostic models to identify subgroups. The subgroups were compared using genome-wide association analyses, conventional polygenic risk scores (PRSs), and machine learning-based genetic risk scores. A total of 43,197 individuals were included in the diagnostic models, and 12,185 individuals were included in the data-driven phenotyping (mean [SD] age 49.1 [16.7] years; 51.7% women). The top-performing diagnostic model was a light gradient boosting machine, with a test set AUC of 0.80 (95% CI 0.78-0.81). Two main clusters were identified, one with 1,425 individuals, 94% of whom met diagnostic criteria for migraine, and another with 10,760 individuals, whereof 71% had nonmigraine headaches. The former was subclustered into 4 relatively distinct groups: one with only men, one with prominent neck pain, one with more musculoskeletal pain, anxiety and depression, and one with "classic" migraine. The groups were better discriminated by machine learning-based genetic risk scores compared with PRSs. Migraine can accurately be diagnosed from nonheadache data, suggesting that it is biologically describable by combinations of clinical, genetic, and environmental data. Data-driven phenotyping with such data identifies migraine subgroups with distinct phenotypic and genotypic signals, possibly not captured by current diagnostic criteria-but with potential implications for management.

ABSTRACT Accurate prediction of the freeze-thaw resistance of fiber-reinforced concrete (FRC) is crucial for optimizing its mix design and evaluating long-term durability. To enhance the accuracy of predicting FRC durability under freeze-thaw cycles, a dataset comprising 1,969 sets of FRC durability data was constructed. A machine learning hyperparameter optimization method based on the improved golden jackal optimization algorithm (PEGJO) was proposed for predicting FRC freeze resistance. The K-means++ algorithm was applied to cluster the data, balancing the training and testing sets. The PEGJO algorithm performs hyperparameter optimization for five machine learning algorithms: Support Vector Regression (SVR), Random Forest (RF), Kernel Ridge Regression (KRR), CatBoost, and XGBoost. Results indicate that the PEGJO-optimized XGBoost model achieved the highest predictive accuracy, with test set R2 values of 0.9581, 0.9585, and 0.9699 for compressive strength, relative dynamic modulus, and mass loss rate, respectively. Additionally, key characteristics influencing the durability of FRC were investigated using interpretable methodologies. To enhance practical applicability, an interactive graphical user interface based on the Tkinter framework was developed. This provides valuable guidance for FRC mix design, thereby advancing the field of performance evaluation for FRC.

To develop and validate a prediction model combining Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) parameters and clinicopathologic features for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study applied a retrospective method to collect preoperative MRI imaging data of patients with HCC from January 2021 to June 2025. All 279 patients (mean age of 58, M:F=203:76, 195 cases in training set and 84 cases in validation set) underwent MRI by Gd-EOB-DTPA and received DWI imaging scan before surgery. The MRI imaging features were observed, and the apparent diffusion coefficient (ADC) of the tumor solid region and tumor-to-liver parenchyma relative intensity ratio (RIR) were measured. LASSO logistic regression algorithm and multivariate analysis method were conducted to analyze the correlation between preoperative MRI imaging features and MVI. The prediction model was established, and the efficiency evaluation of the model was performed. Among 279 patients, 66 cases (23.66%) were MVI-positive. The study subjects were assigned to a training set (195 cases) and a validation set (84 cases) at a 7:3 ratio. Twelve variables were selected by LASSO regression. Multivariate analysis identified RIR transitional phase (OR=0.21), tumor size (OR=4.52), and ADC (OR=0.63) as independent predictors of MVI (P=0.029, <0.001, 0.032). The model achieved an AUC of 0.85 in the training set and 0.83 in the validation set. The negative predictive value (NPV) was 0.94 and 0.90 in the training and validation sets, respectively, while the positive predictive value (PPV) was limited to 0.49 and 0.40. Calibration curve and decision curve analyses demonstrated good consistency and clinical utility. On the basis of clinicopathologic features, MRI imaging features, and DWI parameters, this study preliminarily constructs a prediction model for positive MVI risk in HCC patients. The model exhibits good discrimination and a high NPV for effectively ruling out MVI, but its limited PPV warrants cautious interpretation of positive predictions due to a high false-positive rate.

Development and validation of a nomogram prediction model for prolonged length of hospital stay in patients with AECOPD.

To identify risk factors, mechanisms, and develop a validated predictive model for platelet transfusion refractoriness (PTR) in pediatric oncology to optimize transfusion outcomes. This retrospective cohort analyzed pediatric oncology patients receiving platelet transfusions, stratified by response (effective vs. refractory). Statistically significant indicators from t-tests underwent multivariable logistic regression to determine independent PTR predictors [platelet count (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer (D-D)]. A combined predictor derived via logistic regression was evaluated using ROC analysis. A Nomogram integrating PLT, hemoglobin (Hb), PT, APTT, and D-D was developed, with calibration (calibration curves) and clinical utility (decision curve analysis, DCA) rigorously validated. Multivariable logistic regression revealed an inverse association between elevated PLT and PTR risk (OR = 1.396), while elevated PT, APTT, and D-D were directly associated with increased PTR risk (OR: 0.794, 0.943, and < 0.001, respectively). The combined predictor demonstrated excellent discriminatory power (AUC = 0.962). The nomogram model exhibited high calibration accuracy in both training and validation sets, as evidenced by well-fitted calibration curves. DCA confirmed superior net benefit compared to alternative strategies. High AUC values (training set: 0.9723; validation set: 0.9639) indicated robust discrimination of PTR risk. PLT, PT, APTT, and D-dimer are key modulators of PTR risk in pediatric oncology. The developed nomogram model demonstrates favorable calibration, strong discriminatory ability, and significant clinical utility. It enables quantitative PTR risk stratification and guides pathway-targeted interventions, providing a precision tool for optimizing platelet transfusion management in this vulnerable population.

Capturing "source" and "evolution": A 5-year recurrence risk prediction model for nasopharyngeal carcinoma based on integrated radiomic spatial heterogeneity of primary tumor and lymph nodes.

Frailty is increasingly recognized as a predictor of poor surgical outcomes, yet its preoperative assessment in patients with non-small-cell lung cancer (NSCLC) remains challenging. This study aimed to develop and validate a high-performance predictive model for assessing frailty risk using routinely available clinical parameters and machine learning (ML) techniques. This single-center, cross-sectional study enrolled 489 preoperative patients with NSCLC hospitalized from April to October 2024. Participants were randomly divided into training (n=342) and validation (n=147) sets. Frailty was assessed using the FRAIL scale. We developed a logistic regression-based nomogram and compared it with six ML models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Frailty/pre-frailty prevalence was 36.1%. Logistic regression identified age, body mass index, comorbidity grade, fatigue, walking difficulty, single-breath diffusing capacity of the lung for carbon monoxide and the triglyceride-glucose (TyG) index as independent predictors. Although the nomogram achieved an AUC of 0.77 (95% confidence interval 0.69-0.85) in the validation set, the Light Gradient Boosting Machine (LGBM) model demonstrated superior discrimination, with an AUC of 0.965 in the training set and 0.807 in the validation set. Feature importance analysis highlighted the TyG index, comorbidity grade, and maximal voluntary ventilation as top predictors. This study developed a robust frailty risk prediction framework. The integration of ML algorithms with objective physiological markers (TyG index and respiratory reserve) significantly enhanced predictive accuracy over traditional methods, providing a reliable tool for preoperative risk stratification in patients with NSCLC.

Development and evaluation of a predictive biomarker model based on the niacin flush pathway for differentiating schizophrenia and bipolar disorder.

Reflux esophagitis (RE) is common among individuals with obesity and candidates for Metabolic Bariatric Surgery (MBS). Therefore, this study aimed to develop and internally evaluate a clinical nomogram to estimate the risk of RE in candidates for MBS. A total of 694 patients scheduled for MBS between July 2020 and December 2023 were retrospectively enrolled and randomly split into a training set and an internal test set at a 7:3 ratio. All patients underwent preoperative upper gastrointestinal endoscopy to assess RE status. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify candidate predictors. Variables with non-zero coefficients were subsequently entered into multivariable logistic regression analysis, and independent factors were used to construct the nomogram. Model discrimination, calibration, and clinical utility were evaluated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA), respectively. The overall prevalence of RE was 20.03%. Multivariable logistic regression analysis identified age, BMI, HDL-C, sex, and Helicobacter pylori (H. pylori) infection status as independent factors associated with RE. The nomogram constructed based on these variables demonstrated acceptable predictive performance in both sets. The area under the curve (AUC) was 0.796 in the training set and 0.79 in the internal test set. After bootstrap internal validation with 1,000 resamples in the training set, the bias-corrected C-index was 0.786. Calibration curves showed acceptable agreement between predicted probabilities and the observed incidence of RE. We developed a concise nomogram incorporating preoperative clinical variables to estimate the risk of RE in candidates for MBS. The model demonstrated acceptable discrimination and calibration based on internal validation; however, its clinical applicability requires further external validation.

Predictive value of early blood glucose trajectory for poor prognosis in ARDS patients.

ARDSMLpred: A machine learning model for predicting SCAP-associated ARDS based on MIMIC-IV database.

Identification of potential biomarkers for Polygonum multiflorum-induced liver injury in a clinical cohort: integrating machine learning of metabolomics with transcriptomic profiling.

Integrating machine learning and SHAP analysis to identify the risk of cognitive frailty in older adults with chronic heart failure.

3D deep-learning radiomics from MR-T2WI for predicting placenta accreta spectrum disorders: A multicenter study.

The application of a nomogram model integrating clinical factors and multimodal MRI radiomics features for predicting cervical lymph node metastasis for patients with oral tongue squamous cell carcinoma in different tumor stages.

MUSIOMICS: A multi-region radiomics framework that outperforms single-region analysis in classifying malignant pulmonary nodules.