Journal of Biomedical Informatics最新文献

{"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}

{"title":"Analysis of longitudinal social media for monitoring symptoms during a pandemic","authors":"Shixu Lin ,&nbsp;Lucas Garay ,&nbsp;Yining Hua ,&nbsp;Zhijiang Guo ,&nbsp;Wanxin Li ,&nbsp;Minghui Li ,&nbsp;Yujie Zhang ,&nbsp;Xiaolin Xu ,&nbsp;Jie Yang","doi":"10.1016/j.jbi.2025.104778","DOIUrl":"10.1016/j.jbi.2025.104778","url":null,"abstract":"<div><h3>Objective</h3><div>Current studies leveraging social media data for disease monitoring face challenges like noisy colloquial language and insufficient tracking of user disease progression in longitudinal data settings. This study aims to develop a pipeline for collecting, cleaning, and analyzing large-scale longitudinal social media data for disease monitoring, with a focus on COVID-19 pandemic.</div></div><div><h3>Materials and methods</h3><div>This pipeline initiates by screening COVID-19 cases from tweets spanning February 1, 2020, to April 30, 2022. Longitudinal data is collected for each patient, two months before and three months after self-reporting. Symptoms are extracted using Name Entity Recognition (NER), followed by denoising with a combination of Graph Convolutional Network (GCN) and Bidirectional Encoder Representations from Transformers (BERT) model to retain only User-experienced Symptom Mentions (USM). Subsequently, symptoms are mapped to standardized medical concepts using the Unified Medical Language System (UMLS). Finally, this study conducts symptom pattern analysis and visualization to illustrate temporal changes in symptom prevalence and co-occurrence.</div></div><div><h3>Results</h3><div>This study identified 191,096 self-reported COVID-19-positive cases from COVID-19-related tweets and retrospectively collected 811,398,280 historical tweets, of which 2,120,964 contained symptoms information. After denoising, 39 % (832,287) of symptom-sharing tweets reflected user-experienced mentions. The trained USM model achieved an average F1 score of 0.927. Further analysis revealed a higher prevalence of upper respiratory tract symptoms during the Omicron period compared to the Delta and Wild-type periods. Additionally, there was a pronounced co-occurrence of lower respiratory tract and nervous system symptoms in the Wild-type strain and Delta variant.</div></div><div><h3>Conclusion</h3><div>This study established a robust framework for analyzing longitudinal social media data to monitor symptoms during a pandemic. By integrating denoising of user-experienced symptom mentions, our findings reveal the duration of different symptoms over time and by variant within a cohort of nearly 200,000 patients, providing critical insights into symptom trends that are often difficult to capture through traditional data source.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104778"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006056","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":"DOME: Directional medical embedding vectors from Electronic Health Records","authors":"Jun Wen ,&nbsp;Hao Xue ,&nbsp;Everett Rush ,&nbsp;Vidul A. Panickan ,&nbsp;Tianrun Cai ,&nbsp;Doudou Zhou ,&nbsp;Yuk-Lam Ho ,&nbsp;Lauren Costa ,&nbsp;Edmon Begoli ,&nbsp;Chuan Hong ,&nbsp;J. Michael Gaziano ,&nbsp;Kelly Cho ,&nbsp;Katherine P. Liao ,&nbsp;Junwei Lu ,&nbsp;Tianxi Cai","doi":"10.1016/j.jbi.2024.104768","DOIUrl":"10.1016/j.jbi.2024.104768","url":null,"abstract":"<div><h3>Motivation:</h3><div>The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts.</div></div><div><h3>Methods:</h3><div>We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts.</div></div><div><h3>Results:</h3><div>We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at <span><span>https://github.com/celehs/Directional-EHR-embedding</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104768"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142926986","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":"Deep learning in surgical process modeling: A systematic review of workflow recognition","authors":"Zhenzhong Liu ,&nbsp;Kelong Chen ,&nbsp;Shuai Wang ,&nbsp;Yijun Xiao ,&nbsp;Guobin Zhang","doi":"10.1016/j.jbi.2025.104779","DOIUrl":"10.1016/j.jbi.2025.104779","url":null,"abstract":"<div><div>Objective: The application of artificial intelligence (AI) in health care has led to a surge of interest in surgical process modeling (SPM). The objective of this study is to investigate the role of deep learning in recognizing surgical workflows and extracting reliable patterns from datasets used in minimally invasive surgery, thereby advancing the development of context-aware intelligent systems in endoscopic surgeries. Methods<strong>:</strong> We conducted a comprehensive search of articles related to SPM from 2018 to April 2024 in the PubMed, Web of Science, Google Scholar, and IEEE Xplore databases. We chose surgical videos with annotations to describe the article on surgical process modeling and focused on examining the specific methods and research results of each study. Results: The search initially yielded 2937 articles. After filtering on the basis of the relevance of titles, abstracts, and content, 59 articles were selected for full-text review. These studies highlight the widespread adoption of neural networks, and transformers for surgical workflow analysis (SWA). They focus on minimally invasive surgeries performed with laparoscopes and microscopes. However, the process of surgical annotation lacks detailed description, and there are significant differences in the annotation process for different surgical procedures. Conclusion: Time and spatial sequences are key factors determining the identification of surgical phase. RNN, TCN, and transformer networks are commonly used to extract long-distance temporal relationships. Multimodal data input is beneficial, as it combines information from surgical instruments. However, publicly available datasets often lack clinical knowledge, and establishing large annotated datasets for surgery remains a challenge. To reduce annotation costs, methods such as semi supervised learning, self-supervised learning, contrastive learning, transfer learning, and active learning are commonly used.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104779"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006134","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":"Modelling diversity in hospital strategies in city-scale ambulance dispatching with coupled game-theoretic model and discrete-event simulation","authors":"Xinyu Fu ,&nbsp;Valeria Krzhizhanovskaya ,&nbsp;Alexey Yakovlev ,&nbsp;Sergey Kovalchuk","doi":"10.1016/j.jbi.2025.104777","DOIUrl":"10.1016/j.jbi.2025.104777","url":null,"abstract":"<div><div>The optimization in the ambulance dispatching process is significant for patients who need early treatments. However, the problem of dynamic ambulance redeployment for destination hospital selection has rarely been investigated. The paper proposes an approach to model and simulate the ambulance dispatching process in multi-agent healthcare environments of large cities. The proposed approach is based on using the coupled game-theoretic (GT) approach to identify hospital strategies (considering hospitals as players within a non-cooperative game) and performing discrete-event simulation (DES) of patient delivery and provision of healthcare services to evaluate ambulance dispatching (selection of target hospital). Assuming the collective nature of decisions on patient delivery, the approach assesses the influence of the diverse behaviors of hospitals on system performance with possible further optimization of this performance. The approach is studied through a series of cases starting with a simplified 1D model and proceeding with a coupled 2D model and real-world application. The study considers the problem of dispatching ambulances to patients with the Acute Coronary Syndrome (ACS) directed to the Percutaneous Coronary Intervention (PCI) in the target hospital. A real-world case study of data from Saint Petersburg (Russia) is analyzed showing the better conformity of the global characteristics (mortality rate) of the healthcare system with the proposed approach being applied to discovering the agents’ diverse behavior.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104777"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006137","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":"Discontinuous named entities in clinical text: A systematic literature review","authors":"Areej Alhassan ,&nbsp;Viktor Schlegel ,&nbsp;Monira Aloud ,&nbsp;Riza Batista-Navarro ,&nbsp;Goran Nenadic","doi":"10.1016/j.jbi.2025.104783","DOIUrl":"10.1016/j.jbi.2025.104783","url":null,"abstract":"<div><h3>Objective</h3><div>Extracting named entities from clinical free-text presents unique challenges, particularly when dealing with discontinuous entities—mentions that are separated by unrelated words. Traditional NER methods often struggle to accurately identify these entities, prompting the development of specialised computational solutions. This paper systematically reviews and presents the methodologies developed for Discontinuous Named Entity Recognition in clinical texts, highlighting their effectiveness and the challenges they face.</div></div><div><h3>Method</h3><div>We conducted a systematic literature review focused on discontinuous named entities, using structured searches across four Computer Science-related and one medical-related electronic database. A combination of search terms, grouped into three synonym categories—problem, entity/approach, and task—yielded 2,442 articles. Guided by our research objectives, we identified five key dimensions to systematically annotate and normalise the data for comprehensive analysis.</div></div><div><h3>Result</h3><div>The review included 44 studies which were coded across several key dimensions: the chronological development of approaches, the corpora used, the downstream tasks affected by discontinuous named entities, the methodological approaches proposed to address the issue, and the reported performance outcomes. The discussion section examines the challenges encountered in this area and suggests potential directions for future research.</div></div><div><h3>Conclusion</h3><div>Significant progress has been made in discontinuous named entity recognition; however, there remains a need for more adaptable, generalisable solutions that are independent of custom annotation schemes. Exploring various configurations of generative language models presents a promising avenue for advancing this area. Additionally, future research should investigate the impact of precise versus imprecise recognition of discontinuous entities on clinical downstream tasks to better understand its practical implications in healthcare applications.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104783"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143038921","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":"ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis","authors":"Ziming Gan ,&nbsp;Doudou Zhou ,&nbsp;Everett Rush ,&nbsp;Vidul A. Panickan ,&nbsp;Yuk-Lam Ho ,&nbsp;George Ostrouchovm ,&nbsp;Zhiwei Xu ,&nbsp;Shuting Shen ,&nbsp;Xin Xiong ,&nbsp;Kimberly F. Greco ,&nbsp;Chuan Hong ,&nbsp;Clara-Lea Bonzel ,&nbsp;Jun Wen ,&nbsp;Lauren Costa ,&nbsp;Tianrun Cai ,&nbsp;Edmon Begoli ,&nbsp;Zongqi Xia ,&nbsp;J. Michael Gaziano ,&nbsp;Katherine P. Liao ,&nbsp;Kelly Cho ,&nbsp;Junwei Lu","doi":"10.1016/j.jbi.2024.104761","DOIUrl":"10.1016/j.jbi.2024.104761","url":null,"abstract":"<div><h3>Objective:</h3><div>Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient <strong>A</strong>ggregated na<strong>R</strong>rative <strong>C</strong>odified <strong>H</strong>ealth (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.</div></div><div><h3>Methods:</h3><div>Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated <span><math><mi>p</mi></math></span>-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients.</div></div><div><h3>Results:</h3><div>ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.<span><span>³</span></span> ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates.</div></div><div><h3>Conclusion:</h3><div>The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104761"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143038917","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":"BiomedRAG: A retrieval augmented large language model for biomedicine","authors":"Mingchen Li ,&nbsp;Halil Kilicoglu ,&nbsp;Hua Xu ,&nbsp;Rui Zhang","doi":"10.1016/j.jbi.2024.104769","DOIUrl":"10.1016/j.jbi.2024.104769","url":null,"abstract":"<div><div>Retrieval-augmented generation (RAG) involves a solution by retrieving knowledge from an established database to enhance the performance of large language models (LLM). , these models retrieve information at the sentence or paragraph level, potentially introducing noise and affecting the generation quality. To address these issues, we propose a novel BiomedRAG framework that directly feeds automatically retrieved chunk-based documents into the LLM. Our evaluation of BiomedRAG across four biomedical natural language processing tasks using eight datasets demonstrates that our proposed framework not only improves the performance by 9.95% on average, but also achieves state-of-the-art results, surpassing various baselines by 4.97%. BiomedRAG paves the way for more accurate and adaptable LLM applications in the biomedical domain.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104769"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006083","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":"Selecting a smartwatch for trials involving older adults with neurodegenerative diseases: A researcher’s framework to avoid hidden pitfalls","authors":"Lydia D. Boyle ,&nbsp;Brice Marty ,&nbsp;Kristoffer Haugarvoll ,&nbsp;Ole Martin Steihaug ,&nbsp;Monica Patrascu ,&nbsp;Bettina S. Husebo","doi":"10.1016/j.jbi.2025.104781","DOIUrl":"10.1016/j.jbi.2025.104781","url":null,"abstract":"<div><h3>Background</h3><div>Increased prevalence of neurodegenerative diseases complicates care needs for older adults. Sensing technologies, such as smartwatches, are one available solution which can help address the challenges of aging. Knowledge of the possibilities and pitfalls of these sensing technologies is of key importance to researchers when choosing a device for a trial and considering the sustainability of these technologies in real-world settings.</div></div><div><h3>Objective</h3><div>This study aims to uncover hidden truths related to the suitability of smartwatches for use in clinical trials which include older adults with neurodegenerative diseases, including end-of-life and palliative care studies.</div></div><div><h3>Method</h3><div>We perform an analysis of smartwatch features vs. user and researcher needs and provide an overview of hidden expenses which should be considered by the research team. Investigative research on 11 smartwatches is presented, selected based on previous use in clinical studies and recommendations from fellow researchers.</div></div><div><h3>Results</h3><div>We found that expenses, battery life, choice of research vs. commercial grade devices, data management, study methodology, and participant demographics are principal factors in selecting a smartwatch for a clinical trial involving older adults with neurodegenerative diseases. A revised framework based on our findings, and concepts from Connely (2021), Mattison (2023), and Espay (2019) et al.’s previous work, is presented as a tool for researchers in evaluation of smartwatches and future sensing technologies.</div></div><div><h3>Conclusion</h3><div>Careful consideration must be given to the fitness of technologies for future research, especially considering that this is a rapidly changing field. The process of selection of a smartwatch for a clinical trial should be thoughtful, scrutinous, and include interdisciplinary collaboration.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"162 ","pages":"Article 104781"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046962","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}