BMC Medical Informatics and Decision Making最新文献

{"title":"Enhancing patient care with AI agents: integrating advanced chatbot technologies for improved healthcare delivery.","authors":"Yasmine Abu Adla, Ahmad El Hajj","doi":"10.1186/s12911-026-03694-9","DOIUrl":"10.1186/s12911-026-03694-9","url":null,"abstract":"<p><p>The integration of artificial intelligence (AI) in healthcare presents significant opportunities to enhance patient care and streamline medical workflows. However, challenges related to accuracy, reliability, and accessibility continue to limit the widespread adoption of AI-driven medical assistants. To the best of our knowledge, this is the first study to integrate multiple advanced AI techniques into a unified system designed to provide real-time, context-aware medical insights while ensuring accuracy, engagement, and interpretability. This study aims to develop an AI-powered medical agent capable of assisting both patients and healthcare professionals by generating informed medical responses, automating healthcare-related tasks, and improving patient interaction through interactive and reliable assistance. The methods employed in this research include Retrieval-Augmented Generation (RAG) for contextualized medical responses, the Wikipedia API for real-time knowledge retrieval, and knowledge graphs for mapping symptoms to potential diseases. Additionally, a symptom checker tool enables preliminary diagnosis and personalized health recommendations using a prompt-based system. The system was evaluated through automated performance assessments and expert reviews. The results demonstrate that the knowledge graph tool achieved 95% accuracy in medical query responses, showcasing its reliability in symptom-disease mapping. Additionally, sentiment analysis of patient interactions reached 97% accuracy, reinforcing the system's ability to understand and respond empathetically. The Wikipedia-based retrieval system maintained an average response time of 10 seconds, ensuring real-time applicability. Furthermore, 100 medical experts rated the AI agent's responses 4.6 for comprehensiveness, 4.5 for engagement, and 4.5 for empathy and tone on a 5-point scale. These findings suggest that integrating advanced AI techniques enhances the accuracy, responsiveness, and contextual relevance of AI-driven medical agents. This scalable and reliable system presents a viable solution to reduce healthcare workload, enhance patient engagement, and democratize access to trusted medical information, reinforcing its potential as a transformative tool in modern healthcare.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based prediction of 3-6-month post-stroke cognitive impairment using acute-phase clinical data: a two-center retrospective prognostic modeling study.","authors":"Xiuming Chen, Jiang Ma, Yingying Chang, Hui Wang, Xiaoyan Li, Qing Li, Shengkai Ma, Jin Zhang, Jianchao Xu","doi":"10.1186/s12911-026-03703-x","DOIUrl":"10.1186/s12911-026-03703-x","url":null,"abstract":"<p><strong>Background: </strong>Post-stroke cognitive impairment (PSCI) is common and disabling, but identifying patients at risk early remains difficult. We developed and externally validated a retrospective machine-learning prognostic model using acute-phase predictors collected within 1 week after stroke onset to estimate PSCI risk assessed at 3-6 months after stroke.</p><p><strong>Methods: </strong>Two inpatient rehabilitation cohorts were retrospectively analyzed: Center 1 (n = 1,369; training/internal test) and Center 2 (n = 341; external validation). Cognitive status at 3-6 months was the outcome. Features were selected with Boruta and LASSO; eight algorithms were trained with 10-fold cross-validation. Performance was assessed by AUROC, average precision (AP), accuracy, sensitivity, specificity, precision, F1, calibration (Brier score, calibration intercept, calibration slope and plots), and decision-curve analysis (DCA). SHAP was used for interpretability, and a web-based proof-of-concept prototype was developed to display model-derived risk estimates.</p><p><strong>Results: </strong>Ten predictors were ultimately selected. In external validation, XGBoost achieved the highest AUROC (0.827), with an average precision of 0.6668, accuracy of 0.8328, sensitivity of 0.6250, specificity of 0.9241, precision of 0.7831, and an F1-score of 0.6952. Calibration was acceptable (Brier score = 0.1366; calibration intercept = - 0.027; calibration slope = 0.916), and DCA suggested positive net benefit within the 15%-40% threshold-probability range. The largest SHAP contributors to the XGBoost model output included diabetes, NIHSS, Hgb, Hcy, apoA1, TG, ALB, WMH, smoking, and age.</p><p><strong>Conclusion: </strong>This model may support PSCI risk stratification in post-stroke patients admitted for inpatient rehabilitation. However, further validation in broader and more representative stroke populations is required before wider application.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stress distribution in three-unit implant-supported prostheses with different abutment designs: a finite element analysis.","authors":"Ismail Furkan Guney, Busra Tosun","doi":"10.1186/s12911-026-03690-z","DOIUrl":"10.1186/s12911-026-03690-z","url":null,"abstract":"<p><strong>Background: </strong>Implant-supported prostheses are widely used in posterior mandibular restorations, where biomechanical factors play a critical role in long-term performance. Abutment design may influence stress distribution within implant-supported systems, potentially affecting both prosthetic components and surrounding bone. This study aimed to evaluate stress distribution in three-unit implant-supported prostheses with different abutment designs using three-dimensional finite element analysis.</p><p><strong>Methods: </strong>A three-dimensional finite element model of the posterior mandible with two implants (4.1 mm diameter, 10 mm length) placed in the first premolar and first molar regions was constructed. Three abutment designs (multi-unit, octa, and variobase) supporting monolithic zirconia restorations were analyzed. Vertical (150 N) forces were applied perpendicular to the occlusal surfaces, and oblique (200 N at 45°) forces were applied in the buccolingual direction. Stress distribution in implants, abutments, screws, restorations, and surrounding bone was evaluated.</p><p><strong>Results: </strong>Stress distribution varied according to abutment design and loading condition. Variobase abutments showed higher stress concentrations in implants under both loading conditions, while octa abutments exhibited higher stress values in screws and restorations. Multi-unit abutments demonstrated relatively lower stress values in screws and restorations compared to other designs. Oblique loading produced higher stress levels than vertical loading across all components. Bone stress values increased under oblique loading conditions.</p><p><strong>Conclusions: </strong>Within the limitations of this finite element analysis, abutment design influenced stress distribution in implant-supported prostheses, and loading direction affected stress magnitude. These findings provide biomechanical insight into the behavior of different abutment designs in posterior implant-supported restorations.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approach to analyzing complex care coordination patterns for medically complex children.","authors":"Saki Aoto, Yoshikazu Ito, Shintaro Morooka, Ai Ito, Akihiro Umezawa, Kohji Okamura, Tomoo Nakamura","doi":"10.1186/s12911-026-03691-y","DOIUrl":"10.1186/s12911-026-03691-y","url":null,"abstract":"<p><strong>Background: </strong>The number of children with medical complexity (CMC) in Japan has exceeded 20,000, doubling over the past 15 years, forming a significant population group internationally. While care coordination for CMC requires consideration of growth and school attendance, the relatively small population size compared to elderly care makes it challenging for care coordinators to gain sufficient experience. We investigated whether machine learning models could assist in care coordination for CMC.</p><p><strong>Methods: </strong>We developed three tree-based gradient boosting prediction models using 1,042 care sheets from a pediatric respite facility. The first model estimated ventilator use to verify if care patterns could predict symptoms; the second predicted home doctor utilization to examine if symptoms could indicate required care; and the third estimated age to analyze school-related care needs. SHAP values were used to interpret feature importance in the prediction models. Each model was independently created and validated ten times to account for potential sampling bias among respite facility users.</p><p><strong>Results: </strong>The highest F1 scores for the three models were 0.83, 0.826, and 0.829, respectively. The ventilator model identified equipment maintenance services as highly significant; the home doctor model revealed patterns in complex service utilization; and the age model demonstrated varying important factors across school-age transitions.</p><p><strong>Conclusion: </strong>This study highlights AI's potential in identifying crucial factors related to CMC services and its effectiveness as a valuable tool for service coordination. Despite the limited dataset of 1,042 cases, our findings emphasize the importance of digital preservation of CMC healthcare information.</p><p><strong>Trial registration: </strong>Retrospectively registered.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of spinal cord injury after thoracoabdominal aortic repair by machine learning method.","authors":"Jian Song, Kai Xu, Jingyu Wang, Tianxia Gu, Wenyuan Lu, Yumeng Ji, Yunpeng Ling, Juntao Qiu, Cuntao Yu","doi":"10.1186/s12911-026-03684-x","DOIUrl":"10.1186/s12911-026-03684-x","url":null,"abstract":"<p><strong>Background: </strong>Spinal cord injury (SCI) is a severe complication after thoracoabdominal aortic repair (TAAR), substantially increasing postoperative mortality and disability. This study aimed to develop an improved machine learning model using comprehensive perioperative data to predict SCI risk.</p><p><strong>Methods: </strong>270 patients were included in the study, and 66 potentially meaningful variables were selected from 84 original variables as input data to drive machine learning. An optimized machine learning algorithm (PSO-FLXGBoost) was applied to stratify and rank predictors, and Shapley additive explanations (SHAP) were used to interpret feature contributions.</p><p><strong>Results: </strong>The PSO-FLXGBoost model achieved strong discrimination with an AUC of 0.895. SHAP analysis highlighted four key predictors: intraoperative hemoglobin < 70 g/L, preoperative D-dimer > 6 µg/mL, platelet count > 250 × 10^9/L, and operation time > 500 min, all of which significantly increased SCI risk.</p><p><strong>Conclusions: </strong>The optimized PSO-FLXGBoost model reliably predicts SCI after TAAR, emphasizing intraoperative hemoglobin, coagulation status, and operative duration as critical risk factors. These insights may guide tailored perioperative strategies to improve patient outcomes.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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-based treatment decision support framework for multi-vessel coronary artery disease using integrated coronary angiography and clinical data.","authors":"Byeolhee Kim, Junhee Kim, Young-Hak Kim, Tae Joon Jun, Jung-Min Ahn","doi":"10.1186/s12911-026-03692-x","DOIUrl":"10.1186/s12911-026-03692-x","url":null,"abstract":"<p><strong>Background: </strong>Treatment selection between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) for multi-vessel coronary artery disease remains challenging, requiring careful consideration of both anatomical and clinical factors.</p><p><strong>Methods: </strong>We developed a deep learning framework that automatically analyzes coronary angiography videos and integrates clinical data to support revascularization decisions. The framework consists of three key modules: (1) a video filtering module for quality screening, (2) a representative frame selection module based on curriculum learning, and (3) a treatment classification module combining imaging features with clinical characteristics. The framework was evaluated using 5,647 patients' data from a single center, with cross-validation.</p><p><strong>Results: </strong>Our framework demonstrated superior performance with a mean AUC of 0.8275 ± 0.0167 in 5-fold cross-validation, significantly outperforming traditional machine learning approaches (baseline AUC: 0.66 ± 0.007, [Formula: see text]). Ablation studies showed sequential improvements: representative frame selection improved performance over baseline by 3.69% (AUC: 0.6657 to 0.7026), video quality filtering provided additional 0.56% improvement (AUC: 0.7026 to 0.7082), and clinical information integration achieved final enhancement of 1.35% (AUC: 0.7082 to 0.7217). For frame selection specifically, curriculum learning outperformed supervised learning by 6.3% (AUC: 0.9067 to 0.9637).</p><p><strong>Conclusions: </strong>This study provides a promising approach for objective, data-driven decision support in complex coronary revascularization cases. The framework's multi-modal integration strategy and automated analysis capabilities demonstrate potential for improving the consistency and efficiency of treatment selection while maintaining high standards of clinical care.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13628886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mixture cure modeling framework for survival analysis of hospitalized COVID-19 patients: implications for hospital resource management.","authors":"Zahra Rezaei Ghahroodi, Roxana Darvishi, Abdollah Safari, Firoozeh Haghighi, Marjan Mansourian","doi":"10.1186/s12911-026-03687-8","DOIUrl":"10.1186/s12911-026-03687-8","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;COVID-19 has caused substantial global morbidity and mortality, placing unprecedented strain on hospital systems. Understanding heterogeneity in survival outcomes and identifying subgroups with distinct mortality risk profiles are essential for improving preparedness in future pandemics. Traditional survival models do not account for the presence of patients who are effectively cured within a clinically meaningful window. Mixture cure models offer a framework for simultaneously estimating the probability of long-term survival and the time-to-event among uncured individuals. This study applies a mixture cure modeling approach to evaluate mortality risk factors among hospitalized COVID-19 patients and to derive statistical insights relevant to hospital resource planning.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Data were obtained from 1,998 patients enrolled in the Khorshid COVID Cohort (KCC), a prospective hospital-based study with one-year follow-up. The outcome was COVID-19 death occurring during hospitalization or within 12 months post-discharge. Patients who remained alive throughout the one-year follow-up window were classified as cured. The analysis accounted for exact, right-censored, and interval-censored event times within the mixture cure model. Feature selection was performed using random survival forests and binary random forests, followed by interaction identification with decision trees. The cure component was modeled using logistic regression, while Weibull and Cox proportional hazards models characterized time-to-death among uncured individuals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Older age was a strong predictor of mortality, with hazard ratios of 2.30 for ages 65-74 and 3.32 for ages &gt; 74 compared with &lt; 45 years. Lower baseline oxygen saturation (&lt; 80%) increased the death hazard by 3.41-fold. Longer time to ICU admission increased mortality risk (HR = 1.17). Higher cumulative methylprednisolone dosage increased the odds of death by 2.06-fold, whereas cumulative ceftriaxone use reduced the death hazard by 49% among patients with oxygen saturation of 80-89%. Meropenem use-particularly without concurrent methylprednisolone-was associated with a significantly elevated hazard of death. Results were consistent across mixture cure and Cox models. Feature selection identified key predictive variables, including intubation duration, ICU duration, oxygen saturation, pulse and respiratory rates, BUN, and specific treatment interactions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The mixture cure framework distinguished between patients with long-term survival potential and those at sustained risk of mortality, revealing clinically relevant factors associated with both cure probability and death hazard. Although descriptive-not causal-these findings highlight subgroups with high resource needs and may inform conceptual approaches to planning ICU capacity, ventilator demand, and treatment prioritization during future pandemics. M","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A generalizable computational framework for integrating heterogeneous biomarkers into interpretable scalar risk representations.","authors":"Fernanda Oliveira Duarte, Mauro Masili, Luciana Camillo, Jaqueline Bianchi, Krissia Franco de Godoy, Bruna Dias de Lima Fragelli, Joice Margareth de Almeida Rodolpho, Juliana de Almeida Prado, Carlos Speglich, Fernanda de Freitas Aníbal","doi":"10.1186/s12911-026-03632-9","DOIUrl":"10.1186/s12911-026-03632-9","url":null,"abstract":"<p><p>A central challenge in biomedical informatics remains the integration of heterogeneous biomarker data into unified, interpretable, and clinically meaningful representations. In this study, we present a computational framework for the construction of unified scalar representations derived from multiple blood-based biomarkers, with the objective of capturing latent structures associated with mental health status. As a proof-of-principle, we instantiate this framework through the Mental Disorder Risk Index (MDRI), a scalar and dimensionless representation that integrates multiple biomarkers through an optimization-based and severity-sensitive aggregation strategy. The proposed framework includes linear and nonlinear formulations, with parameter estimation performed through a combination of deterministic and meta-heuristic optimization strategies. Model performance is evaluated using established metrics, mainly the Matthews correlation coefficient. The framework is applied to a dataset composed of eight biomarkers and respective mental health scores. The results indicate that stable scalar representations can be obtained within the normalized biomarker space, with robustness observed in several optimization trajectories, confirming the stability of the underlying latent structure of the biomarkers. Sensitivity analysis reveals heterogeneous contributions of the biomarkers, indicating that the proposed index captures non-trivial multivariate relationships. These findings establish the computational viability of the construction of unified biomarker-based indices and provide a structured basis for the development of interpretable, scalable, and generalizable representations of complex biomedical data.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13628972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph representation and learning for drug administration prediction.","authors":"Simone Cammarasana, Giuseppe Patanè","doi":"10.1186/s12911-026-03675-y","DOIUrl":"10.1186/s12911-026-03675-y","url":null,"abstract":"<p><strong>Background: </strong>The management of drug administration to patients promotes the customisation and accuracy of the treatments, reducing the risk of ineffective therapies and negative drug effects, and improving drug efficiency and management.</p><p><strong>Methods: </strong>We propose a graph representation learning model for predicting drug administration using the MIMIC-III database, which contains over 53K critical care admissions. We design a heterogeneous, weighted, directed, and multi-feature graph from patient demographics, diagnoses, and drug administration records. Our method uses a graph convolutional network to process node and edge features, predicting edge connections between patients and drugs. This choice enables us to analyse database properties, such as the relationship between drug administration and demographic classes, as well as the prediction accuracy with respect to drug occurrence.</p><p><strong>Results: </strong>We analyse the results in terms of correct, false positive, and false negative edge predictions of drug-patient administration. Our method has an accuracy of [Formula: see text] on the MIMIC-III database, with an F1-score of [Formula: see text]. We discuss the training results regarding convergence and execution time, analyse the accuracy on low-occurrence drugs, and compare our method with previous work.</p><p><strong>Conclusion: </strong>Previous work has focused primarily on query and classification tasks for feature extraction and diagnosis prediction, achieving comparable accuracy but is often limited to specific pathologies (e.g., diabetes), patient groups (e.g., pregnant women), or drug types (e.g., antibiotics). Our approach processes heterogeneous data encompassing various patient characteristics, pathologies, and drug types, providing a more comprehensive and scalable solution.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13629112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing semantic interoperability in precision medicine: converting OMOP CDM to Beacon v2 in the Spanish IMPaCT-Data project.","authors":"Manuel Rueda, Juan Manuel Ramírez-Anguita, Victoria López-Sánchez, Sergi Aguiló-Castillo, Maria Eugenia Gas López, Alberto Labarga, Miguel-Ángel Mayer, Javier Ripoll Esteve, Ivo G Gut","doi":"10.1186/s12911-026-03649-0","DOIUrl":"10.1186/s12911-026-03649-0","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate a file-based approach for converting OMOP CDM data into GA4GH Beacon v2 format and outline an alternative on-the-fly architecture for real-time access, with the aim of enhancing semantic interoperability within Spain's IMPaCT-Data program for personalized medicine.</p><p><strong>Materials and methods: </strong>We used the Convert-Pheno tool to transform OMOP CDM exports into Beacon v2 format using a file-based approach. In addition, we describe an architecture that connects PostgreSQL-based OMOP CDM instances directly to the Beacon v2 API, enabling real-time data access without intermediary text files.</p><p><strong>Results: </strong>We successfully converted OMOP CDM datasets from three research centers (CNAG, IIS La Fe, and HMar) to Beacon v2 format with high data completeness (≥ 99.9% across most domains).</p><p><strong>Discussion and conclusion: </strong>This study evaluates a file-based approach and describes an alternative on-the-fly architecture for integrating OMOP CDM data with Beacon v2. These approaches provide practical options for integrating OMOP CDM data with Beacon v2 in different deployment settings.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}