Journal of Biomedical Informatics最新文献

{"title":"Interpretable statistical modeling of patient flow in emergency departments","authors":"Hugo Álvarez-Chaves,&nbsp;María D. R-Moreno","doi":"10.1016/j.jbi.2025.104937","DOIUrl":"10.1016/j.jbi.2025.104937","url":null,"abstract":"<div><h3>Objective:</h3><div>This paper aims to develop a data-driven simulation framework for modeling patient flow in a hospital Emergency Department using interpretable methods throughout the entire process in the absence of system resource data. The goal is to improve understanding of system dynamics and support decision-making processes through transparent simulations, even when resource data are unavailable.</div></div><div><h3>Methods:</h3><div>We developed a simulation framework using anonymized medical records from a Spanish hospital’s Emergency Department. The model captures patient flow considering triage levels by identifying routes and measuring the transition times between each stage in them. We estimated these transitions using both parametric (theoretical) distributions and non-parametric Kernel Density Estimation (KDE). Patient admissions times are modeled by using probability distributions. We enhanced realism through an iterative refinement process guided by tolerance thresholds and quantitative metrics. This process refined the synthetic data to match the original distributions.</div></div><div><h3>Results:</h3><div>Our approach produces highly realistic patient flow simulations with low tolerance values in the iterative method. The process gradually converges toward the original data. Distance and divergence metrics, together with statistical test results, indicate a high degree of similarity between the simulations and the real data, passing the Mann–Whitney U and Kolmogorov–Smirnov tests simultaneously in 100% of the generated samples when the tolerance threshold is low.</div></div><div><h3>Conclusion:</h3><div>The experimental results demonstrate that our simulation method effectively reproduces patient flow dynamics with a high level of realism and flexibility, even in the absence of information related to service resources. Its interpretable design and adjustable parameters enable safe data analysis and the exploration of alternative management strategies (e.g., modifying potential patient routes or restricting some transitions). These features position the methodology as a valuable tool for supporting informed decision-making and suggest its potential for use in other hospitals with suitable data, pending validation on external datasets.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104937"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145308156","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":"Discovering signature disease trajectories in pancreatic cancer and soft-tissue sarcoma from longitudinal patient records","authors":"Liwei Wang ,&nbsp;Rui Li ,&nbsp;Andrew Wen ,&nbsp;Qiuhao Lu ,&nbsp;Jinlian Wang ,&nbsp;Xiaoyang Ruan ,&nbsp;Adriana Gamboa ,&nbsp;Neha Malik ,&nbsp;Christina L. Roland ,&nbsp;Matthew H.G. Katz ,&nbsp;Heather Lyu ,&nbsp;Hongfang Liu","doi":"10.1016/j.jbi.2025.104935","DOIUrl":"10.1016/j.jbi.2025.104935","url":null,"abstract":"<div><h3>Background</h3><div>Most clinicians have limited experience with rare diseases, making diagnosis and treatment challenging. Large real-world data sources, such as electronic health records (EHRs), provide a massive amount of information that can potentially be leveraged to determine the patterns of diagnoses and treatments for rare tumors that can serve as clinical decision aids.</div></div><div><h3>Objectives</h3><div>We aimed to discover signature disease trajectories of 3 rare cancer types: pancreatic cancer, STS of the trunk and extremity (STS-TE), and STS of the abdomen and retroperitoneum (STS-AR).</div></div><div><h3>Materials and Methods</h3><div>Leveraging IQVIA Oncology Electronic Medical Record, we identified significant diagnosis pairs across 3 years in patients with these cancers through matched cohort sampling, statistical computation, right-tailed binomial hypothesis test, and then visualized trajectories up to 3 progressions. We further conducted systematic validation for the discovered trajectories with the UTHealth Electronic Health Records (EHR).</div></div><div><h3>Results</h3><div>Results included 266 significant diagnosis pairs for pancreatic cancer, 130 for STS-TE, and 118 for STS-AR. We further found 44 2-hop (i.e., 2-progression) and 136 3-hop trajectories before pancreatic cancer, 36 2-hop and 37 3-hop trajectories before STS-TE, and 17 2-hop and 5 3-hop trajectories before STS-AR. Meanwhile, we found 54 2-hop and 129 3-hop trajectories following pancreatic cancer, 11 2-hop and 17 3-hop trajectories following STS-TE, 5 2-hop and 0 3-hop trajectories following STS-AR. For example, pain in joint and gastro-oesophageal reflux disease occurred before pancreatic cancer in 64 (0.5%) patients, pain in joint and “pain in limb, hand, foot, fingers and toes” occurred before STS-TE in 40 (0.9%) patients, agranulocytosis secondary to cancer chemotherapy and neoplasm related pain occurred after pancreatic cancer in 256 (1.9%) patients. Systematic validation using the UTHealth EHR confirmed the validity of the discovered trajectories.</div></div><div><h3>Conclusion</h3><div>We identified signature disease trajectories for the studied rare cancers by leveraging large-scale EHR data and trajectory mining approaches. These disease trajectories could serve as potential resources for clinicians to deepen their understanding of the temporal progression of conditions preceding and following these rare cancers, further informing patient-care decisions.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104935"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145344968","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":"Advancing healthcare analytics: a thematic review of machine learning, health informatics, and real-world data applications","authors":"Maria I. Arias,&nbsp;Lorena Cadavid,&nbsp;Juan D. Velásquez","doi":"10.1016/j.jbi.2025.104934","DOIUrl":"10.1016/j.jbi.2025.104934","url":null,"abstract":"<div><h3>Objective</h3><div>To map the conceptual and methodological landscape of healthcare analytics by identifying dominant thematic clusters, synthesizing key trends, and outlining translational challenges and research opportunities in the field.</div></div><div><h3>Methods</h3><div>A total of 2,281 Scopus-indexed publications were analyzed using unsupervised text mining and clustering techniques. The analysis focused on identifying recurring themes, methodological innovations, and gaps within healthcare analytics literature across clinical, administrative, and public health contexts.</div></div><div><h3>Results</h3><div>Eight dominant themes were identified: intelligent systems for predictive healthcare, patient-centered health analytics, adaptive AI for clinical insights, demographic health analytics, digital mental health surveillance, ethical analytics for health surveillance, personalized care through data analytics, and AI-driven insights for outbreak response. These reflect a transition toward real-time, multimodal, and ethically grounded analytics ecosystems. Persistent challenges include data interoperability, algorithmic opacity, standardization of evaluation, and demographic bias.</div></div><div><h3>Conclusions</h3><div>The review highlights emerging priorities, including explainable AI, federated learning, and context-aware modeling, as well as ethical considerations related to data privacy and digital equity. Practical recommendations include co-designing with healthcare professionals, investing in infrastructure, and deploying real-time clinical decision support. Healthcare analytics is positioned as a foundational pillar of learning health systems with broad implications for translational research and precision health.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104934"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318166","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":"GraphFusion: Integrative prediction of drug synergy using multi-scale graph representations and cell line contexts","authors":"Biyang Zeng,&nbsp;Shikui Tu,&nbsp;Lei Xu","doi":"10.1016/j.jbi.2025.104921","DOIUrl":"10.1016/j.jbi.2025.104921","url":null,"abstract":"<div><div>Predicting the synergy of drug combinations is crucial for cancer treatment and drug development. Accurate prediction requires the integration of multiple types of data, including molecular structures of individual drugs, available synergy scores between drugs, and gene expression information from different cancer cell lines. The first two types contain multi-scale information within or between drugs, while the cell lines serve as the contextual background for drug interactions. Existing machine learning methods fail to fully utilize and integrate these information, leading to suboptimal performance. To address this issue, we introduce GraphFusion, an innovative approach that combines molecular graphs and drug synergy graphs with cell line contextual information. By employing novel GCN and Graphormer modules capable of accepting and utilizing external information, GraphFusion integrates these two levels of graph information. Specifically, the molecular graphs pass fine-grained structural information to the synergy graphs, while the synergy graphs convey global drug interaction data to the molecular graphs. Additionally, cell line information is incorporated as contextual background. This comprehensive integration enables GraphFusion to achieve state-of-the-art results on the O’Neil and NCI-ALMANAC datasets.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104921"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145212771","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":"Cross-scale semantic fusion integration of dual pathway models in drug repositioning","authors":"Mingxuan Li,&nbsp;Shuai Li,&nbsp;Zhen Li,&nbsp;Mandong Hu","doi":"10.1016/j.jbi.2025.104914","DOIUrl":"10.1016/j.jbi.2025.104914","url":null,"abstract":"<div><div>Drug Repositioning (DR) represents an innovative drug development strategy that significantly reduces both cost and time by identifying new therapeutic indications for approved drugs. Current methods primarily focus on extracting information from drug–disease networks, but often overlook critical local structural details between nodes. This study introduces CSDPDR, a novel Dual-branch graph neural network that integrates Topology Feature Information and Salient Feature Information to enhance drug repositioning accuracy and efficiency. Through the Topology-aware branch with Adaptive Residual Graph Attention and the Saliency-aware branch with Score-Driven Top-K Convolutional Graph Pooling, the model can capture both large-scale topology patterns and fine-grained local information. Furthermore, our approach effectively alleviate graph sparsity issues through meta-path-based network enhancement and confidence-based filtering mechanisms. Comparative experiments on two benchmark datasets an additional dataset demonstrate that CSDPDR significantly outperforms several state-of-the-art baseline methods. Case studies on Alzheimer’s disease and breast neoplasms further validate the model’s practical applicability and effectiveness.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104914"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182173","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":"LFVDNet: Low-frequency variable-driven network for medical time series","authors":"Yue Zhang ,&nbsp;Dengqun Sun ,&nbsp;Lei Li ,&nbsp;Jian Zhou ,&nbsp;Xiuquan Du ,&nbsp;Shuo Li","doi":"10.1016/j.jbi.2025.104913","DOIUrl":"10.1016/j.jbi.2025.104913","url":null,"abstract":"<div><h3>Objective:</h3><div>Medical time series, a type of multivariate time series with missing values, is widely used to predict time series analysis, the “impute first, then predict” end-to-end architecture is used to address this issue. However, existing methods are likely to lead to the loss of uniqueness and key information of low-frequency sampled variables (LFSVs) when dealing with them. In this paper, we aim to develop a method that effectively handles LFSVs, preserving their distinctive characteristics and essential information throughout the modeling process.</div></div><div><h3>Methods:</h3><div>We propose a novel end-to-end method named <em><strong>L</strong>ow-<strong>F</strong>requency <strong>V</strong>ariable-<strong>D</strong>riven network</em> (LFVDNet) for medical time series analysis. Specifically, the Time-Aware Imputer (TA) module encodes the observed values and critical time information, and uses the attention mechanism to establish an association between the observed values and the missing values. TA adopts channel-independent strategy to prevent interference from high-frequency sampled variables (HFSVs) on LFSVs, thereby preserving the unique information contained in LFSVs. The Offset-Selection Module (OS) independently selects data points for each variable through offsets, avoiding the natural disadvantages of LFSVs in selection-based imputation, thus solving the problem of the loss of key information of LFSVs. LFVDNet is the first method for analyzing multivariate time series with missing values that emphasizes the effective utilization of LFSVs.</div></div><div><h3>Results:</h3><div>We carried out the experiments on four public datasets and the experimental results indicate that LFVDNet has better robustness and performance. All code is available at <span><span>https://github.com/dxqllp/LFVDNet</span><svg><path></path></svg></span>.</div></div><div><h3>Conclusions:</h3><div>This study proposes a novel method for medical time series analysis, namely LFVDNet, which aims to effectively utilize LFSVs. Specifically, we have designed the TA module, which performs imputation through temporal correlations. The OS module, on the other hand, performs selective imputation based on a data point selection strategy. We have verified the effectiveness of this method on four datasets constructed from PhysioNet 2012 and MIMIC-IV.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104913"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145149181","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":"Towards a Biological Evaluation Framework for Oversampling (BEFO) gene expression data","authors":"Kevin Fee ,&nbsp;Suneil Jain ,&nbsp;Ross G. Murphy ,&nbsp;Anna Jurek-Loughrey","doi":"10.1016/j.jbi.2025.104932","DOIUrl":"10.1016/j.jbi.2025.104932","url":null,"abstract":"<div><div>Machine learning (ML) techniques are progressively being used in biomedical research to improve diagnostic and prognostic accuracy when used in conjunction with a clinician as a decision support system. However, many datasets used in biomedical research often suffer from severe class imbalance due to small population sizes, which causes machine learning models to become biased to majority class samples. Current oversampling methods primarily focus on balancing datasets without adequately validating the biological relevance of synthetic data, risking the clinical applicability of downstream model predictions. To address these shortcomings, we propose the Biological Evaluation Framework for Oversampling (BEFO) designed to ensure that synthetic gene expression samples accurately reflect the biological patterns present in original datasets. This innovation not only mitigates bias but enhances the trustworthiness of predictive models in clinical scenarios. We have developed a ranking method for synthetic samples based on this and evaluated each sample’s inclusion based on its rank. This ranking method calculates the WGCNA gene co-expression clusters on the original dataset. Several random forests are constructed to assess the alignment of each synthetic sample to each cluster. Only synthetic samples more important than real samples are included in a study. The experimental results demonstrate that our proposed ML oversampling framework can improve the biological feasibility of oversampled datasets by an average of 11%, leading to improved classification performance by an average of 9% when compared against five state-of-the-art (SOTA) oversampling methods and ten classification algorithms across six real world gene expressions datasets. Thereby establishing a new standard for synthetic data evaluation in biomedical ML applications.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104932"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145329281","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":"A LangChain-based pipeline for one-shot synthetic text generation using generative pre-trained transformers in palliative care research","authors":"Isabel Ronan ,&nbsp;Patrice Crowley ,&nbsp;Eva Rombouts ,&nbsp;Nicola Cornally ,&nbsp;Mohamad M. Saab ,&nbsp;David Murphy ,&nbsp;Sabin Tabirca","doi":"10.1016/j.jbi.2025.104936","DOIUrl":"10.1016/j.jbi.2025.104936","url":null,"abstract":"<div><h3>Objective:</h3><div>As the world’s population ages, nursing homes are of increasing importance. In order to care for a growing number of older adults, intelligent technologies are needed. Artificial Intelligence can be utilised to enhance palliative care in nursing homes. However, the data needed to train artificially intelligent agents is lacking within this sensitive domain due to privacy issues. Therefore, it is difficult for researchers to develop technological solutions. With the advent of large language models, such as ChatGPT, new text generation methods are made possible using limited data. In this pilot study, we investigate the use of large language models to generate synthetic data.</div></div><div><h3>Methods:</h3><div>We investigate the feasibility of using GPT-3.5 and GPT-4o models along with one-shot prompting to produce synthetic nurse notes which faithfully describe nursing home residents with met or unmet palliative care needs. We used LangChain to create a repeatable pipeline which can be adapted to different use-cases. We also compare the performance of both models using a set of qualitative and quantitative evaluations to determine which set of notes is more suitable for subsequent research.</div></div><div><h3>Results:</h3><div>GPT-3.5 performed slightly better than GPT-4o in our qualitative healthcare professional analysis. Quantitative analysis revealed appropriately heterogenous results across contextual similarity, lexical overlap, sentiment, and readability scores.</div></div><div><h3>Conclusion:</h3><div>Our work is the first investigation of such a generation method in the nursing home palliative care domain. Further refinement and validation of such data is needed in order to ensure the safe use of our approach.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104936"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145300760","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":"A comprehensive evaluation framework for synthetic medical tabular data generation","authors":"Anastasia Kurakova,&nbsp;Hajar Homayouni","doi":"10.1016/j.jbi.2025.104939","DOIUrl":"10.1016/j.jbi.2025.104939","url":null,"abstract":"<div><div>Machine learning (ML) applications have enabled significant advancements in healthcare, such as predicting pandemics, personalizing treatments, and developing life-saving drugs. However, ML model training requires large datasets, which are difficult to obtain in healthcare due to privacy concerns. Synthetic data generation offers a promising solution by providing access to large-scale training data while protecting patient privacy. Our research focuses on tabular medical data, the predominant format for Electronic Health Records (EHRs), and introduces a comprehensive evaluation framework that assesses synthetic data in four critical dimensions: quality, privacy, usability, and computational complexity of the data generation process. The framework ensures that synthetic data maintains sufficient similarity to real data for ML applications while preserving patient confidentiality. To validate our approach, we applied six state-of-the-art (SOTA) generative models to generate synthetic medical datasets and evaluated them within our framework. In contrast to conventional approaches that focus primarily on statistical similarity, our framework provides a broader assessment that incorporates outlier detection, privacy risks, and domain-specific constraints. Our findings demonstrate that our framework can identify critical shortcomings in synthetic data generation models, such as the amplification of duplicate rows and the generation of out-of-range values, which are overlooked by traditional statistical evaluation methods. Our implementation of the framework is available at: <span><span>https://github.com/akurakova/SDE_Framework</span><svg><path></path></svg></span></div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104939"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145308155","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":"Review of tools to support Target Trial Emulation","authors":"Christina A. van Hal ,&nbsp;Elmer V. Bernstam ,&nbsp;Todd R. Johnson","doi":"10.1016/j.jbi.2025.104897","DOIUrl":"10.1016/j.jbi.2025.104897","url":null,"abstract":"<div><h3>Objective:</h3><div>Randomized Controlled Trials (RCTs) are the gold standard for clinical evidence, but ethical and practical constraints sometimes necessitate or warrant the use of observational data. The aim of this study is to identify informatics tools that support the design and conduct of Target Trial Emulations (TTEs), a framework for designing observational studies that closely emulate RCTs so as to minimize biases that often arise when using real-world evidence (RWE) to estimate causal effects.</div></div><div><h3>Methods:</h3><div>We divided the process of conducting TTEs into three phases and seven steps. We then systematically reviewed the literature to identify currently available tools that support one or more of the seven steps required to conduct a TTE. For each tool, we noted which step or steps the tool supports.</div></div><div><h3>Results:</h3><div>7625 papers were included in the initial review, with 76 meeting our inclusion criteria. Our review identified 24 distinct tools applicable to the three phases of TTE. Specifically, 3 tools support the Design Phase, 5 support the Implementation Phase, and 19 support the Analysis Phase, with some tools applicable to multiple phases.</div></div><div><h3>Conclusion:</h3><div>This review revealed significant gaps in tool support for the Design Phase of TTEs, while support for the Implementation and Analysis phases was highly variable. No single tool currently supports all aspects of TTEs from start to finish and few tools are interoperable, meaning they cannot be easily integrated into a unified workflow. The results highlight the need for further development of informatics tools for supporting TTEs.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104897"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145185991","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}