This paper explores the potential benefits and limitations of synthetic data (SD) in paediatrics, addressing the challenges of data scarcity and privacy concerns in paediatric research.
Methodology
A narrative literature review was conducted, searching PubMed and Scopus databases for relevant publications up to August 2025. The review focused on studies addressing the use, development, or application of SD in paediatric healthcare settings.
Findings
Synthetic data offers numerous benefits in paediatrics, including enhancing dataset diversity, protecting patient privacy, and enabling AI model development, especially in areas with limited real datasets such as rare diseases. Applications of SD in paediatrics span various fields, including neonatology, oncology, radiology, and neurodevelopmental disorders. However, challenges persist, including potential data bias, ensuring accuracy and quality, privacy concerns, and the lack of standardized guidelines for data generation and validation.
Conclusions and future directions
While SD demonstrates potential in specific paediatric applications, such as improving AI early warning systems and augmenting datasets for rare conditions, its use requires a structured, actionable framework for evaluation. Future efforts should focus through multi-stakeholder engagement, on developing paediatric-specific guidelines, ensuring fair and safe use of SD, and addressing unique aspects of child development in data synthesis.
{"title":"Synthetic data generation in paediatrics and paediatric nursing: what, how, and why?","authors":"Elisabetta Mezzalira , Maria Paola Boaro , Giulia Reggiani , Riccardo Biondi , Gastone Castellani , Raffaella Colombatti","doi":"10.1016/j.ijmedinf.2025.106236","DOIUrl":"10.1016/j.ijmedinf.2025.106236","url":null,"abstract":"<div><h3>Introduction</h3><div>This paper explores the potential benefits and limitations of synthetic data (SD) in paediatrics, addressing the challenges of data scarcity and privacy concerns in paediatric research.</div></div><div><h3>Methodology</h3><div>A narrative literature review was conducted, searching PubMed and Scopus databases for relevant publications up to August 2025. The review focused on studies addressing the use, development, or application of SD in paediatric healthcare settings.</div></div><div><h3>Findings</h3><div>Synthetic data offers numerous benefits in paediatrics, including enhancing dataset diversity, protecting patient privacy, and enabling AI model development, especially in areas with limited real datasets such as rare diseases. Applications of SD in paediatrics span various fields, including neonatology, oncology, radiology, and neurodevelopmental disorders. However, challenges persist, including potential data bias, ensuring accuracy and quality, privacy concerns, and the lack of standardized guidelines for data generation and validation.</div></div><div><h3>Conclusions and future directions</h3><div>While SD demonstrates potential in specific paediatric applications, such as improving AI early warning systems and augmenting datasets for rare conditions, its use requires a structured, actionable framework for evaluation. Future efforts should focus through multi-stakeholder engagement, on developing paediatric-specific guidelines, ensuring fair and safe use of SD, and addressing unique aspects of child development in data synthesis.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106236"},"PeriodicalIF":4.1,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913972","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}
Pub Date : 2025-12-25DOI: 10.1016/j.ijmedinf.2025.106243
{"title":"Reviewer Acknowledgement 2025","authors":"","doi":"10.1016/j.ijmedinf.2025.106243","DOIUrl":"10.1016/j.ijmedinf.2025.106243","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106243"},"PeriodicalIF":4.1,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145866390","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}
Pub Date : 2025-12-25DOI: 10.1016/j.ijmedinf.2025.106235
Wenhao Han , Xinyu Yang , Xin Li , Jiacheng Wang , Juan Liu , Wei Pang
Objective
Eye-tracking technology has been increasingly investigated as an objective approach for distinguishing individuals with Autism Spectrum Disorder (ASD) from typically developing (TD) individuals. Artificial intelligence and machine learning (ML) methods have been widely applied to support ASD diagnosis and treatment, and prior studies suggest that ML models leveraging eye-tracking data can achieve high diagnostic accuracy. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of machine-learning models using eye-tracking data to distinguish children and adolescents with ASD from TD peers.
Methods
We systematically searched PubMed, Embase, Web of Science, IEEE Xplore, Scopus, and the Cochrane Library from inception to August 3, 2025. We included studies that applied ML methods to eye-tracking data to distinguish children with ASD from TD children. We extracted data on participant characteristics, model performance, eye-tracking protocols, and machine-learning algorithms. The review protocol was registered in PROSPERO (CRD420251162462).
Results
We identified 1,045 records, of which 25 studies were included in the meta-analysis. The included studies comprised 2,319 participants, with sample sizes ranging from 32 to 529 per study. The pooled accuracy, sensitivity, and specificity of machine-learning models using eye-tracking data to distinguish children with ASD from TD children were 85 % (95 % CI, 81–89 %), 86 % (95 % CI, 82–89 %), and 86 % (95 % CI, 79–91 %), respectively. These results suggest that eye-tracking–based machine-learning approaches have good diagnostic performance for identifying ASD.
Conclusion
Eye-tracking–based machine-learning approaches show considerable potential for distinguishing children with ASD from TD children. However, the robustness and generalizability of these findings are limited by the lack of external validation, small sample sizes, and substantial between-study heterogeneity. To establish generalizability, future research should prioritize standardized eye-tracking paradigms and large-scale, prospective, multicenter study designs with external validation. Such efforts may facilitate the translation of these models into clinical practice as objective and efficient adjunctive screening tools.
{"title":"Machine learning-based diagnosis of autism spectrum disorder in children and adolescents using eye-tracking data: a systematic review and meta-analysis","authors":"Wenhao Han , Xinyu Yang , Xin Li , Jiacheng Wang , Juan Liu , Wei Pang","doi":"10.1016/j.ijmedinf.2025.106235","DOIUrl":"10.1016/j.ijmedinf.2025.106235","url":null,"abstract":"<div><h3>Objective</h3><div>Eye-tracking technology has been increasingly investigated as an objective approach for distinguishing individuals with Autism Spectrum Disorder (ASD) from typically developing (TD) individuals. Artificial intelligence and machine learning (ML) methods have been widely applied to support ASD diagnosis and treatment, and prior studies suggest that ML models leveraging eye-tracking data can achieve high diagnostic accuracy. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of machine-learning models using eye-tracking data to distinguish children and adolescents with ASD from TD peers.</div></div><div><h3>Methods</h3><div>We systematically searched PubMed, Embase, Web of Science, IEEE Xplore, Scopus, and the Cochrane Library from inception to August 3, 2025. We included studies that applied ML methods to eye-tracking data to distinguish children with ASD from TD children. We extracted data on participant characteristics, model performance, eye-tracking protocols, and machine-learning algorithms. The review protocol was registered in PROSPERO (CRD420251162462).</div></div><div><h3>Results</h3><div>We identified 1,045 records, of which 25 studies were included in the meta-analysis. The included studies comprised 2,319 participants, with sample sizes ranging from 32 to 529 per study. The pooled accuracy, sensitivity, and specificity of machine-learning models using eye-tracking data to distinguish children with ASD from TD children were 85 % (95 % CI, 81–89 %), 86 % (95 % CI, 82–89 %), and 86 % (95 % CI, 79–91 %), respectively. These results suggest that eye-tracking–based machine-learning approaches have good diagnostic performance for identifying ASD.</div></div><div><h3>Conclusion</h3><div>Eye-tracking–based machine-learning approaches show considerable potential for distinguishing children with ASD from TD children. However, the robustness and generalizability of these findings are limited by the lack of external validation, small sample sizes, and substantial between-study heterogeneity. To establish generalizability, future research should prioritize standardized eye-tracking paradigms and large-scale, prospective, multicenter study designs with external validation. Such efforts may facilitate the translation of these models into clinical practice as objective and efficient adjunctive screening tools.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106235"},"PeriodicalIF":4.1,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885672","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}
Pub Date : 2025-12-25DOI: 10.1016/j.ijmedinf.2025.106242
Janne Kommusaar , Silja Elunurm , Taridzo Chomutare , Mari Kangasniemi , Sanna Salanterä , Laura-Maria Peltonen
<div><h3>Background</h3><div>The rise of digital health data has expanded opportunities for data-driven innovation, yet privacy, legal and ethical barriers frame data sharing and collaborative artificial intelligence development. Federated Learning (FL) offers a privacy-preserving alternative, but current research considers mainly technical aspects. There is no end-to-end roadmap that integrates ethical, legal, technical and administrative principles tailored to FL projects in healthcare. This study addresses that gap by developing a roadmap to guide responsible and scalable FL research in the European context.</div></div><div><h3>Methods</h3><div>A multi-method participatory approach was used to develop a roadmap for scientific projects using FL on health data. The iterative process involved three phases. First, key questions were defined and existing evidence was explored through (i) a survey of domain experts (researchers, data governance specialists and infrastructure providers), (ii) a targeted literature review of FL applications in health research and (iii) systematic mapping of relevant EU-level legislation and policy frameworks. Evidence from these sources was synthesized to identify technical, organizational, legal and sustainability-related requirements for FL-based research. Second, preliminary roadmap components were refined through stakeholder engagement in an online workshop, where feasibility, scalability and sustainability considerations were explicitly discussed. Third, the roadmap was validated and iteratively refined by an expert panel through a structured group discussion, focusing on long-term sustainability, governance and transferability across research contexts. The process was carried out within a Baltic-Nordic collaboration in 2023–2025.</div></div><div><h3>Results</h3><div>The developed roadmap integrates ethical, legal, technical, administrative and sustainability-related considerations essential for applying FL to health data. It emphasizes the importance of multidisciplinary collaboration throughout the FL project lifecycle, with particular attention to long-term governance, scalability and reuse of infrastructures and practices. The process is structured into six phases: (1) Planning, (2) Execution refinement, (3) Data, (4) FL platform, (5) FL experiment and (6) Dissemination. Across these phases, sustainability is addressed through mechanisms such as regulatory alignment, shared governance models, capacity building and integration with existing research and health data infrastructures. By merging ethical, legal, technical and administrative aspects into a unified, end-to-end framework, the roadmap provides actionable, novel guidance beyond existing recommendations.</div></div><div><h3>Conclusions</h3><div>This work consolidates early lessons from FL in healthcare into a practical, step-by-step roadmap that integrates ethical, legal, technical and administrative aspects in the European context. By offering a shared
{"title":"A roadmap for federated learning projects using health data to guide sustainable artificial intelligence development in the European Union","authors":"Janne Kommusaar , Silja Elunurm , Taridzo Chomutare , Mari Kangasniemi , Sanna Salanterä , Laura-Maria Peltonen","doi":"10.1016/j.ijmedinf.2025.106242","DOIUrl":"10.1016/j.ijmedinf.2025.106242","url":null,"abstract":"<div><h3>Background</h3><div>The rise of digital health data has expanded opportunities for data-driven innovation, yet privacy, legal and ethical barriers frame data sharing and collaborative artificial intelligence development. Federated Learning (FL) offers a privacy-preserving alternative, but current research considers mainly technical aspects. There is no end-to-end roadmap that integrates ethical, legal, technical and administrative principles tailored to FL projects in healthcare. This study addresses that gap by developing a roadmap to guide responsible and scalable FL research in the European context.</div></div><div><h3>Methods</h3><div>A multi-method participatory approach was used to develop a roadmap for scientific projects using FL on health data. The iterative process involved three phases. First, key questions were defined and existing evidence was explored through (i) a survey of domain experts (researchers, data governance specialists and infrastructure providers), (ii) a targeted literature review of FL applications in health research and (iii) systematic mapping of relevant EU-level legislation and policy frameworks. Evidence from these sources was synthesized to identify technical, organizational, legal and sustainability-related requirements for FL-based research. Second, preliminary roadmap components were refined through stakeholder engagement in an online workshop, where feasibility, scalability and sustainability considerations were explicitly discussed. Third, the roadmap was validated and iteratively refined by an expert panel through a structured group discussion, focusing on long-term sustainability, governance and transferability across research contexts. The process was carried out within a Baltic-Nordic collaboration in 2023–2025.</div></div><div><h3>Results</h3><div>The developed roadmap integrates ethical, legal, technical, administrative and sustainability-related considerations essential for applying FL to health data. It emphasizes the importance of multidisciplinary collaboration throughout the FL project lifecycle, with particular attention to long-term governance, scalability and reuse of infrastructures and practices. The process is structured into six phases: (1) Planning, (2) Execution refinement, (3) Data, (4) FL platform, (5) FL experiment and (6) Dissemination. Across these phases, sustainability is addressed through mechanisms such as regulatory alignment, shared governance models, capacity building and integration with existing research and health data infrastructures. By merging ethical, legal, technical and administrative aspects into a unified, end-to-end framework, the roadmap provides actionable, novel guidance beyond existing recommendations.</div></div><div><h3>Conclusions</h3><div>This work consolidates early lessons from FL in healthcare into a practical, step-by-step roadmap that integrates ethical, legal, technical and administrative aspects in the European context. By offering a shared","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106242"},"PeriodicalIF":4.1,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879368","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}
Pub Date : 2025-12-24DOI: 10.1016/j.ijmedinf.2025.106241
Hongbing Liu , Ying Yao , Ce Zong , Ke Zhang , Haixu Zhao , Yuan Song , Yuming Xu , Yuan Gao
Objective
A substantial proportion of patients (12 %–25 %) with recent small subcortical infarction (RSSI) suffer poor functional outcomes at 3 months. Despite the identification of prognostic factors, a significant gap exists in predictive modeling. This study aimed to develop and validate machine learning models to accurately predict 3-month functional status in this patient population.
Methods
This multicenter study prospectively enrolled 1576 patients diagnosed with RSSI. The primary cohort (n = 1126) was randomly split into a training set (70 %) and an internal validation set (30 %). An independent external cohort (n = 450) was used for further validation. The primary outcome was an unfavorable functional status at 3 months, defined as a modified Rankin Scale (mRS) score ≥3. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was employed for feature selection from demographic, clinical, laboratory, and imaging variables. 8 supervised machine learning models were developed and compared. Model performance was rigorously evaluated in the validation cohorts using the Area Under the Receiver Operating Characteristic Curve (AUC) for discrimination, calibration curves for consistency, and Decision Curve Analysis (DCA) for clinical utility. The optimal model was interpreted using SHapley Additive exPlanations (SHAP).
Results
LASSO regression identified 8 features with non-zero coefficients for predicting outcomes: NIHSS, proximal RSSI (pRSSI), glucose, stress hyperglycemia ratio (SHR), neutrophil-to-lymphocyte ratio (NLR), age, systolic blood pressure (SBP), and LDL-C. Among the eight developed models, the CatBoost model demonstrated the best performance. It achieved the highest AUC in the training set (0.961), the internal validation cohort (0.940), and the external validation cohort (0.875). The CatBoost model also showed excellent calibration and provided the greatest net benefit across a wide range of threshold probabilities in DCA for both validation cohorts. SHAP analysis identified the NIHSS score as the most significant predictor of unfavorable outcomes, followed by pRSSI, Glucose, SHR, and NLR. A publicly accessible web tool based on the model is available at: https:// predictrssi.streamlit.app.
Conclusion
This study successfully developed and validated a robust CatBoost machine learning model that accurately predicts 3-month functional outcomes in patients with RSSI using eight readily accessible features. This model, which outperforms seven other machine learning algorithms, is available as a user-friendly web application to aid clinicians in early risk stratification and personalized patient management.
{"title":"Development and validation of a machine learning model to predict functional outcomes in patients with recent small subcortical infarction","authors":"Hongbing Liu , Ying Yao , Ce Zong , Ke Zhang , Haixu Zhao , Yuan Song , Yuming Xu , Yuan Gao","doi":"10.1016/j.ijmedinf.2025.106241","DOIUrl":"10.1016/j.ijmedinf.2025.106241","url":null,"abstract":"<div><h3>Objective</h3><div>A substantial proportion of patients (12 %–25 %) with recent small subcortical infarction (RSSI) suffer poor functional outcomes at 3 months. Despite the identification of prognostic factors, a significant gap exists in predictive modeling. This study aimed to develop and validate machine learning models to accurately predict 3-month functional status in this patient population.</div></div><div><h3>Methods</h3><div>This multicenter study prospectively enrolled 1576 patients diagnosed with RSSI. The primary cohort (n = 1126) was randomly split into a training set (70 %) and an internal validation set (30 %). An independent external cohort (n = 450) was used for further validation. The primary outcome was an unfavorable functional status at 3 months, defined as a modified Rankin Scale (mRS) score ≥3. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was employed for feature selection from demographic, clinical, laboratory, and imaging variables. 8 supervised machine learning models were developed and compared. Model performance was rigorously evaluated in the validation cohorts using the Area Under the Receiver Operating Characteristic Curve (AUC) for discrimination, calibration curves for consistency, and Decision Curve Analysis (DCA) for clinical utility. The optimal model was interpreted using SHapley Additive exPlanations (SHAP).</div></div><div><h3>Results</h3><div>LASSO regression identified 8 features with non-zero coefficients for predicting outcomes: NIHSS, proximal RSSI (pRSSI), glucose, stress hyperglycemia ratio (SHR), neutrophil-to-lymphocyte ratio (NLR), age, systolic blood pressure (SBP), and LDL-C. Among the eight developed models, the CatBoost model demonstrated the best performance. It achieved the highest AUC in the training set (0.961), the internal validation cohort (0.940), and the external validation cohort (0.875). The CatBoost model also showed excellent calibration and provided the greatest net benefit across a wide range of threshold probabilities in DCA for both validation cohorts. SHAP analysis identified the NIHSS score as the most significant predictor of unfavorable outcomes, followed by pRSSI, Glucose, SHR, and NLR. A publicly accessible web tool based on the model is available at: <span><span>https:// predictrssi.streamlit.app</span><svg><path></path></svg></span>.</div></div><div><h3>Conclusion</h3><div>This study successfully developed and validated a robust CatBoost machine learning model that accurately predicts 3-month functional outcomes in patients with RSSI using eight readily accessible features. This model, which outperforms seven other machine learning algorithms, is available as a user-friendly web application to aid clinicians in early risk stratification and personalized patient management.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106241"},"PeriodicalIF":4.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145851363","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}
Pub Date : 2025-12-24DOI: 10.1016/j.ijmedinf.2025.106238
Yuto Arai , Yoshiyasu Takefuji
{"title":"Limitations of SHAP-based interpretability in sepsis progression models and paths to more robust feature validation","authors":"Yuto Arai , Yoshiyasu Takefuji","doi":"10.1016/j.ijmedinf.2025.106238","DOIUrl":"10.1016/j.ijmedinf.2025.106238","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106238"},"PeriodicalIF":4.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841723","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}
Pub Date : 2025-12-23DOI: 10.1016/j.ijmedinf.2025.106232
Petra Hospodková , Jan Bruthans , Adéla Englová
Introduction
The Patient Summary (PS), a standardized subset of the electronic health record is designed to provide essential patient information for use in emergencies, unplanned care, and cross-border healthcare. While its technical development has progressed across Europe, little is known about real-world PS adoption and physician perceptions at the national level. This study explores the awareness, usage, and perceived barriers to the PS adoption among Czech physicians.
Methods
A cross-sectional online survey was distributed to all registered physicians in the Czech Republic between February and March 2025. The questionnaire assessed demographic characteristics, PS usage patterns, perceived benefits and barriers, and alignment with clinical practice. Descriptive statistics were calculated, and non-parametric tests (Wilcoxon rank-sum, Kruskal–Wallis) were used to examine differences by years of experience and medical specialty.
Results
A total of 1,739 responses were received (response rate: 4.14 %). Most respondents (66.4 %) reported not using the PS at all, and 72.1 % were unaware that their electronic medical record could be connected to the National Contact Point for eHealth. Only 1.7 % reported a current connection. There was no significant difference in PS use by years of clinical experience (P = 0.391), but a significant difference was observed across specialties (P < 0.001), with the highest usage reported in intensive care medicine and internal medicine.
Discussion and conclusion
Despite recognized benefits, PS usage remains low in the Czech Republic, largely due to limited awareness and system integration. Targeted policy measures, improved communication, and enhanced digital training are needed to support effective adoption.
{"title":"Physicians’ attitudes toward the patient summary in the Czech Republic: A national cross-sectional survey on awareness, use, and barriers","authors":"Petra Hospodková , Jan Bruthans , Adéla Englová","doi":"10.1016/j.ijmedinf.2025.106232","DOIUrl":"10.1016/j.ijmedinf.2025.106232","url":null,"abstract":"<div><h3>Introduction</h3><div>The Patient Summary (PS), a standardized subset of the electronic health record is designed to provide essential patient information for use in emergencies, unplanned care, and cross-border healthcare. While its technical development has progressed across Europe, little is known about real-world PS adoption and physician perceptions at the national level. This study explores the awareness, usage, and perceived barriers to the PS adoption among Czech physicians.</div></div><div><h3>Methods</h3><div>A cross-sectional online survey was distributed to all registered physicians in the Czech Republic between February and March 2025. The questionnaire assessed demographic characteristics, PS usage patterns, perceived benefits and barriers, and alignment with clinical practice. Descriptive statistics were calculated, and non-parametric tests (Wilcoxon rank-sum, Kruskal–Wallis) were used to examine differences by years of experience and medical specialty.</div></div><div><h3>Results</h3><div>A total of 1,739 responses were received (response rate: 4.14 %). Most respondents (66.4 %) reported not using the PS at all, and 72.1 % were unaware that their electronic medical record could be connected to the National Contact Point for eHealth. Only 1.7 % reported a current connection. There was no significant difference in PS use by years of clinical experience (P = 0.391), but a significant difference was observed across specialties (P < 0.001), with the highest usage reported in intensive care medicine and internal medicine.</div></div><div><h3>Discussion and conclusion</h3><div>Despite recognized benefits, PS usage remains low in the Czech Republic, largely due to limited awareness and system integration. Targeted policy measures, improved communication, and enhanced digital training are needed to support effective adoption.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106232"},"PeriodicalIF":4.1,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145829159","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}
Pub Date : 2025-12-23DOI: 10.1016/j.ijmedinf.2025.106239
Priyadharsini Ramamurthy , Zheng Han , Dursun Delen , Zhuqi Miao , Andrew Gin , Xiao Luo , William Paiva
Background
Traumatic brain injury (TBI) is a major risk factor for neurological disorders, including post-traumatic epilepsy (PTE), a debilitating condition associated with significant long-term consequences. The prognosis of PTE occurrence remains challenging due to the complex pathophysiology of PTE and the impracticality of traditional blood biomarker- or imaging-based screening for large populations. This study proposes a graph-based deep learning approach that leverages electronic health records (EHR) to enhance the predictive assessment of PTE risk.
Methods
We utilized Oracle Real-World Data (ORWD) to construct a Heterogeneous Graph Attention Network (HeteroGAT) that contains patient and diagnosis nodes, with temporal information represented using patients-to-diagnosis edges, and comorbidity connectivity embedded using diagnosis-to-diagnosis edges. The HeteroGAT was trained on a cohort of 1,598,998 TBI-only patients and 102,687 individuals who developed epilepsy after TBI. Model performance was evaluated using sensitivity, specificity, macro F1-score, and area under the receiver operating characteristic curve (AUC-ROC), benchmarked against traditional machine learning models. Attention scores of nodes were used to evaluate node importance. The capabilities of the HeteroGATs trained to differentiate early vs late PTE patients following TBI were also assessed.
Results
HeteroGAT significantly outperformed conventional models in PTE prediction by effectively integrating demographic data and comorbidity profiles spanning from 20 to 500 distinct conditions. The model’s multi-head attention mechanisms, in combination with learned comorbidity connectivity, enhanced its ability to capture complex dependencies within EHR data. HeteroGAT achieved an AUC-ROC of 0.80, outperforming the best-performing traditional model, random forest (AUC-ROC = 0.77). HeteroGAT also demonstrated capabilities in differentiating early and late PTEs. Ranking of nodes based on attention scores also identified predictors of PTE that are clinically relevant.
Conclusion
By modeling sparse EHR data through patient encounter embeddings, HeteroGAT effectively captures temporal and relational patterns in comorbidities critical for PTE prediction. Our findings highlight the potential of graph-based deep learning models, synergized with large-scale EHR data, in advancing personalized risk assessment, ultimately addressing the urgent need for more precise and proactive management of PTE in TBI patients.
{"title":"Graph attention network with comorbidity connectivity embedding for post-traumatic epilepsy risk prediction using sparse time-series electronic health records","authors":"Priyadharsini Ramamurthy , Zheng Han , Dursun Delen , Zhuqi Miao , Andrew Gin , Xiao Luo , William Paiva","doi":"10.1016/j.ijmedinf.2025.106239","DOIUrl":"10.1016/j.ijmedinf.2025.106239","url":null,"abstract":"<div><h3>Background</h3><div>Traumatic brain injury (TBI) is a major risk factor for neurological disorders, including post-traumatic epilepsy (PTE), a debilitating condition associated with significant long-term consequences. The prognosis of PTE occurrence remains challenging due to the complex pathophysiology of PTE and the impracticality of traditional blood biomarker- or imaging-based screening for large populations. This study proposes a graph-based deep learning approach that leverages electronic health records (EHR) to enhance the predictive assessment of PTE risk.</div></div><div><h3>Methods</h3><div>We utilized Oracle Real-World Data (ORWD) to construct a Heterogeneous Graph Attention Network (HeteroGAT) that contains patient and diagnosis nodes, with temporal information represented using patients-to-diagnosis edges, and comorbidity connectivity embedded using diagnosis-to-diagnosis edges. The HeteroGAT was trained on a cohort of 1,598,998 TBI-only patients and 102,687 individuals who developed epilepsy after TBI. Model performance was evaluated using sensitivity, specificity, macro F1-score, and area under the receiver operating characteristic curve (AUC-ROC), benchmarked against traditional machine learning models. Attention scores of nodes were used to evaluate node importance. The capabilities of the HeteroGATs trained to differentiate early vs late PTE patients following TBI were also assessed.</div></div><div><h3>Results</h3><div>HeteroGAT significantly outperformed conventional models in PTE prediction by effectively integrating demographic data and comorbidity profiles spanning from 20 to 500 distinct conditions. The model’s multi-head attention mechanisms, in combination with learned comorbidity connectivity, enhanced its ability to capture complex dependencies within EHR data. HeteroGAT achieved an AUC-ROC of 0.80, outperforming the best-performing traditional model, random forest (AUC-ROC = 0.77). HeteroGAT also demonstrated capabilities in differentiating early and late PTEs. Ranking of nodes based on attention scores also identified predictors of PTE that are clinically relevant.</div></div><div><h3>Conclusion</h3><div>By modeling sparse EHR data through patient encounter embeddings, HeteroGAT effectively captures temporal and relational patterns in comorbidities critical for PTE prediction. Our findings highlight the potential of graph-based deep learning models, synergized with large-scale EHR data, in advancing personalized risk assessment, ultimately addressing the urgent need for more precise and proactive management of PTE in TBI patients.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106239"},"PeriodicalIF":4.1,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885680","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}
Pub Date : 2025-12-23DOI: 10.1016/j.ijmedinf.2025.106240
Jun Guo , Fan Xiong , Baisheng Sun , Mingxing Lei , Yong Qin
Background
Sepsis represents a life-threatening complication in severe orthopedic trauma, significantly increasing short-term mortality risk. Despite the clinical urgency for early prognosis assessment, current predictive tools remain inadequate. To address this gap, this study used a machine learning (ML)-based framework for mortality risk stratification in this high-risk population.
Methods
This retrospective cohort study established ML models to predict 30-day all-cause mortality in critically ill patients with orthopedic trauma and sepsis. Data from 2,060 eligible patients were extracted from the intensive care unit (ICU) of Beth Israel Deaconess Medical Center (2008–2019) in the United State and randomly split into training (80 %) and internal validation (20 %) sets. After handling missing data and addressing class imbalance, seven ML algorithms (including CatBoost [Categorical Boosting], RF [Random Forest], and SVM [Support Vector Machine]) were trained and optimized using 10-fold cross-validation. Model performance was assessed based on discrimination (AUC [Area Under the Curve], accuracy, F1-score), calibration (Brier score, calibration slope), and clinical utility. The top-performing models were further validated on an independent external Chinese cohort (n = 273, 2020–2024).
Results
The study cohort had a mean age of 62.8 years and a 30-day mortality rate of 19.9 % (410/2060). Non-survivors were significantly older, had a higher comorbidity burden, and more severe physiological derangements. The LASSO analysis identified 16 prognostic variables, with age, hematologic parameters (RDW, WBC), SOFA scores, hemodynamic measures (SBP), and antihypertensive therapy emerging as significant predictors. Among all models, the CatBoost algorithm demonstrated superior performance in the internal validation set, achieving the highest AUC (0.955), accuracy (0.884), and F1-score (0.878), along with excellent calibration (Brier score: 0.081). A soft voting ensemble model, integrating the top three algorithms (CatBoost, RF, SVM), was subsequently constructed. In external validation, this ensemble model generalized robustly, maintaining strong discrimination (AUC: 0.842, Accuracy: 0.737) and calibration (Brier score: 0.173), outperforming the standalone CatBoost model. SHapley Additive exPlanations analysis provided interpretable, individualized risk assessments.
Conclusions
This study trains, optimizes, and evaluates a high-performing ML-based prediction model for 30-day mortality in patients with critical orthopedic trauma and sepsis. The CatBoost model and the soft voting ensemble, particularly the latter, demonstrates strong generalizability and clinical utility, offering a potential tool for early risk stratification and personalized management in this vulnerable population.
脓毒症是严重骨科创伤中一种危及生命的并发症，显著增加短期死亡风险。尽管临床迫切需要早期预后评估，但目前的预测工具仍然不足。为了解决这一差距，本研究在这一高危人群中使用了基于机器学习（ML）的死亡率风险分层框架。方法回顾性队列研究建立ML模型，预测骨科创伤合并脓毒症危重患者30天全因死亡率。从美国贝斯以色列女执事医疗中心（Beth Israel Deaconess Medical Center）重症监护室（ICU）提取2060例符合条件的患者数据（2008-2019），随机分为训练组（80%）和内部验证组（20%）。在处理缺失数据和解决类不平衡问题后，使用10倍交叉验证对七种ML算法（包括CatBoost [Categorical Boosting]， RF [Random Forest]和SVM [Support Vector Machine]）进行了训练和优化。模型性能评估基于鉴别（AUC[曲线下面积]，准确性，f1评分），校准（Brier评分，校准斜率）和临床实用性。在一个独立的外部中国队列（n = 273, 2020-2024）上进一步验证了表现最好的模型。结果研究队列的平均年龄为62.8岁，30天死亡率为19.9%（410/2060）。非幸存者明显更老，有更高的合并症负担，更严重的生理紊乱。LASSO分析确定了16个预后变量，其中年龄、血液学参数（RDW、WBC）、SOFA评分、血流动力学测量（SBP）和抗高血压治疗成为重要的预测因素。在所有模型中，CatBoost算法在内部验证集中表现优异，AUC（0.955）、准确率（0.884）和f1评分（0.878）最高，校准效果也很好（Brier评分：0.081）。随后构建了一个软投票集成模型，该模型集成了前三种算法（CatBoost、RF、SVM）。在外部验证中，该集成模型具有鲁棒性泛化，保持了较强的判别性（AUC: 0.842，准确度：0.737）和校准性（Brier评分：0.173），优于独立的CatBoost模型。SHapley加性解释分析提供了可解释的、个性化的风险评估。本研究训练、优化并评估了一种高性能的基于ml的骨科创伤和脓毒症患者30天死亡率预测模型。CatBoost模型和软投票集合，特别是后者，显示出很强的通用性和临床实用性，为这一弱势群体的早期风险分层和个性化管理提供了潜在的工具。
{"title":"Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study","authors":"Jun Guo , Fan Xiong , Baisheng Sun , Mingxing Lei , Yong Qin","doi":"10.1016/j.ijmedinf.2025.106240","DOIUrl":"10.1016/j.ijmedinf.2025.106240","url":null,"abstract":"<div><h3>Background</h3><div>Sepsis represents a life-threatening complication in severe orthopedic trauma, significantly increasing short-term mortality risk. Despite the clinical urgency for early prognosis assessment, current predictive tools remain inadequate. To address this gap, this study used a machine learning (ML)-based framework for mortality risk stratification in this high-risk population.</div></div><div><h3>Methods</h3><div>This retrospective cohort study established ML models to predict 30-day all-cause mortality in critically ill patients with orthopedic trauma and sepsis. Data from 2,060 eligible patients were extracted from the intensive care unit (ICU) of Beth Israel Deaconess Medical Center (2008–2019) in the United State and randomly split into training (80 %) and internal validation (20 %) sets. After handling missing data and addressing class imbalance, seven ML algorithms (including CatBoost [Categorical Boosting], RF [Random Forest], and SVM [Support Vector Machine]) were trained and optimized using 10-fold cross-validation. Model performance was assessed based on discrimination (AUC [Area Under the Curve], accuracy, F1-score), calibration (Brier score, calibration slope), and clinical utility. The top-performing models were further validated on an independent external Chinese cohort (n = 273, 2020–2024).</div></div><div><h3>Results</h3><div>The study cohort had a mean age of 62.8 years and a 30-day mortality rate of 19.9 % (410/2060). Non-survivors were significantly older, had a higher comorbidity burden, and more severe physiological derangements. The LASSO analysis identified 16 prognostic variables, with age, hematologic parameters (RDW, WBC), SOFA scores, hemodynamic measures (SBP), and antihypertensive therapy emerging as significant predictors. Among all models, the CatBoost algorithm demonstrated superior performance in the internal validation set, achieving the highest AUC (0.955), accuracy (0.884), and F1-score (0.878), along with excellent calibration (Brier score: 0.081). A soft voting ensemble model, integrating the top three algorithms (CatBoost, RF, SVM), was subsequently constructed. In external validation, this ensemble model generalized robustly, maintaining strong discrimination (AUC: 0.842, Accuracy: 0.737) and calibration (Brier score: 0.173), outperforming the standalone CatBoost model. SHapley Additive exPlanations analysis provided interpretable, individualized risk assessments.</div></div><div><h3>Conclusions</h3><div>This study trains, optimizes, and evaluates a high-performing ML-based prediction model for 30-day mortality in patients with critical orthopedic trauma and sepsis. The CatBoost model and the soft voting ensemble, particularly the latter, demonstrates strong generalizability and clinical utility, offering a potential tool for early risk stratification and personalized management in this vulnerable population.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106240"},"PeriodicalIF":4.1,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885681","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}
Pub Date : 2025-12-22DOI: 10.1016/j.ijmedinf.2025.106234
Zheqing Li , Liyang Tang , Yin Li , Yuanyuan Dang , Lin Yao
Context
Internet hospitals have emerged as a digital innovation in healthcare, optimizing resource allocation and enhancing patient experience. They also support hierarchical diagnosis and treatment and contribute to the Healthy China initiative.
Objectives
To establish a comprehensive evaluation system to promote the sustainable development of Internet hospitals.
Methods
A systematic review of literature related to the evaluation of Internet-based healthcare services was conducted. Using Web of Science and CNKI as data sources, studies published between 2015 and 2024 were screened based on predefined criteria, focusing on high-quality journals and research reports. The selected literature was coded and analyzed across four dimensions: patient services, doctor services, management services, and information security.
Results
The final analysis included 34 papers, with 25 mentioning patient services indicators, 20 mentioning doctor services indicators, 18 mentioning medical services process management indicators, and 9 mentioning information security. This study identifies key evaluation indicators and examines their interrelationships, highlighting potential systemic risks from localized optimizations.
Conclusion
This review analyzed Internet hospital evaluation across patient services, doctor services, services management, and information security. While it highlights potential efficiency gains, it notes the lack of comprehensive indicators, limiting assessment and improvement. For sustainable development, a more comprehensive evaluation system should integrate multi-stakeholder perspectives (patients, doctors, institutions), address systemic risks from localized optimization, and incorporate coordinated policy considerations.
背景：互联网医院作为医疗领域的数字化创新，优化了资源配置，提升了患者体验。他们还支持分级诊疗，为“健康中国”倡议做出贡献。目的：建立促进互联网医院可持续发展的综合评价体系。方法：系统回顾与互联网医疗服务评价相关的文献。以Web of Science和CNKI为数据来源，根据预先设定的标准筛选2015 - 2024年间发表的研究，重点筛选高质量的期刊和研究报告。对选定的文献进行编码，并从四个方面进行分析：患者服务、医生服务、管理服务和信息安全。结果：最终分析共纳入34篇论文，其中患者服务指标25篇，医生服务指标20篇，医疗服务流程管理指标18篇，信息安全9篇。本研究确定了关键的评估指标，并检查了它们之间的相互关系，突出了局部优化带来的潜在系统性风险。结论：本综述分析了互联网医院在患者服务、医生服务、服务管理和信息安全方面的评价。虽然它强调了潜在的效率提高，但它指出缺乏全面的指标，限制了评估和改进。为了实现可持续发展，更全面的评价体系应该整合多方利益相关者（患者、医生、机构）的视角，从局部优化中解决系统性风险，并纳入协调一致的政策考虑。
{"title":"A review of evaluation system for Internet hospitals","authors":"Zheqing Li , Liyang Tang , Yin Li , Yuanyuan Dang , Lin Yao","doi":"10.1016/j.ijmedinf.2025.106234","DOIUrl":"10.1016/j.ijmedinf.2025.106234","url":null,"abstract":"<div><h3>Context</h3><div>Internet hospitals have emerged as a digital innovation in healthcare, optimizing resource allocation and enhancing patient experience. They also support hierarchical diagnosis and treatment and contribute to the Healthy China initiative.</div></div><div><h3>Objectives</h3><div>To establish a comprehensive evaluation system to promote the sustainable development of Internet hospitals.</div></div><div><h3>Methods</h3><div>A systematic review of literature related to the evaluation of Internet-based healthcare services was conducted. Using Web of Science and CNKI as data sources, studies published between 2015 and 2024 were screened based on predefined criteria, focusing on high-quality journals and research reports. The selected literature was coded and analyzed across four dimensions: patient services, doctor services, management services, and information security.</div></div><div><h3>Results</h3><div>The final analysis included 34 papers, with 25 mentioning patient services indicators, 20 mentioning doctor services indicators, 18 mentioning medical services process management indicators, and 9 mentioning information security. This study identifies key evaluation indicators and examines their interrelationships, highlighting potential systemic risks from localized optimizations.</div></div><div><h3>Conclusion</h3><div>This review analyzed Internet hospital evaluation across patient services, doctor services, services management, and information security. While it highlights potential efficiency gains, it notes the lack of comprehensive indicators, limiting assessment and improvement. For sustainable development, a more comprehensive evaluation system should integrate multi-stakeholder perspectives (patients, doctors, institutions), address systemic risks from localized optimization, and incorporate coordinated policy considerations.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106234"},"PeriodicalIF":4.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913911","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}
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