International Journal of Medical Informatics最新文献_第9页

Reviewer Acknowledgement 2025 审稿人致谢2025。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-25 DOI: 10.1016/j.ijmedinf.2025.106243

引用次数: 0

Machine learning-based diagnosis of autism spectrum disorder in children and adolescents using eye-tracking data: a systematic review and meta-analysis 基于机器学习的儿童和青少年自闭症谱系障碍的眼动追踪诊断：系统回顾和荟萃分析

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-25 DOI: 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.

眼动追踪技术作为一种区分自闭症谱系障碍（ASD）和正常发育（TD）个体的客观方法，已得到越来越多的研究。人工智能和机器学习（ML）方法已广泛应用于支持ASD的诊断和治疗，先前的研究表明，利用眼动追踪数据的ML模型可以实现较高的诊断准确性。本系统综述和荟萃分析旨在评估使用眼动追踪数据的机器学习模型的诊断性能，以区分自闭症儿童和青少年与TD同龄人。方法系统检索PubMed、Embase、Web of Science、IEEE explore、Scopus、Cochrane Library自成立至2025年8月3日的文献。我们纳入了将ML方法应用于眼动追踪数据以区分ASD儿童和TD儿童的研究。我们提取了参与者特征、模型性能、眼动追踪协议和机器学习算法的数据。该审查方案已在PROSPERO注册（CRD420251162462）。结果我们确定了1045条记录，其中25项研究被纳入meta分析。纳入的研究包括2319名参与者，每项研究的样本量从32到529不等。使用眼动追踪数据的机器学习模型区分ASD儿童和TD儿童的总准确性、灵敏度和特异性分别为85% （95% CI, 81 - 89%）、86% （95% CI, 82 - 89%）和86% （95% CI, 79 - 91%）。这些结果表明，基于眼动追踪的机器学习方法在识别ASD方面具有良好的诊断性能。结论基于眼动追踪的机器学习方法在区分ASD儿童和TD儿童方面具有很大的潜力。然而，这些发现的稳健性和普遍性受到缺乏外部验证、小样本量和大量研究间异质性的限制。为了建立普遍性，未来的研究应优先考虑标准化的眼动追踪范式和具有外部验证的大规模、前瞻性、多中心研究设计。这些努力可能有助于将这些模型转化为临床实践，作为客观有效的辅助筛查工具。

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引用次数: 0

A roadmap for federated learning projects using health data to guide sustainable artificial intelligence development in the European Union 使用健康数据指导欧洲联盟可持续人工智能发展的联合学习项目路线图。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-25 DOI: 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

背景：数字健康数据的兴起扩大了数据驱动创新的机会，但隐私、法律和道德障碍阻碍了数据共享和协作式人工智能的发展。联邦学习（FL）提供了一种保护隐私的替代方案，但目前的研究主要考虑技术方面的问题。目前还没有一个端到端的路线图，可以整合为医疗保健领域的FL项目量身定制的道德、法律、技术和管理原则。本研究通过制定路线图来指导欧洲范围内负责任和可扩展的FL研究，从而解决了这一差距。方法：采用多方法参与式方法，为利用FL处理卫生数据的科学项目制定路线图。迭代过程包括三个阶段。首先，通过(i)对领域专家（研究人员、数据治理专家和基础设施提供商）的调查，（ii）对FL在卫生研究中的应用进行有针对性的文献综述，以及（iii）系统地绘制相关欧盟层面的立法和政策框架，定义了关键问题并探索了现有证据。综合了这些来源的证据，以确定基于fl的研究的技术、组织、法律和可持续性相关要求。其次，通过在线研讨会的利益相关者参与，对初步路线图组件进行了细化，其中明确讨论了可行性、可扩展性和可持续性考虑因素。第三，路线图由专家小组通过结构化的小组讨论进行验证和迭代完善，重点关注长期可持续性、治理和跨研究背景的可转移性。该过程是在2023-2025年的波罗的海-北欧合作中进行的。结果：制定的路线图整合了将FL应用于健康数据所必需的伦理、法律、技术、行政和可持续性相关考虑因素。它强调了在整个FL项目生命周期中多学科协作的重要性，特别关注基础结构和实践的长期治理、可伸缩性和重用。该过程分为六个阶段：(1)计划，(2)执行改进，(3)数据，(4)FL平台，(5)FL实验和(6)传播。在这些阶段，可持续性是通过监管协调、共享治理模式、能力建设以及与现有研究和卫生数据基础设施的整合等机制来解决的。通过将道德、法律、技术和管理方面合并到一个统一的端到端框架中，路线图提供了超越现有建议的可操作的新颖指导。结论：这项工作将早期FL在医疗保健方面的经验教训整合到一个实用的、逐步的路线图中，该路线图在欧洲背景下整合了伦理、法律、技术和行政方面。通过为不同的利益相关者提供共享框架，它支持跨医疗保健系统的更值得信赖、可扩展和兼容的人工智能协作。

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引用次数: 0

Development and validation of a machine learning model to predict functional outcomes in patients with recent small subcortical infarction 机器学习模型的开发和验证，以预测近期小皮质下梗死患者的功能结局。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-24 DOI: 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.

目的：相当比例的近期小皮质下梗死（RSSI）患者（12% - 25%）在3个月时功能预后较差。尽管确定了预后因素，但在预测建模方面存在显着差距。本研究旨在开发和验证机器学习模型，以准确预测该患者群体3个月的功能状态。方法：本多中心研究前瞻性纳入1576例诊断为RSSI的患者。主要队列（n = 1126）随机分为训练集（70%）和内部验证集（30%）。一个独立的外部队列（n = 450）被用于进一步验证。主要结局是3个月时不良的功能状态，定义为修改的Rankin量表（mRS）评分≥3。最小绝对收缩和选择算子（LASSO）逻辑回归模型用于从人口统计学、临床、实验室和影像学变量中进行特征选择。开发并比较了8个监督机器学习模型。在验证队列中，使用受试者工作特征曲线下面积（AUC）进行甄别，使用校准曲线进行一致性，使用决策曲线分析（DCA）进行临床效用，严格评估模型的性能。最优模型采用SHapley加性解释（SHAP）进行解释。结果：LASSO回归确定了8个预测结果的非零系数特征：NIHSS、近端RSSI （pRSSI）、葡萄糖、应激性高血糖比（SHR）、中性粒细胞与淋巴细胞比（NLR）、年龄、收缩压（SBP）和LDL-C。在八个已开发的模型中，CatBoost模型表现出最好的性能。在训练集（0.961）、内部验证队列（0.940）和外部验证队列（0.875）中AUC最高。CatBoost模型也显示出出色的校准，并在DCA的广泛阈值概率范围内为两个验证队列提供了最大的净收益。SHAP分析发现NIHSS评分是不良结果的最重要预测因子，其次是pRSSI、葡萄糖、SHR和NLR。结论：本研究成功开发并验证了一个强大的CatBoost机器学习模型，该模型可以使用8个易于访问的特征准确预测RSSI患者3个月的功能结果。该模型优于其他7种机器学习算法，可作为用户友好的web应用程序，帮助临床医生进行早期风险分层和个性化患者管理。

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引用次数: 0

Limitations of SHAP-based interpretability in sepsis progression models and paths to more robust feature validation 基于shap的脓毒症进展模型可解释性的局限性和更稳健的特征验证途径

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-24 DOI: 10.1016/j.ijmedinf.2025.106238

Yuto Arai , Yoshiyasu Takefuji

引用次数: 0

Physicians’ attitudes toward the patient summary in the Czech Republic: A national cross-sectional survey on awareness, use, and barriers 捷克共和国医生对病人总结的态度：一项关于意识、使用和障碍的全国性横断面调查。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-23 DOI: 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.

简介：患者摘要（PS）是电子健康记录的一个标准化子集，旨在为紧急情况、计划外护理和跨境医疗保健提供必要的患者信息。虽然它的技术发展在整个欧洲都取得了进展，但人们对现实世界中PS的采用和国家层面上医生的看法知之甚少。本研究探讨了意识，使用和感知障碍的PS采用捷克医生。方法：在2025年2月至3月期间，对捷克共和国所有注册医生进行横断面在线调查。调查问卷评估了人口统计学特征、PS使用模式、可感知的益处和障碍，以及与临床实践的一致性。计算描述性统计数据，并使用非参数检验（Wilcoxon秩和，Kruskal-Wallis）来检查经验年限和医学专业的差异。结果：共收到问卷1739份，回复率4.14%。大多数受访者（66.4%）表示根本没有使用PS， 72.1%的人不知道他们的电子病历可以连接到国家电子健康联络点。只有1.7%的人报告有当前连接。不同临床年限对PS的使用差异无统计学意义（P = 0.391），但不同专科对PS的使用差异有统计学意义（P < 0.001），其中重症监护医学和内科使用率最高。讨论和结论：尽管公认的好处，PS使用率仍然很低在捷克共和国，主要是由于有限的认识和系统集成。需要有针对性的政策措施、改进的沟通和加强的数字培训来支持有效采用。

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引用次数: 0

Graph attention network with comorbidity connectivity embedding for post-traumatic epilepsy risk prediction using sparse time-series electronic health records 带共病连通性嵌入的图注意网络用于稀疏时间序列电子病历创伤后癫痫风险预测

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-23 DOI: 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.

背景外伤性脑损伤（TBI）是神经系统疾病的主要危险因素，包括创伤后癫痫（PTE），这是一种与严重的长期后果相关的衰弱性疾病。由于PTE的病理生理复杂，以及传统的基于血液生物标志物或影像学筛查在大人群中的不实用性，PTE的预后仍然具有挑战性。本研究提出了一种基于图形的深度学习方法，该方法利用电子健康记录（EHR）来增强PTE风险的预测评估。方法利用Oracle真实世界数据（ORWD）构建了包含患者和诊断节点的异构图关注网络（HeteroGAT），其中时间信息使用患者到诊断边表示，共病连接使用诊断到诊断边嵌入。HeteroGAT在1,598,998名TBI患者和102,687名TBI后发生癫痫的患者中进行了培训。以传统机器学习模型为基准，使用灵敏度、特异性、宏观f1评分和受试者工作特征曲线下面积（AUC-ROC）来评估模型的性能。节点的注意分数用于评价节点的重要性。此外，还评估了经过训练的heterogat区分TBI后早期和晚期PTE患者的能力。结果通过有效整合人口统计数据和20 - 500种不同疾病的共病概况，sheterogat在PTE预测方面明显优于传统模型。该模型的多头注意机制与习得的共病连接相结合，增强了其捕获EHR数据中复杂依赖关系的能力。HeteroGAT的AUC-ROC为0.80，优于表现最好的传统模型随机森林（AUC-ROC = 0.77）。此外，HeteroGAT还具有区分早期和晚期pte的能力。基于注意力得分的节点排名也确定了临床相关的PTE预测因子。结论：通过患者遭遇嵌入对稀疏的EHR数据进行建模，HeteroGAT可以有效捕获对PTE预测至关重要的合并症的时间和关系模式。我们的研究结果强调了基于图的深度学习模型与大规模电子病历数据协同的潜力，在推进个性化风险评估方面，最终解决了对TBI患者PTE更精确和主动管理的迫切需求。

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引用次数: 0

Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study 集成机器学习用于感染性骨科创伤早期死亡风险分层：一项国际队列研究

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-23 DOI: 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。

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引用次数: 0

A review of evaluation system for Internet hospitals 互联网医院评价体系述评。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-22 DOI: 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。

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引用次数: 0

Advancing healthcare with large language models: A scoping review of applications and future directions 使用大型语言模型推进医疗保健：对应用程序和未来方向的范围审查。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2025-12-22 DOI: 10.1016/j.ijmedinf.2025.106231

Zhihong Zhang , Mohamad Javad Momeni Nezhad , Seyed Mohammad Bagher Hosseini , Ali Zolnour , Zahra Zonour , Seyedeh Mahdis Hosseini , Maxim Topaz , Maryam Zolnoori

Background

The release of ChatGPT has spurred the widespread adoption of generative large language models (LLMs) in healthcare. This scoping review systematically examines their use in healthcare.

Methods

A systematic search was conducted using PubMed, a comprehensive and representative database on biomedical and health science, to identify studies published between January 1, 2023, and July 30, 2024. Studies were included if they assessed the performance of generative LLMs in healthcare applications; review or perspective articles were excluded.

Results

A total of 415 studies were included, with a significant increase in publications observed after April 2023. Generative LLMs were applied across various medical specialties, primarily supporting clinical decision-making (26.7%) and providing patient information (23.9%). Smaller proportions were focused on professional education and training (18.1%), research (16.1%), and workflow support (12.5%). These applications were mainly supported by three key NLP tasks: question answering (36.1%), text classification (27.5%), and text generation (26.3%). Public datasets appeared in 20% of studies, and 15% used clinical patient data. Of the 98 LLMs used, GPT-4 (51.3%), GPT-3.5 (36.6%), and ChatGPT (22.4%) were the most common. Direct prompting was the most common adaptation method (92.5%), with reinforcement learning rarely utilized (1.4%). Accuracy was the most frequently assessed metric, while errors and safety (9.4%) and time efficiency (7.0%) were less commonly evaluated.

Conclusion

LLMs hold promise across healthcare applications. Expanding their use in workflow optimization, trainee education, and research tools could enhance healthcare delivery and innovation. Comprehensive evaluation using standardized criteria is essential for LLMs integration into healthcare.

背景：ChatGPT的发布促进了生成式大型语言模型（llm）在医疗保健领域的广泛采用。本综述系统地考察了它们在医疗保健中的应用。方法：系统检索具有代表性的综合性生物医学与健康科学数据库PubMed，检索2023年1月1日至2024年7月30日期间发表的研究。如果研究评估生成法学硕士在医疗保健应用中的表现，则纳入研究；综述或透视文章被排除在外。结果：共纳入415项研究，在2023年4月之后观察到的出版物显著增加。生成法学硕士应用于不同的医学专业，主要是支持临床决策（26.7%）和提供患者信息（23.9%）。较小的比例集中在专业教育和培训（18.1%），研究（16.1%）和工作流程支持（12.5%）。这些应用主要由三个关键的NLP任务支持：问答（36.1%）、文本分类（27.5%）和文本生成（26.3%）。20%的研究使用了公共数据集，15%的研究使用了临床患者数据。在使用的98个llm中，最常见的是GPT-4（51.3%）、GPT-3.5（36.6%）和ChatGPT（22.4%）。直接提示是最常见的适应方法（92.5%），强化学习很少使用（1.4%）。准确性是最常被评估的指标，而错误和安全性（9.4%）以及时间效率（7.0%）则不常被评估。结论：llm在医疗保健应用中具有前景。扩大它们在工作流程优化、培训生教育和研究工具中的应用，可以增强医疗保健服务和创新。使用标准化标准的综合评估对于法学硕士融入医疗保健至关重要。

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引用次数: 0