Journal of Biomedical Informatics最新文献

{"title":"Tentative renderings: Describing local data infrastructures that support the implementation and evaluation of national evaluation Initiatives.","authors":"Jennifer Van Tiem, Nicole L Johnson, Erin Balkenende, DeShauna Jones, Julia E Friberg Walhof, Emily E Chasco, Jane Moeckli, Kenda S Steffensmeier, Melissa J A Steffen, Kanika Arora, Borsika A Rabin, Heather Schacht Reisinger","doi":"10.1016/j.jbi.2025.104814","DOIUrl":"https://doi.org/10.1016/j.jbi.2025.104814","url":null,"abstract":"<p><strong>Objective: </strong>Data journeys are a way to describe and interrogate \"the life of data\" (Bates et al 2010). Thus far, they have been used to clarify the mobile nature of data by visualizing the pathways made by handling and moving data. We wanted to use the data journeys method (Eleftheriou et al. 2018) to compare different data journeys by noticing repetitions, patterns, and gaps.</p><p><strong>Methods: </strong>We conducted qualitative interviews with 43 evaluators, implementers and administrators associated with 21 clinical and training programs, called \"Enterprise-Wide Initiatives\" (EWIs) that are part of a national health system in the United States. We used inductive and deductive coding to identify narratives of data journeys, and then we used the \"swim lane\" (Collar et al 2012) format to make data journey maps based on those narratives.</p><p><strong>Results: </strong>Unlike the actors in Eleftheriou et al. (2018)'s work, who built a data infrastructure to manage clinical data, the actors in our study built data infrastructures to evaluate clinical data. We created and compared two data journey maps that helped us explore differences in data production and management. In tracing the pathways available to the data entity of interest, and the processes through which the actors interacted with it, we noticed how the same piece of information was made to work in different ways.</p><p><strong>Conclusions: </strong>Researchers often must build a new data infrastructures to respond to the unique needs of their evaluation work. Differing abilities lead to differences in what programs can build, and consequently what kinds of evaluation work they can support. With the goal of straightforward comparisons across different programs, a more limited focus on quantitative values, and a better description of the data journeys used by the evaluation teams, might facilitate more nuanced assessments of the evidence of complex outcomes.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104814"},"PeriodicalIF":4.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639642","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 novel data-driven approach for Personas validation in healthcare using self-supervised machine learning.","authors":"Emanuele Tauro, Alessandra Gorini, Grzegorz Bilo, Enrico Gianluca Caiani","doi":"10.1016/j.jbi.2025.104815","DOIUrl":"https://doi.org/10.1016/j.jbi.2025.104815","url":null,"abstract":"<p><strong>Objective: </strong>Persona validation is a challenging task, often relying on costly external validation methods. The aim of this study was the development of a novel method for Personas validation based on data already available during their creation.</p><p><strong>Methods: </strong>A novel approach based on self-supervised machine learning (SSML) was proposed. A training-test split was performed (80 %-20 %), with the training set used for Personas development. The obtained labels were used as input for a 5-fold cross-validation grid search, resulting in 5 optimal different models. The \"weak\" ground truth for the test set was determined using the trained clustering model, and was compared with the prediction obtained by the majority voting of the optimal models. Performance evaluation was conducted by means of weighted accuracy, precision, recall and F1 score.</p><p><strong>Results: </strong>The proposed method was evaluated on two very different healthcare datasets composed by questionnaires. The former was presented 1070 subjects, resulting in three unbalanced Personas (P0 n = 100; P1 n = 292; P2 n = 464). The latter included 176 subjects with three slightly unbalanced Personas. (P0 n = 58; P1 n = 32; P2 n = 50). The SSML approach resulted capable of correctly differentiating the clusters with high values of weighted accuracy (88.27 % and 94.12 %), precision (87.11 % and 92.83 %), recall (86.92 % and 91.67 %), and F1 score (86.92 % and 91.76 %).</p><p><strong>Conclusions: </strong>The proposed method showed high capabilities in generalization beyond the training data, validating the Personas' capability of stratifying the characteristics of target populations. Additionally, this method significantly reduced the costs to validate Personas when compared to other methods in current literature.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104815"},"PeriodicalIF":4.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639641","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":"MedicalGLM: A Pediatric Medical Question Answering Model with a quality evaluation mechanism","authors":"Xin Wang ,&nbsp;Zhaocai Sun ,&nbsp;Pingping Wang ,&nbsp;Benzheng Wei","doi":"10.1016/j.jbi.2025.104793","DOIUrl":"10.1016/j.jbi.2025.104793","url":null,"abstract":"<div><h3>Objective:</h3><div>Large Language models (LLMs) have a wide range of medical applications, especially in scenarios such as question-answering. However, existing models face the challenge of accurately assessing the quality of information when generating medical information, which may lead to the inability to effectively distinguish beneficial and harmful information, thus affecting the quality of question-answering. This study aims to improve the information quality and practicability of medical question-answering.</div></div><div><h3>Methods:</h3><div>This study proposes MedicalGLM, a fine-tuning model based on a quality evaluation mechanism. Specifically, MedicalGLM contains a reward model for assessing the quality of medical QA. It adjusts its training process by returning the assessment scores to the QA model as penalties through a quality score loss function.</div></div><div><h3>Results:</h3><div>The experimental results indicate that MedicalGLM achieved the highest scores among the evaluated models in the Rouge-1, Rouge-2, Rouge-L, and BLEU metrics, with values of 54.90, 28.02, 44.50, and 32.61, respectively. Its proficiency in generating responses for the pediatric medical quiz task is notably superior to other prevailing LLMs in the medical domain.</div></div><div><h3>Conclusion:</h3><div>MedicalGLM significantly improves the quality and practicability of the generated information of the medical question-answering model by introducing a quality evaluation mechanism, which provides an effective improvement idea for researching medical large language models. Our code and model are publicly available for further research on <span><span>https://github.com/wangxinwwang/MedicalGLM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104793"},"PeriodicalIF":4.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143585855","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":"FedIMPUTE: Privacy-preserving missing value imputation for multi-site heterogeneous electronic health records","authors":"Siqi Li ,&nbsp;Mengying Yan ,&nbsp;Ruizhi Yuan ,&nbsp;Molei Liu ,&nbsp;Nan Liu ,&nbsp;Chuan Hong","doi":"10.1016/j.jbi.2025.104780","DOIUrl":"10.1016/j.jbi.2025.104780","url":null,"abstract":"<div><h3>Objectives:</h3><div>We propose FedIMPUTE, a communication-efficient federated learning (FL) based approach for missing value imputation (MVI). Our method enables multiple sites to collaboratively perform MVI in a privacy-preserving manner, addressing challenges of data-sharing constraints and population heterogeneity.</div></div><div><h3>Methods:</h3><div>We begin by conducting MVI locally at each participating site, followed by the application of various FL strategies, ranging from basic to advanced, to federate local MVI models without sharing site-specific data. The federated model is then broadcast and used by each site for MVI. We evaluate FedIMPUTE using both simulation studies and a real-world application on electronic health records (EHRs) to predict emergency department (ED) outcomes as a proof of concept.</div></div><div><h3>Results:</h3><div>Simulation studies show that FedIMPUTE outperforms all baseline MVI methods under comparison, improving downstream prediction performance and effectively handling data heterogeneity across sites. By using ED datasets from three hospitals within the Duke University Health System (DUHS), FedIMPUTE achieves the lowest mean squared error (MSE) among benchmark MVI methods, indicating superior imputation accuracy. Additionally, FedIMPUTE provides good downstream prediction performance, outperforming or matching other benchmark methods.</div></div><div><h3>Conclusion:</h3><div>FedIMPUTE enhances the performance of downstream risk prediction tasks, particularly for sites with high missing data rates and small sample sizes. It is easy to implement and communication-efficient, requiring sites to share only non-patient-level summary statistics.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104780"},"PeriodicalIF":4.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143585854","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 generalization of medical image segmentation via game theory-based domain selection","authors":"Zuyu Zhang ,&nbsp;Yan Li ,&nbsp;Byeong-Seok Shin","doi":"10.1016/j.jbi.2025.104802","DOIUrl":"10.1016/j.jbi.2025.104802","url":null,"abstract":"<div><div>Medical image segmentation models often fail to generalize well to new datasets due to substantial variability in imaging conditions, anatomical differences, and patient demographics. Conventional domain generalization (DG) methods focus on learning domain-agnostic features but often overlook the importance of maintaining performance balance across different domains, leading to suboptimal results. To address these issues, we propose a novel approach using game theory to model the training process as a zero-sum game, aiming for a Nash equilibrium to enhance adaptability and robustness against domain shifts. Specifically, our adaptive domain selection method, guided by the Beta distribution and optimized via reinforcement learning, dynamically adjusts to the variability across different domains, thus improving model generalization. We conducted extensive experiments on benchmark datasets for polyp segmentation, optic cup/optic disc (OC/OD) segmentation, and prostate segmentation. Our method achieved an average Dice score improvement of 1.75% compared with other methods, demonstrating the effectiveness of our approach in enhancing the generalization performance of medical image segmentation models.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"164 ","pages":"Article 104802"},"PeriodicalIF":4.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143573099","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":"Contextual information contributes to biomedical named entity normalization","authors":"Gengxin Luo ,&nbsp;Nannan Shi ,&nbsp;Gang Wang ,&nbsp;Buzhou Tang","doi":"10.1016/j.jbi.2025.104806","DOIUrl":"10.1016/j.jbi.2025.104806","url":null,"abstract":"<div><h3>Objective:</h3><div>As one of the most crucial upstream tasks in biomedical informatics, biomedical named entity normalization (BNEN) aims to map mentioned named entities to uniform standard identifiers or terms. Most existing methods only consider the similarity between the individual mention itself and its candidates, however, ignore the valuable information of the context around the mention, which is also very important to understand the real semantic of the mention when it is ambiguous.</div></div><div><h3>Material and Methods:</h3><div>In this paper, based on IA-BIOSYN, a representative SOTA (state-of-the-art) BNEN method, we propose a novel BNEN method with contextual information fusion, called CIFSYN, where the context of a given mention is comprehensively considered by putting the given mention’s candidates in the same context of the mention, and the contextual information fusion module is introduced to capture the relationship among the mention, candidates, and context.</div></div><div><h3>Results:</h3><div>Experiments on five public BNEN datasets show that our proposed method achieves Acc@1 of 0.934, 0.937, 0.969, 0.959, and 0.856 on NCBI-Disease, BC5CDR-Disease, BC5CDR-Chemical, TAC2017-ADR, and COMETA, respectively, significantly better than other existing SOTA methods, and the introduced context information module brings a 0.5% improvement in Acc@1 on average.</div></div><div><h3>Conclusion:</h3><div>Contextual information around the mention improves the performance of biomedical named entity normalization.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"165 ","pages":"Article 104806"},"PeriodicalIF":4.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143567235","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":"From GPT to DeepSeek: Significant gaps remain in realizing AI in healthcare","authors":"Yifan Peng ,&nbsp;Bradley A. Malin ,&nbsp;Justin F. Rousseau ,&nbsp;Yanshan Wang ,&nbsp;Zihan Xu ,&nbsp;Xuhai Xu ,&nbsp;Chunhua Weng ,&nbsp;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":"163 ","pages":"Article 104791"},"PeriodicalIF":4.0,"publicationDate":"2025-03-01","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ieGENES: A machine learning method for selecting differentially expressed genes in cancer studies","authors":"Xiao-Lei Xia ,&nbsp;Shang-Ming Zhou ,&nbsp;Yunguang Liu ,&nbsp;Na Lin ,&nbsp;Ian M. Overton","doi":"10.1016/j.jbi.2025.104803","DOIUrl":"10.1016/j.jbi.2025.104803","url":null,"abstract":"<div><div><strong>Gene selection</strong> is crucial for cancer classification using microarray data. In the interests of improving cancer classification accuracy, in this paper, we developed a new wrapper method called <strong><em>ieGENES</em> for gene selection</strong>. First we proposed a <strong>parsimonious kernel machine regularization (PKMR) model</strong> by using ridge regularization in kernel machine driven classification to tackle multi-collinearity for the sake of stable estimates in <strong>high-dimensional</strong> settings. Then the <em>ieGENES</em> algorithm was developed to <strong>optimally identify relevant genes</strong> while iteratively eliminating redundant ones based on leave-one-out cross-validation accuracy. In particular, we developed a new methodology to optimally update model parameters upon gene removal. The <em>ieGENES</em> algorithm was evaluated on six <strong>cancer microarray datasets</strong> and compared to existing methods. Classification accuracy and number of <strong>differentially expressed genes</strong> (DEGs) identified were assessed. In terms of gene selection accuracy, the <em>ieGENES</em> <strong>outperformed</strong> multiple wrapper methods on 5 out of 6 datasets (Colon, Leukemia, Hepato, Glioma, and Breast Cancers), with statistically significant improvements (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>). For the Colon dataset, <em>ieGENES</em> achieved 96.21% accuracy with 167 DEGs. The proposed <em>ieGENES</em> technique demonstrated <strong>superior performance</strong> in identifying DEGs for cancer diagnosis comparing with existing techniques. It offers a promising tool for identifying <strong>biologically relevant genes</strong> in <strong>microarray data analysis</strong> and <strong>biomarker discovery</strong> for cancer research.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"164 ","pages":"Article 104803"},"PeriodicalIF":4.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537220","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":"CECRel: A joint entity and relation extraction model for Chinese electronic medical records of coronary angiography via contrastive learning","authors":"Yetao Tong ,&nbsp;Jijun Tong ,&nbsp;Shudong Xia ,&nbsp;Qingli Zhou ,&nbsp;Yuqiang Shen","doi":"10.1016/j.jbi.2025.104792","DOIUrl":"10.1016/j.jbi.2025.104792","url":null,"abstract":"<div><div>Entity and relation extraction from Chinese electronic medical records (EMRs) is a crucial foundation for constructing medical knowledge graphs and supporting downstream tasks. Chinese EMRs face challenges in accurately extracting medical entity relations due to limited data and the complexity of overlapping medical relationships. We propose CECRel, a joint extraction model for Chinese EMR entity relations based on contrastive learning and feature enhancement to address this issue. CECRel employs data augmentation strategies to generate positive and negative samples for contrastive loss computation and utilizes a feature enhancement module to enrich textual structural features, enabling the accurate extraction of complex relational triples. Experiments conducted on our constructed dataset, CACMeD, demonstrated that the model achieves an accuracy of 80.56%, a recall of 74.69%, and an F1 score of 77.51%. Furthermore, in the Baidu DuIE dataset, the model achieved an accuracy of 79.71%, a recall of 74.14%, and an F1 score of 76.82%, demonstrating that the proposed model is competitive among existing models.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"164 ","pages":"Article 104792"},"PeriodicalIF":4.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488371","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":"Network-based analysis of Alzheimer’s Disease genes using multi-omics network integration with graph diffusion","authors":"Softya Sebastian ,&nbsp;Swarup Roy ,&nbsp;Jugal Kalita","doi":"10.1016/j.jbi.2025.104797","DOIUrl":"10.1016/j.jbi.2025.104797","url":null,"abstract":"<div><div>Alzheimer’s Disease (AD) is a complex neurodegenerative disorder affecting millions worldwide. Despite extensive research, the mechanisms behind AD remain elusive. Many studies suggest that disease-responsible genes often act as hub genes in biological networks. However, this assumption requires further investigation in the context of AD. To examine the network characteristics of known AD genes, it is crucial to construct a highly confident network, which is challenging to achieve using a single data source. This work integrates multi-omics networks inferred from microarray, single-cell RNA sequencing, and single-nuclei RNA sequencing expression data, weighted with protein interaction and gene ontology information. We generate a high-quality integrated network by utilizing various inference methods and combining them through a graph diffusion-based integration approach. This network is then analyzed to investigate the properties of known AD-specific genes. Our findings reveal that AD genes are not always high-degree or central hub nodes in the network. Instead, these genes are distributed across different quartiles of degree centrality while maintaining significant interconnections for effective regulation. Furthermore, our study highlights that peripheral genes, often overlooked, also play crucial roles by connecting to relevant disease nodes and hub genes. These findings challenge the conventional understanding that AD-responsible genes are primarily the hub genes in the network, offering new insights into the complex regulatory mechanisms of AD and suggesting novel directions for future research.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"164 ","pages":"Article 104797"},"PeriodicalIF":4.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143492050","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}