Journal of Biomedical Informatics最新文献

{"title":"Enhanced heart failure mortality prediction through model-independent hybrid feature selection and explainable machine learning","authors":"Georgios Petmezas ,&nbsp;Vasileios E. Papageorgiou ,&nbsp;Vassilios Vassilikos ,&nbsp;Efstathios Pagourelias ,&nbsp;Dimitrios Tachmatzidis ,&nbsp;George Tsaklidis ,&nbsp;Aggelos K. Katsaggelos ,&nbsp;Nicos Maglaveras","doi":"10.1016/j.jbi.2025.104800","DOIUrl":"10.1016/j.jbi.2025.104800","url":null,"abstract":"<div><div>Heart failure (HF) remains a significant public health challenge with high mortality rates. Machine learning (ML) techniques offer a promising approach to predict HF mortality, potentially improving clinical outcomes. However, the effectiveness of these techniques heavily depends on the quality and relevance of the features used. This study introduces a novel hybrid feature selection methodology that combines Extremely Randomized Trees (Extra-Trees) and non-linear correlation measures to enhance 1-year all-cause mortality prediction in HF patients using echocardiographic and key demographic data. Unlike existing feature selection methods that are often tied to specific ML models and produce inconsistent feature sets across different algorithms, our proposed approach is model-independent, ensuring robustness and generalizability. Moreover, the optimal number of predictive features is identified through loss graph inspection, leading to a compact and highly informative subset of seven features. We trained and evaluated seven widely-used ML models on both the full feature set and the selected subset, finding that most models maintained or improved their predictive performance despite an 80% reduction in features. Model interpretability was enhanced using SHapley Additive exPlanations (SHAP), allowing for a detailed examination of how individual features influence predictions. To further assess its effectiveness, we compared our methodology against widely known feature selection techniques across all seven ML models. The results underscore the superiority of our proposed feature set in accurately predicting HF mortality over conventional methods, offering new opportunities for personalized management strategies based on a streamlined and explainable feature subset.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"163 ","pages":"Article 104800"},"PeriodicalIF":4.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing clinical data warehousing with provenance data to support longitudinal analyses and large file management : The gitOmmix approach for genomic and image data.","authors":"Maxime Wack, Adrien Coulet, Anita Burgun, Bastien Rance","doi":"10.1016/j.jbi.2025.104788","DOIUrl":"https://doi.org/10.1016/j.jbi.2025.104788","url":null,"abstract":"<p><strong>Background: </strong>If hospital Clinical Data Warehouses are to address today's focus in personalized medicine, they need to be able to track patients longitudinally and manage the large data sets generated by whole genome sequencing, RNA analyses, and complex imaging studies. Current Clinical Data Warehouses address neither issue. This paper reports on methods to enrich current systems by providing provenance data allowing patient histories to be followed longitudinally and managing the linking and versioning of large data sets from whatever source. The methods are open source and applicable to any clinical data warehouse system, whether data schema it uses.</p><p><strong>Method: </strong>We introduce gitOmmix, an approach that overcomes these limitations, and illustrate its usefulness in the management of medical omics data. gitOmmix relies on (i) a file versioning system: git, (ii) an extension that handles large files: git-annex, (iii) a provenance knowledge graph: PROV-O, and (iv) an alignment between the git versioning information and the provenance knowledge graph.</p><p><strong>Results: </strong>Capabilities inherited from git and git-annex enable retracing the history of a clinical interpretation back to the patient sample, through supporting data and analyses. In addition, the provenance knowledge graph, aligned with the git versioning information, enables querying and browsing provenance relationships between these elements.</p><p><strong>Conclusion: </strong>gitOmmix adds a provenance layer to CDWs, while scaling to large files and being agnostic of the CDW system. For these reasons, we think that it is a viable and generalizable solution for omics clinical studies.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104788"},"PeriodicalIF":4.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision Drug Repurposing (PDR): Patient-level modeling and prediction combining foundational knowledge graph with biobank data","authors":"Çerağ Oğuztüzün ,&nbsp;Zhenxiang Gao ,&nbsp;Hui Li ,&nbsp;Rong Xu","doi":"10.1016/j.jbi.2025.104786","DOIUrl":"10.1016/j.jbi.2025.104786","url":null,"abstract":"<div><h3>Objective:</h3><div>Drug repurposing accelerates therapeutic development by finding new indications for approved drugs. However, accounting for individual patient differences is challenging. This study introduces a Precision Drug Repurposing (PDR) framework at single-patient resolution, integrating individual-level data with a foundational biomedical knowledge graph to enable personalized drug discovery.</div></div><div><h3>Methods:</h3><div>We developed a framework integrating patient-specific data from the UK Biobank (Polygenic Risk Scores, biomarker expressions, and medical history) with a comprehensive biomedical knowledge graph (61,146 entities, 1,246,726 relations). Using Alzheimer’s Disease as a case study, we compared three diverse patient-specific models with a foundational model through standard link prediction metrics. We evaluated top predicted candidate drugs using patient medication history and literature review.</div></div><div><h3>Results:</h3><div>Our framework maintained the robust prediction capabilities of the foundational model. The integration of patient data, particularly Polygenic Risk Scores (PRS), significantly influenced drug prioritization (Cohen’s d = 1.05 for scoring differences). Ablation studies demonstrated PRS’s crucial role, with effect size decreasing to 0.77 upon removal. Each patient model identified novel drug candidates that were missed by the foundational model but showed therapeutic relevance when evaluated using patient’s own medication history. These candidates were further supported by aligned literature evidence with the patient-level genetic risk profiles based on PRS.</div></div><div><h3>Conclusion:</h3><div>This exploratory study demonstrates a promising approach to precision drug repurposing by integrating patient-specific data with a foundational knowledge graph.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"163 ","pages":"Article 104786"},"PeriodicalIF":4.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From GPT to DeepSeek: Significant gaps remain in realizing AI in healthcare.","authors":"Yifan Peng, Bradley A Malin, Justin F Rousseau, Yanshan Wang, Zihan Xu, Xuhai Xu, Chunhua Weng, Jiang Bian","doi":"10.1016/j.jbi.2025.104791","DOIUrl":"10.1016/j.jbi.2025.104791","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104791"},"PeriodicalIF":4.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143407974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media.","authors":"Yiming Li, Deepthi Viswaroopan, William He, Jianfu Li, Xu Zuo, Hua Xu, Cui Tao","doi":"10.1016/j.jbi.2025.104789","DOIUrl":"https://doi.org/10.1016/j.jbi.2025.104789","url":null,"abstract":"<p><strong>Objective: </strong>Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional deep learning models are adept at learning intricate feature representations and dependencies in sequential data, but often require extensive labeled data. In contrast, large language models (LLMs) excel in understanding contextual information, but exhibit unstable performance on named entity recognition (NER) tasks, possibly due to their broad but unspecific training. This study aims to evaluate the effectiveness of LLMs and traditional deep learning models in AE extraction, and to assess the impact of ensembling these models on performance.</p><p><strong>Methods: </strong>In this study, we utilized reports and posts from the Vaccine Adverse Event Reporting System (VAERS) (n = 230), Twitter (n = 3,383), and Reddit (n = 49) as our corpora. Our goal was to extract three types of entities: vaccine, shot, and adverse event (ae). We explored and fine-tuned (except GPT-4) multiple LLMs, including GPT-2, GPT-3.5, GPT-4, Llama-2 7b, and Llama-2 13b, as well as traditional deep learning models like Recurrent neural network (RNN) and Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT). To enhance performance, we created ensembles of the three models with the best performance. For evaluation, we used strict and relaxed F1 scores to evaluate the performance for each entity type, and micro-average F1 was used to assess the overall performance.</p><p><strong>Results: </strong>The ensemble demonstrated the best performance in identifying the entities \"vaccine,\" \"shot,\" and \"ae,\" achieving strict F1-scores of 0.878, 0.930, and 0.925, respectively, and a micro-average score of 0.903. These results underscore the significance of fine-tuning models for specific tasks and demonstrate the effectiveness of ensemble methods in enhancing performance.</p><p><strong>Conclusion: </strong>In conclusion, this study demonstrates the effectiveness and robustness of ensembling fine-tuned traditional deep learning models and LLMs, for extracting AE-related information following COVID-19 vaccination. This study contributes to the advancement of natural language processing in the biomedical domain, providing valuable insights into improving AE extraction from text data for pharmacovigilance and public health surveillance.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104789"},"PeriodicalIF":4.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143382606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient deep spatio-temporal encoding and medication substructure mapping for safe medication recommendation","authors":"Haoqin Yang ,&nbsp;Yuandong Liu ,&nbsp;Longbo Zhang ,&nbsp;Hongzhen Cai ,&nbsp;Kai Che ,&nbsp;Linlin Xing","doi":"10.1016/j.jbi.2025.104785","DOIUrl":"10.1016/j.jbi.2025.104785","url":null,"abstract":"<div><div>Medication recommendations are designed to provide physicians and patients with personalized, accurate and safe medication choices to maximize patient outcomes. Although significant progress has been made in related research, three major challenges remain: inadequate modeling of patients’ multidimensional and time-series information, insufficient representation of medication substructures, and poor balance between model accuracy and drug-drug interactions. To address these issues , a safe medication recommendation model SDRBT based on patient deep spatio-temporal encoding and medication substructure mapping is proposed in this paper. SDRBT has developed a patient deep temporal and spatial coding module, which combines symptom information, disease diagnosis information, and treatment information from the patient’s electronic health record data. It innovatively utilizes the Block Recurrent Transformer to model longitudinal temporal information of patients in different dimensions to obtain the horizontal representation of the patient’s current visit. A dual-domain mapping module for medication substructures is designed to perform global and local mapping of medications, fully learning and aggregating medication substructure representations. Finally, a PID LOSS control unit was designed, in which we studied a drug interaction control module based on the similarity calculation between the electronic health map and the drug interaction graph. This module ensures the safety of the recommended medication combination effectively improved the recommendation efficiency and reduced the model training time. Experiments on the public MIMIC-III dataset demonstrate SDRBT’s superior accuracy in medication recommendation.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"163 ","pages":"Article 104785"},"PeriodicalIF":4.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Bayesian network learning from multi-site data","authors":"Shuai Liu ,&nbsp;Xiao Yan ,&nbsp;Xiao Guo ,&nbsp;Shun Qi ,&nbsp;Huaning Wang ,&nbsp;Xiangyu Chang","doi":"10.1016/j.jbi.2025.104784","DOIUrl":"10.1016/j.jbi.2025.104784","url":null,"abstract":"<div><h3>Objective:</h3><div>Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance the understanding of disorder mechanisms and early intervention. Multi-site data arise naturally which could enhance the statistical power of single-site-based methods. However, the main concern is the inter-site heterogeneity and data sharing barriers between different sites. Our objective is to overcome these barriers to learn multiple Bayesian networks (BNs) from rs-fMRI data.</div></div><div><h3>Methods:</h3><div>We propose a federated joint estimator and the corresponding optimization algorithm, called NOTEARS-PFL. Specifically, we incorporate both shared and site-specific information into NOTEARS-PFL by utilizing the sparse group lasso penalty. Addressing data-sharing constraint, we develop the alternating direction method of multipliers for the optimization of NOTEARS-PFL. This entails processing neuroimaging data locally at each site, followed by the transmission of the learned network structures for central global updates.</div></div><div><h3>Results:</h3><div>The effectiveness and accuracy of the NOTEARS-PFL method are validated through its application on both synthetic and real-world multi-site resting-state functional magnetic resonance imaging (rs-fMRI) datasets. This demonstrates its superior efficiency and precision in comparison to alternative approaches.</div></div><div><h3>Conclusion:</h3><div>We proposed a toolbox called NOTEARS-PFL to learn the heterogeneous brain functional connectivity in MDD patients using multi-site data efficiently and with the data sharing constraint. The comprehensive experiments on both synthetic data and real-world multi-site rs-fMRI datasets with MDD highlight the excellent efficacy of our proposed method.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"163 ","pages":"Article 104784"},"PeriodicalIF":4.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143255682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies","authors":"Naimin Jing ,&nbsp;Yiwen Lu ,&nbsp;Jiayi Tong ,&nbsp;James Weaver ,&nbsp;Patrick Ryan ,&nbsp;Hua Xu ,&nbsp;Yong Chen","doi":"10.1016/j.jbi.2025.104787","DOIUrl":"10.1016/j.jbi.2025.104787","url":null,"abstract":"<div><h3>Objectives</h3><div>Binary outcomes in electronic health records (EHR) derived using automated phenotype algorithms may suffer from phenotyping error, resulting in bias in association estimation. Huang et al. <span><span>[1]</span></span> proposed the Prior Knowledge-Guided Integrated Likelihood Estimation (PIE) method to mitigate the estimation bias, however, their investigation focused on point estimation without statistical inference, and the evaluation of PIE therein using simulation was a proof-of-concept with only a limited scope of scenarios. This study aims to comprehensively assess PIE’s performance including (1) how well PIE performs under a wide spectrum of operating characteristics of phenotyping algorithms under real-world scenarios (e. g., low prevalence, low sensitivity, high specificity); (2) beyond point estimation, how much variation of the PIE estimator was introduced by the prior distribution; and (3) from a hypothesis testing point of view, if PIE improves type I error and statistical power relative to the naïve method (i.e., ignoring the phenotyping error).</div></div><div><h3>Methods</h3><div>Synthetic data and use-case analysis were utilized to evaluate PIE. The synthetic data were generated under diverse outcome prevalence, phenotyping algorithm sensitivity, and association effect sizes. Simulation studies compared PIE under different prior distributions with the naïve method, assessing bias, variance, type I error, and power. Use-case analysis compared the performance of PIE and the naïve method in estimating the association of multiple predictors with COVID-19 infection.</div></div><div><h3>Results</h3><div>PIE exhibited reduced bias compared to the naïve method across varied simulation settings, with comparable type I error and power. As the effect size became larger, the bias reduced by PIE was larger. PIE has superior performance when prior distributions aligned closely with true phenotyping algorithm characteristics. Impact of prior quality was minor for low-prevalence outcomes but large for common outcomes. In use-case analysis, PIE maintains a relatively accurate estimation across different scenarios, particularly outperforming the naïve approach under large effect sizes.</div></div><div><h3>Conclusion</h3><div>PIE effectively mitigates estimation bias in a wide spectrum of real-world settings, particularly with accurate prior information. Its main benefit lies in bias reduction rather than hypothesis testing. The impact of the prior is small for low-prevalence outcomes.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"163 ","pages":"Article 104787"},"PeriodicalIF":4.0,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Source drug combination and Omnidirectional feature fusion approach for predicting Drug-Drug interaction events","authors":"Shiwei Gao,&nbsp;Jingjing Xie,&nbsp;Yizhao Zhao","doi":"10.1016/j.jbi.2025.104772","DOIUrl":"10.1016/j.jbi.2025.104772","url":null,"abstract":"<div><h3>Background</h3><div>In the medical context where polypharmacy is increasingly common, accurately predicting drug-drug interactions (DDIs) is necessary for enhancing clinical medication safety and personalized treatment. Despite progress in identifying potential DDIs, a deep understanding of the underlying mechanisms of DDIs remains limited, constraining the rapid development and clinical application of new drugs.</div></div><div><h3>Methods</h3><div>This study introduces a novel multimodal drug-drug interaction (MMDDI) model based on multi-source drug data and comprehensive feature fusion techniques, aiming to improve the accuracy and depth of DDI prediction. We utilized the real-world DrugBank dataset, which contains rich drug information. Our task was to predict multiple interaction events between drug pairs and analyze the underlying mechanisms of these interactions. The MMDDI model achieves precise predictions through four key stages: feature extraction, drug pairing strategy, fusion network, and multi-source feature integration. We employed advanced data fusion techniques and machine learning algorithms for multidimensional analysis of drug features and interaction events.</div></div><div><h3>Results</h3><div>The MMDDI model was comprehensively evaluated on three representative prediction tasks. Experimental results demonstrated that the MMDDI model outperforms existing technologies in terms of predictive accuracy, generalization ability, and interpretability. Specifically, the MMDDI model achieved an accuracy of 93% on the test set, and the area under the AUC-ROC curve reached 0.9505, showing excellent predictive performance. Furthermore, the model’s interpretability analysis revealed the complex relationships between drug features and interaction mechanisms, providing new insights for clinical medication decisions.</div></div><div><h3>Conclusion</h3><div>The MMDDI model not only improves the accuracy of DDI prediction but also provides significant scientific support for clinical medication safety and drug development by deeply analyzing the mechanisms of drug interactions. These findings have the potential to improve patient medication outcomes and contribute to the development of personalized medicine.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104772"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Valley-Forecast: Forecasting Coccidioidomycosis incidence via enhanced LSTM models trained on comprehensive meteorological data","authors":"Leif Huender ,&nbsp;Mary Everett ,&nbsp;John Shovic","doi":"10.1016/j.jbi.2025.104774","DOIUrl":"10.1016/j.jbi.2025.104774","url":null,"abstract":"<div><div>Coccidioidomycosis (cocci), or more commonly known as Valley Fever, is a fungal infection caused by Coccidioides species that poses a significant public health challenge, particularly in the semi-arid regions of the Americas, with notable prevalence in California and Arizona. Previous epidemiological studies have established a correlation between cocci incidence and regional weather patterns, indicating that climatic factors influence the fungus’s life cycle and subsequent disease transmission. This study hypothesizes that Long Short-Term Memory (LSTM) and extended Long Short-Term Memory (xLSTM) models, known for their ability to capture long-term dependencies in time-series data, can outperform traditional statistical methods in predicting cocci outbreak cases. Our research analyzed daily meteorological features from 2001 to 2022 across 48 counties in California, covering diverse microclimates and cocci incidence. The study evaluated 846 LSTM models and 176 xLSTM models with various fine-tuning metrics. To ensure the reliability of our results, these advanced neural network architectures are cross analyzed with Baseline Regression and Multi-Layer Perceptron (MLP) models, providing a comprehensive comparative framework. We found that LSTM-type architectures outperform traditional methods, with xLSTM achieving the lowest test RMSE of 282.98 (95% CI: 259.2-306.8) compared to the baseline’s 468.51 (95% CI: 458.2-478.8), demonstrating a reduction of 39.60% in prediction error. While both LSTM (283.50, 95% CI: 259.7-307.3) and MLP (293.14, 95% CI: 268.3-318.0) also showed substantial improvements over the baseline, the overlapping confidence intervals suggest similar predictive capabilities among the advanced models. This improvement in predictive capability suggests a strong correlation between temporal microclimatic variations and regional cocci incidences. The increased predictive power of these models has significant public health implications, potentially informing strategies for cocci outbreak prevention and control. Moreover, this study represents the first application of the novel xLSTM architecture in epidemiological research and pioneers the evaluation of modern machine learning methods’ accuracy in predicting cocci outbreaks. These findings contribute to the ongoing efforts to address cocci, offering a new approach to understanding and potentially mitigating the impact of the disease in affected regions.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104774"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}