BMC Medical Informatics and Decision Making最新文献

Background: Exploring records from entire cities to make decisions, particularly within public health systems, remains challenging.

Methods: This study investigates the public health data of São Caetano do Sul (SCS), in Brazil, to uncover patterns of antimicrobial prescriptions for infectious diseases using electronic health system records from primary care. Data science techniques such as preprocessing, transformation, loading, and analytics were also applied to achieve this goal.

Results: From January to September 2023, a total of 575,616 records of medical appointments were analyzed, and 67,023 patients underwent one or more medical appointments of which 16,572 had infectious diagnoses. There were 7,938 prescriptions of antimicrobials for infections of which the most frequent were upper respiratory infections (37%), gingivitis/periodontal disease (20%), and urinary tract infections (9%). The most frequently prescribed antimicrobials were amoxicillin (23%), azithromycin (15%), amoxicillin/clavulanate (13%), ciprofloxacin (11%), and cephalexin (11%). A preliminary evaluation of the data highlighted several points for targeted interventions, as well as challenges in obtaining certain information. For instance, some infections lacked documented antimicrobial treatment, while others were managed with medications not considered first-line options.

Conclusion: Implementing a system that can extract data directly from electronic records and automatically present it in a logical and relevant way to health professionals-including policymakers and administrators-would enable the identification of potential problems, the planning of interventions to improve antimicrobial use, and the monitoring of their impact. Our findings highlight opportunities to improve antimicrobial prescribing through data-driven tracking, analysis, and feedback mechanisms.

Background: Atherosclerosis in the carotid artery significantly contributes to embolic events leading to ischemic stroke. Precise identification of unstable carotid plaques through non-invasive imaging, is pivotal for stroke prevention. Artificial intelligence (AI) has demonstrated promise in enhancing the accuracy of plaque risk stratification. This review aims to assess the diagnostic performance of AI algorithms in distinguishing unstable carotid plaques from stable plaques using medical imaging.

Methods: We conducted comprehensive searches in Medline, Embase, Web of Science, IEEE, PubMed, and the Cochrane Library up to June 6, 2023. Eligible studies included those that utilized AI algorithms for identifying unstable carotid plaques from medical images. Binary diagnostic accuracy metrics, including sensitivity, specificity, and Area Under the Curve (AUC), were extracted. QUADAS-AI was used to assess risk of bias of the included studies.

Results: Among the 31 studies reviewed, 14 were subjected to meta-analysis, revealing a pooled sensitivity of 91% (95%CI: 86 - 95%), specificity of 84% (79 - 89%), and AUC of 0.94 (0.91 - 0.95). However, only one study reported external validation, limiting the generalizability of these findings, and substantial heterogeneity was observed (I² > 90%). Subgroup analyses indicated performance variations based on factors such as sample size, type of AI algorithms (machine learning or deep learning), segmentation methods (manual or automatic), and publication year. Despite observed publication bias and study heterogeneity, the findings underscore the promise of AI-driven approaches in carotid plaque risk stratification.

Conclusions: AI algorithms demonstrated favorable diagnostic performance in identifying unstable carotid plaques. Future research should focus on rigorous validation, ensuring generalizability, and enhancing the explainability of AI algorithms to facilitate their translational use.

Clinical trial number: Not applicable.

Background: Accurate and rapid assessment of fluid status of maintenance hemodialysis (MHD) patients and maintaining fluid balance is essential to ensure the quality of dialysis treatment. Currently, clinical methods for assessing ultrafiltration volume are still insufficient, and reliable tools that are more accurate and rapid are needed. The objective of this study was to construct a model for predicting ultrafiltration volume (UF) in MHD patients based on artificial neural network (ANN) algorithms, to validate and evaluate this model, and to investigate the impact of body composition prior to dialysis on UF in MHD patients.

Methods: A total of 319 patients undergoing MHD treatment at our center were enrolled. Basic demographic and clinical characteristics were collected and evaluated using the hemodialysis information system. Body composition was measured on ≥ 3 separate days before dialysis treatment using an Inbody bioimpedance instrument. The target ultrafiltration volume was determined by nephrologists based on the integration of body composition measurements and clinical characteristics, yielding a dataset of 1,205 entries. Heat maps were used to demonstrate the correlation between body composition and UF in MHD patients, and LASSO regression and multifactorial linear regression were used to screen the relevant indicator factors for final inclusion in the model, and Backpropagation Neural Network model (BPNN) was developed using the MATLAB (R2022a) neural network toolbox to establish the projected relationship between UF and pre-dialysis body composition. The effectiveness of the model was assessed based on the coefficient of determination (R²) and root mean square error (RMSE) of the calculated regression.

Results: The artificial neural network model demonstrated an optimal predictive performance metric of R² = 0.965 for forecasting ultrafiltration volume in MHD patients. With an average difference of 0.182 L between observed and predicted values, and highlighted the significant influence of certain body composition indicators on UF in MHD patients.

Conclusion: This study effectively demonstrates the predictive role of an artificial neural network model based on pre-dialysis body composition information in estimating ultrafiltration providing a valuable predictive tool to optimize assessment volume for MHD patients, of ultrafiltration volume in MHD patients.

Background: Acute kidney injury (AKI) has been confirmed to be related to the prognosis of aSAH patients. Evaluating the risk of AKI in the early stage is important to avoid the unfavorable outcome of aSAH patients. However, no study has explored the predictive value of machine learning algorithms for AKI after aSAH. This study was designed to develop a machine learning algorithm-based predictive model for AKI among aSAH patients.

Methods: The outcome of this study was the AKI confirmed using the KDIGO criteria. The predictive value of seven machine learning algorithms for the AKI among aSAH patients was explored and verified using the 5-fold cross-validation. The predictive efficiency of machine learning algorithms-based predictive models was evaluated by the area under the receiver operating characteristics curve (AUC). The Shapley Additive explanation method was performed to visualize the importance of features incorporated in machine learning algorithms-based predictive models.

Results: 711 aSAH patients were enrolled with an AKI incidence of 7.7%. The AKI group had higher WFNS (p = 0.011), Hunt Hess (p = 0.006), and lower Glasgow Coma Scale (GCS) (p = 0.004). The multiple aneurysm was more frequently observed in the AKI group (p = 0.027). The AKI group had longer length of ICU stay (p < 0.001), length of hospital stay (p < 0.001), and higher mortality (p < 0.001). Three algorithms performed well in predicting the AKI in the training dataset including the random forest (AUC = 1.000), AdaBoost (AUC = 0.954), and XGBoost (AUC = 0.947). The random forest performed the best in the validation dataset with an AUC of 0.724. The top ten features in the random forest algorithm were GCS, mean blood pressure, initial serum creatinine, cystatin C level, albumin, neutrophil, lactate dehydrogenase, glucose, white blood cell, and sodium.

Conclusions: The random forest model demonstrated superior performance in predicting AKI in aSAH patients, achieving a high AUC value, predictive accuracy, and remarkable stability. This model could help clinicians evaluate the risk of AKI in the early stage and guide therapeutic options among aSAH patients.

Background: Adolescent depression is a major public health concern. Physical health indicators are rarely included in risk tools. We examined whether adding dry eye disease (DED) to psychosocial and behavioral factors improves prediction of depressive symptoms.

Methods: We analyzed 2,076 adolescent questionnaires (94.5% response) covering ocular health, sleep, electronic device use, social support, and demographics. Five machine-learning classifiers were trained with cross-validation and evaluated for discrimination and calibration.

Results: Models that included DED achieved strong discrimination (AUC ≈ 0.84) and good calibration, with highest accuracy for no and severe depression and lower performance for mild/moderate categories.

Conclusions: Integrating ocular health with psychosocial factors enhances machine-learning prediction of adolescent depression and may support earlier, school-based identification and referral. Given the low-cost, questionnaire-based inputs and favorable calibration, this approach shows promise for population screening and targeted prevention, pending external validation and prospective testing.

Background: Making precise treatment decisions in esophageal cancer is essential for enhancing patient outcomes and avoiding overtreatment. Traditional approaches relying on special features or shallow learning models often fail to capture the complex, multi-scale patterns embedded in PET/CT imaging data. Recent advances in deep learning provide an opportunity to build more robust, data-driven systems for predictive modeling in oncology.

Methods: We propose a novel deep learning model that integrates convolutional and transformer-based components based on PET/CT data to support treatment decisions for esophageal cancer. The architecture introduces a Convolutional Feature Extractor with split-based residual blocks for efficient local feature capture, a Multi-scale Pooling module for spatial context aggregation, and an Multilayer Perceptron block for predicting. The model was evaluated using several performance metrics such as AUCROC, F1 score, Balanced Accuracy and benchmarked against state-of-the-art convolutional and transformer backbones such as ConvNeXt and Vision Transformer.

Results: The proposed model achieved superior performance across all evaluation metrics, including an AUCROC of 0.9935 and a Balanced Accuracy of 0.9630, outperforming existing models. These results validate the effectiveness of combining local-global representation learning through custom-designed modules. In addition, we conducted ablation studies to further demonstrate the individual contributions and effectiveness of each component within the proposed architecture. By systematically removing or replacing specific modules such as the Convolutional Feature Extractor and Multi-scale Pooling, we observed consistent performance degradation, which highlights the necessity and complementary roles of these components in achieving optimal predictive accuracy.

Conclusions: This study presents a novel hybrid deep learning architecture that enhances treatment decision support for esophageal cancer by leveraging multi-scale spatial encoding. The empirical evidence demonstrates that tailored architectural innovations significantly improve predictive accuracy over existing methods.