Objectives To compare the effectiveness of different exercise interventions in improving sleep quality and alleviating insomnia severity among patients with insomnia. Design Our study design was a systematic review and network meta-analysis. Eligibility criteria Our study eligibility criteria was restricted to include randomised controlled trials (RCTs) involving adults with insomnia that evaluated exercise-based interventions. Data sources PubMed, Cochrane Library, Embase, Web of Science, SPORTDiscus, and PsycINFO databases were systematically searched from inception to 1 April 2025. Risk of bias and certainty assessment Risk of bias was assessed using the Cochrane Risk of Bias 2 Tool. The certainty of the evidence was evaluated using the Confidence in Network Meta-Analysis (CINeMA) platform. Synthesis of results A frequentist network meta-analysis was performed to assess the effectiveness of interventions, with outcomes presented as mean differences (MDs) and 95% confidence intervals (95% CIs). Sleep outcomes were measured using validated tools, including the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), sleep diaries, and combined objective sleep measures such as polysomnography or actigraphy. Results The review included 1348 participants from 22 RCTs, examining 13 distinct interventions, of which seven were exercise-based: yoga, Tai Chi, walking or jogging, aerobic plus strength exercise, strength training alone, aerobic exercise combined with therapy, and mixed aerobic exercises. Among the included trials, four (18%) were rated as low risk of bias, 15 (68%) had some concerns, and three (14%) were considered to have a high risk. Compared with active controls (eg, usual care, lifestyle modifications), yoga likely results in a large increase in total sleep time (MD 110.88 minutes (min), 95% CI 58.66 to 163.09; moderate-certainty evidence) and may improve sleep efficiency (MD 15.59%, 95% CI 5.76 to 25.42; low-certainty evidence), reduce wake after sleep onset (MD −55.91 min, 95% CI −98.14 to −13.68; low-certainty evidence), and shorten sleep onset latency (MD −29.27 min, 95% CI −50.09 to −8.45; low-certainty evidence), all based on sleep diary data. Walking or jogging may result in a large reduction in ISI scores (MD −9.57 points, 95% CI −12.12 to −7.02; low-certainty evidence). Tai Chi may reduce PSQI scores (MD −4.57 points, 95% CI −7.50 to −1.63; low-certainty evidence), increase total sleep time (MD 52.07 min, 95% CI 25.53 to 78.61; low-certainty evidence), reduce wake after sleep onset (MD −36.11 min, 95% CI −62.81 to −9.42; low-certainty evidence), and shorten sleep onset latency (MD −24.76 min, 95% CI −41.07 to −8.46; low-certainty evidence), also based on sleep diary data. Additionally, Tai Chi may increase total sleep time as measured by combined objective sleep measures (MD 24.09 min, 95% CI 4.66 to 43.52; low-certainty evidence). Conclusion Exercise is an effective treatment for improving sleep in patients with insomnia. Among the various exercise interventions, yoga, Tai Chi and walking or jogging are more effective than other exercises. To further validate and strengthen these conclusions, large-scale, high-quality and well-designed RCTs are needed. Trial registration number CRD42024541899.

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperkalemia (MTH). We extracted data on patients with MTH from the Medical Information Mart for Intensive Care-IV, version 2.2 (MIMIC-IV v2.2) database. The dataset was split into a training set (75%) and a validation set (25%). We used the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential predictors, which included clinical laboratory indicators and vital signs. Pearson correlation analysis tested the correlation between predictors. In-hospital death was the prediction target. The Area Under the Curve (AUC) and accuracy of the training and validation sets of 7 ML algorithms were compared, and the optimal 1 was selected to develop the model. The calibration curve was used to evaluate the prediction accuracy of the model further. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) enhanced model interpretability. 496 patients with MTH in the Intensive Care Unit (ICU) were included. After screening, 17 clinical features were included in the construction of the ML model, and the Pearson correlation coefficient was <0.8, indicating that the correlation between the clinical features was small. eXtreme Gradient Boosting (XGBoost) outperformed other algorithms, achieving perfect scores in the training set (accuracy: 1.000, AUC: 1.000) and high scores in the validation set (accuracy: 0.734, AUC: 0.733). The calibration curves indicated good predictive calibration of the model. SHAP analysis identified the top 8 predictive factors: urine output, mean heart rate, maximum urea nitrogen, minimum oxygen saturation, minimum mean blood pressure, maximum total bilirubin, mean respiratory rate, and minimum pH. In addition, SHAP and LIME performed in-depth individual case analyses. This study demonstrates the effectiveness of ML methods in predicting mortality risk in ICU patients with MTH. It highlights the importance of predictors like urine output and mean heart rate. SHAP and LIME significantly enhanced the model’s interpretability.

Predicting the prognosis of patients with acute myocardial infarction (AMI) combined with diabetes mellitus (DM) is crucial due to high in-hospital mortality rates. This study aims to develop and validate a mortality risk prediction model for these patients by interpretable machine learning (ML) methods. Data were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.2). Predictors were selected by Least absolute shrinkage and selection operator (LASSO) regression and checked for multicollinearity with Spearman's correlation. Patients were randomly assigned to training and validation sets in an 8:2 ratio. Seven ML algorithms were used to construct models in the training set. Model performance was evaluated in the validation set using metrics such as area under the curve (AUC) with 95% confidence interval (CI), calibration curves, precision, recall, F1 score, accuracy, negative predictive value (NPV), and positive predictive value (PPV). The significance of differences in predictive performance among models was assessed utilising the permutation test, and 10-fold cross-validation further validated the model's performance. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were applied to interpret the models. The study included 2,828 patients with AMI combined with DM. Nineteen predictors were identified through LASSO regression and Spearman's correlation. The Random Forest (RF) model was demonstrated the best performance, with an AUC of 0.823 (95% CI: 0.774-0.872), high precision (0.867), accuracy (0.873), and PPV (0.867). The RF model showed significant differences (p < 0.05) compared to the K-Nearest Neighbours and Decision Tree models. Calibration curves indicated that the RF model's predicted risk aligned well with actual outcomes. 10-fold cross-validation confirmed the superior performance of RF model, with an average AUC of 0.828 (95% CI: 0.800-0.842). Significant Variables in RF model indicated that the top eight significant predictors were urine output, maximum anion gap, maximum urea nitrogen, age, minimum pH, maximum international normalised ratio (INR), mean respiratory rate, and mean systolic blood pressure. This study demonstrates the potential of ML methods, particularly the RF model, in predicting in-hospital mortality risk for AMI patients with DM. The SHAP and LIME methods enhance the interpretability of ML models.

Background: Predicting colorectal cancer (CRC) recurrence risk remains a challenge in clinical practice. Owing to the widespread use of radiomics in CRC diagnosis and treatment, some researchers recently explored the effectiveness of radiomics-based models in forecasting CRC recurrence risk. Nonetheless, the lack of systematic evidence of the efficacy of such models has hampered their clinical adoption. Objective: This study aimed to explore the value of radiomics in predicting CRC recurrence, providing a scholarly rationale for developing more specific interventions. Methods: Overall, 4 databases (Embase, PubMed, the Cochrane Library, and Web of Science) were searched for relevant articles from inception to January 1, 2025. We included studies that developed or validated radiomics-based machine learning models for predicting CRC recurrence using computed tomography or magnetic resonance imaging and provided discriminative performance metrics (c-index). Nonoriginal articles, studies that did not develop a model, and those lacking clear outcome measures were excluded from the study. The quality of the included original studies was assessed using the Radiomics Quality Score. A bivariate mixed-effects model was used to conduct a meta-analysis in which the c-index values with 95% CI were pooled. For the meta-analysis, subgroup analyses were conducted separately on the validation and training sets. Results: This meta-analysis included 17 original studies involving 4600 patients with CRC. The quality of the identified studies was low (mean Radiomics Quality Score 13.23/36, SD 2.56), with limitations in prospective design and biological validation. In the validation set, the c-index values based on clinical features, radiomics features, and radiomics features combined with clinical features were 0.73 (95% CI 0.68‐0.79), 0.80 (95% CI 0.75‐0.85), and 0.83 (95% CI 0.79‐0.87), respectively. In the internal validation set, the c-index values based on clinical features, radiomics features, and radiomics features+clinical features were 0.70 (95% CI 0.61‐0.79), 0.83 (95% CI 0.78‐0.88), and 0.83 (95% CI 0.78‐0.88), respectively. Finally, in the external validation set, the c-index values based on clinical features, radiomics features, and radiomics features combined with clinical features were 0.76 (95% CI 0.70‐0.83), 0.75 (95% CI 0.66‐0.83), and 0.83 (95% CI 0.78‐0.88), respectively. Conclusions: Radiomics-based machine learning models, especially those integrating radiomics and clinical features, showed promising predictive performance for CRC recurrence risk. However, this study has several limitations, such as moderate study quality, limited sample size, and high heterogeneity in modeling approaches. These findings suggest the potential clinical value of integrated models in risk stratification and their potential to enhance personalized treatment, though further high-quality prospective studies are warranted.