International Journal of Medical Informatics最新文献_第2页

Large language models as second reviewers for medical errors in real-world internal medicine reports: a prospective comparative study of open- and closed-source models 大型语言模型作为现实世界内科报告中医疗差错的第二审稿人：开放和封闭源模型的前瞻性比较研究

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-02-09 DOI: 10.1016/j.ijmedinf.2026.106316

Roko Skrabic , Ivan Viculin , Zvonimir Boban , Marko Kumric , Marino Vilovic , Josip Vrdoljak , Josko Bozic

Objective

Preventable errors in clinical documentation and decision-making remain a major threat to patient safety, yet the role of open-source large language models (LLMs) as practical “second reviewers” in general Internal Medicine remains unclear.

Methods

We prospectively assembled 102 real-world Emergency Internal Medicine reports (de-identified) and either inserted or confirmed realistic errors across four categories (diagnostics/investigations, medication/therapy, process/communication/follow-up, other). Three LLMs (open-source Deepseek-v3-r1 and GPT-OSS-120b, and closed-source OpenAI-o3) were prompted with a uniform system instruction to (i) localize the predefined error and (ii) recommend corrections. Two blinded Internal Medicine specialists independently graded outputs for error localization (0–1) and recommendation quality (Likert 1–4); disagreements were resolved analytically, and analyses used the more conservative rater. Three human clinicians independently reviewed subsets of the same cases to provide a comparator.

Results

Using the conservative rater, correct error localization was 72.5% (74/102; 95% CI 63.2–80.3) for Deepseek-v3-r1, 79.2% (80/101; 95% CI 70.3–86.0) for o3, and 65.7% (67/102; 95% CI 56.1–74.2) for GPT-OSS-120b (Cochran’s Q p = 0.033). Pairwise McNemar tests favored o3 over GPT-OSS-120b (p = 0.020; Holm-adjusted p = 0.060); other contrasts were not significant. Recommendation quality was high for all models (median 4/4), with mean ± SD scores of 3.73 ± 0.49 for Deepseek-v3-r1, 3.65 ± 0.64 for o3, and 3.51 ± 0.73 for GPT-OSS-120b. Inter-rater agreement was excellent for GPT-OSS-120b (κ = 0.94 for detection; κ_w = 0.85 for quality), substantial for Deepseek-v3-r1 (κ = 0.75; κ_w = 0.47), and lower for o3 (κ = 0.31; κ_w = 0.14). All models frequently flagged additional clinically useful issues (≥99% of reports).

Conclusion

In real-world Internal Medicine reports with realistic, expert-defined errors, state-of-the-art open-source LLMs approached the performance of a leading closed model and clearly outperformed clinicians in error detection, while providing predominantly guideline-concordant corrective recommendations. Given their advantages for privacy, customizability, and potential local deployment, open models represent credible candidates for privacy-preserving “second-reviewer” support in Internal Medicine. Prospective, workflow-embedded trials that also quantify specificity on error-free notes, alert burden, and patient outcomes are now warranted.

目的：临床文献和决策中的可预防错误仍然是对患者安全的主要威胁，然而开源大型语言模型（LLMs）作为普通内科实用的“第二审稿人”的作用尚不清楚。方法：我们前瞻性地收集了102份真实世界的急诊内科报告（去识别），并插入或确认了四个类别（诊断/调查、药物/治疗、过程/沟通/随访、其他）中的现实错误。三个llm（开源的Deepseek-v3-r1和GPT-OSS-120b，以及闭源的OpenAI-o3）被一个统一的系统指令提示：(i)定位预定义的错误，（ii）建议纠正。两名盲法内科专家独立对错误定位（0-1）和推荐质量（Likert 1-4）的输出进行评分；分歧是通过分析来解决的，分析使用了更保守的评分。三名人类临床医生独立审查了相同病例的子集，以提供比较。结果：使用保守评分法，Deepseek-v3-r1的正确定位误差为72.5% (74/102;95% CI 63.2-80.3)， o3的为79.2% (80/101;95% CI 70.3-86.0)， gpt - ss -120b的为65.7% (67/102;95% CI 56.1-74.2) （Cochran’s Q p = 0.033）。成对McNemar检验倾向于o3优于GPT-OSS-120b (p = 0.020；holm校正p = 0.060)；其他对比不显著。所有模型的推荐质量都很高（中位数为4/4），Deepseek-v3-r1的平均SD评分为3.73 ± 0.49，o3的平均值为3.65 ± 0.64，gpt - osss -120b的平均值为3.51 ± 0.73。两分的协议是适合gpt oss - 120 b(κ = 0.94 检测;κ_w = 0.85 质量),大量的Deepseek-v3-r1(κ = 0.75;κ_w = 0.47),并降低o3(κ = 0.31;κ_w = 0.14)。所有模型经常标记额外的临床有用问题（≥99%的报告）。结论：在现实世界的内科报告中，有现实的、专家定义的错误，最先进的开源法学硕士接近领先的封闭模型的性能，在错误检测方面明显优于临床医生，同时提供主要的指导方针一致的纠正建议。考虑到它们在隐私、可定制性和潜在的本地部署方面的优势，开放模型代表了内科医学中保护隐私的“第二审稿人”支持的可靠候选者。前瞻性的、嵌入工作流程的试验也量化了无错误记录、警报负担和患者结果的特异性。

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引用次数: 0

Explainable AI in Cardiology Diagnostics: A Systematic Review of Machine Learning, Meta-heuristic Optimization, and Clinical Text Mining for Coronary Artery Disease 心脏病诊断中可解释的人工智能：冠状动脉疾病机器学习、元启发式优化和临床文本挖掘的系统综述。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-02-02 DOI: 10.1016/j.ijmedinf.2026.106321

Majdi Jaradat , Mohammed Awad

Background

This systematic review compiles evidence and examines how various artificial intelligence (AI) approaches, including machine learning (ML), natural language processing (NLP), meta-heuristic optimization, and explainable AI (XAI), are utilized to predict and diagnose coronary artery disease (CAD). We aim to identify the most commonly used models, evaluate their performance, and explore how interpretability and optimization enhance their usefulness in clinical practice.

Method

A thorough search was conducted across five major databases (PubMed, Scopus, IEEE Xplore, ACM Digital Library, and SpringerLink) to identify relevant studies published between January 2022 and August 2025, in accordance with the PRISMA guidelines. Dual independent reviewers performed study selection and data extraction. The quality of the included studies was evaluated using a checklist based on QUADAS-2. Data were collected on study characteristics, model types, validation methods, and performance metrics, which will be the cornerstone of the analysis.

Results

Sixty-one studies met the inclusion criteria. ML and deep learning models demonstrated strong performance and achieved high accuracy in benchmark datasets, but showed limited clinical validation. Transformer-based models (e.g., BioBERT, ClinicalBERT) showed high efficacy for medical text analysis, but require substantial data and computational resources. Meta-heuristic algorithms (e.g., Genetic Algorithms, Particle Swarm Optimization) effectively improved model efficiency but were rarely applied to unstructured clinical narratives. XAI tools (e.g., SHAP, LIME) improved model transparency, though most studies highlight a need for more rigorous evaluation.

Conclusion

Integrated ML, NLP, meta-heuristic optimization, and XAI hold significant promise in advancing the diagnosis of CAD by improving both accuracy and interpretability. However, challenges such as data scarcity, limited external validation, and a lack of standardized, clinician-centric explainability impede clinical adoption. Future research should focus on hybrid frameworks validated for large, diverse, and real-world datasets.

背景：本系统综述收集证据并研究了各种人工智能（AI）方法，包括机器学习（ML）、自然语言处理（NLP）、元启发式优化和可解释人工智能（XAI），如何用于预测和诊断冠状动脉疾病（CAD）。我们的目标是确定最常用的模型，评估它们的性能，并探索如何可解释性和优化增强它们在临床实践中的有用性。方法：根据PRISMA指南，在五个主要数据库（PubMed, Scopus, IEEE Xplore， ACM Digital Library和SpringerLink）中进行了彻底的检索，以确定2022年1月至2025年8月期间发表的相关研究。双独立审稿人进行研究选择和数据提取。采用基于QUADAS-2的检查表对纳入研究的质量进行评估。收集有关研究特征、模型类型、验证方法和性能度量的数据，这将是分析的基石。结果：61项研究符合纳入标准。ML和深度学习模型在基准数据集中表现出很强的性能和较高的准确性，但临床验证有限。基于转换器的模型（如BioBERT、ClinicalBERT）在医学文本分析中显示出很高的效率，但需要大量的数据和计算资源。元启发式算法（如遗传算法、粒子群优化）有效地提高了模型效率，但很少应用于非结构化临床叙述。XAI工具（例如，SHAP， LIME）提高了模型的透明度，尽管大多数研究强调需要更严格的评估。结论：整合ML、NLP、元启发式优化和XAI，通过提高准确性和可解释性，在推进CAD诊断方面具有重要的前景。然而，诸如数据稀缺、有限的外部验证以及缺乏标准化、以临床为中心的可解释性等挑战阻碍了临床应用。未来的研究应该集中在大型、多样化和真实世界数据集验证的混合框架上。

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引用次数: 0

The effect of artificial intelligence–assisted pulmonary rehabilitation on exercise capacity: A systematic review and meta-analysis 人工智能辅助肺康复对运动能力的影响：一项系统综述和荟萃分析。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ijmedinf.2026.106336

Ecran Cinkavuk, Ebru Calik, Naciye Vardar-Yagli

Introduction

Artificial intelligence (AI) technologies are increasingly being integrated into pulmonary rehabilitation (PR) to improve individualization, real-time monitoring, and adherence in individuals with chronic respiratory diseases. However, their clinical impact on exercise capacity remains unclear. This systematic review and meta-analysis aimed to evaluate the effectiveness of AI-supported PR programs compared to usual care in improving exercise capacity and respiratory function in adults with chronic respiratory diseases.

Methods

This systematic review and meta-analysis followed PRISMA guidelines and was registered with PROSPERO (ID: CRD420251075622). A comprehensive search was conducted across five electronic databases (PubMed, Web of Science, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL) and PEDro) from inception to July 2025. Statistical analyses for the meta-analysis were conducted using RevMan 5.4.

Results

Three eligible RCTs with a total of 456 participants were included. Pooled analysis showed a significant improvement in 6-minute walk distance (6MWD) after AI-assisted PR group compared to control (MD: 22.08 m; 95% CI: 4.96–39.20; p = 0.01). Moderate heterogeneity was observed (I² = 40%). No meta-analysis was conducted for respiratory function due to insufficient pre-post data. Risk of bias was generally low, though participant blinding was absent in all studies. Methodological quality was good, with a mean PEDro score of 6.0 ± 0.82.

Conclusion

AI-supported PR can significantly improve exercise capacity in individuals with chronic respiratory diseases. Despite promising results, high-quality studies in different pulmonary patient groups are needed to address existing limitations, particularly regarding standardization, cost-effectiveness, and clinical integration of AI-technology.

人工智能（AI）技术越来越多地被整合到肺康复（PR）中，以改善慢性呼吸道疾病患者的个体化、实时监测和依从性。然而，它们对运动能力的临床影响尚不清楚。本系统综述和荟萃分析旨在评估人工智能支持的PR项目与常规护理相比，在改善慢性呼吸系统疾病成人的运动能力和呼吸功能方面的有效性。方法：本系统评价和荟萃分析遵循PRISMA指南，并在PROSPERO注册（ID: CRD420251075622）。我们对5个电子数据库（PubMed、Web of Science、Scopus、Cochrane Central Register of Controlled Trials （Central）和PEDro）进行了全面的检索，检索时间从成立到2025年7月。meta分析采用RevMan 5.4进行统计分析。结果：纳入3项符合条件的随机对照试验，共纳入456名受试者。合并分析显示，人工智能辅助PR组患者6分钟步行距离（6MWD）较对照组有显著改善（MD: 22.08 m; 95% CI: 4.96 ~ 39.20; p = 0.01）。观察到中度异质性（I2 = 40%）。由于前后数据不足，未对呼吸功能进行meta分析。偏倚风险一般较低，但所有研究均未采用受试者盲法。方法质量良好，平均PEDro评分为6.0±0.82。结论：人工智能辅助PR可显著提高慢性呼吸系统疾病患者的运动能力。尽管取得了令人鼓舞的结果，但需要对不同肺部患者群体进行高质量的研究，以解决现有的局限性，特别是在标准化、成本效益和人工智能技术的临床整合方面。

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引用次数: 0

Applying a statistical model-based AI method to identify prognostic factors for long-term cognitive decline in Alzheimer’s disease: Evidence from pooled placebo data of four phase III trials 应用基于统计模型的人工智能方法识别阿尔茨海默病长期认知能力下降的预后因素：来自四个III期试验安慰剂数据汇总的证据

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-02-03 DOI: 10.1016/j.ijmedinf.2026.106337

Ryoichi Hanazawa , Hiroyuki Sato , Keisuke Suzuki , Akihiro Hirakawa

Background

Heterogeneity in the long-term progression of Alzheimer’s disease (AD) challenges the efficiency of clinical trials. Identifying long-term prognostic factors is critical for enhancing trial efficiency, although it has been limited by the lack of appropriate statistical approaches. We applied a recently developed statistical model-based AI method to identify the baseline prognostic factors for long-term cognitive decline in a clinical trial population.

Methods

We analyzed pooled placebo arm data (N = 1,597) from four Phase III trials in patients with mild-to-moderate AD. Long-term trajectories for the Mini-Mental State Examination (MMSE), 11- and 14-item versions of the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog11, ADAS-Cog14), and Clinical Dementia Rating-Sum of Boxes (CDR-SB) were predicted from their short-term data (≤80 weeks). Trajectories were compared between subgroups defined by six baseline factors (age, sex, apolipoprotein E ε4 [APOE ε4] status, years of education, years from diagnosis, and years from disease onset) using the area under the curve (AUC).

Results

Longer years of education (≥13 years) was the most robust predictor associated with faster progression across all four outcomes (e.g., for 20-year ADAS-Cog11, AUC ratio, 1.11, p < 0.001). Younger age (<74 years) was associated with a faster decline in MMSE and ADAS-Cog scores, but not in CDR-SB. APOE ε4 status, sex, years from diagnosis, and years from disease onset were not significantly associated with long-term progression.

Conclusions

Baseline educational level and age were significant prognostic factors of long-term cognitive decline. These findings will help optimize patient stratification in future clinical trials on AD.

阿尔茨海默病（AD）长期进展的异质性对临床试验的效率提出了挑战。确定长期预后因素对于提高试验效率至关重要，尽管由于缺乏适当的统计方法而受到限制。我们应用了最近开发的基于统计模型的人工智能方法来确定临床试验人群长期认知能力下降的基线预后因素。方法：我们分析了来自4个轻至中度AD患者的III期临床试验的安慰剂组数据（N = 1597）。根据短期数据（≤80周）预测迷你精神状态检查（MMSE）、11项和14项版本的阿尔茨海默病评估量表-认知亚量表（ADAS-Cog11、ADAS-Cog14）和临床痴呆评分-盒和（CDR-SB）的长期轨迹。使用曲线下面积（AUC）比较由六个基线因素（年龄、性别、载脂蛋白E ε4 [APOE ε4]状态、受教育年限、诊断年限和发病年限）定义的亚组之间的轨迹。结果：较长的受教育年限（≥13年）是与所有四种结局（例如，对于20年adas， cog11， AUC比为1.11,p < 0.001）中更快进展相关的最可靠预测因子。年龄较小（74岁）与MMSE和ADAS-Cog评分下降较快相关，但与CDR-SB无关。APOE ε4状态、性别、诊断年龄和发病年龄与长期进展无显著相关。结论基线受教育程度和年龄是长期认知能力下降的重要预后因素。这些发现将有助于在未来的阿尔茨海默病临床试验中优化患者分层。

{"title":"Applying a statistical model-based AI method to identify prognostic factors for long-term cognitive decline in Alzheimer’s disease: Evidence from pooled placebo data of four phase III trials","authors":"Ryoichi Hanazawa , Hiroyuki Sato , Keisuke Suzuki , Akihiro Hirakawa","doi":"10.1016/j.ijmedinf.2026.106337","DOIUrl":"10.1016/j.ijmedinf.2026.106337","url":null,"abstract":"<div><h3>Background</h3><div>Heterogeneity in the long-term progression of Alzheimer’s disease (AD) challenges the efficiency of clinical trials. Identifying long-term prognostic factors is critical for enhancing trial efficiency, although it has been limited by the lack of appropriate statistical approaches. We applied a recently developed statistical model-based AI method to identify the baseline prognostic factors for long-term cognitive decline in a clinical trial population.</div></div><div><h3>Methods</h3><div>We analyzed pooled placebo arm data (N = 1,597) from four Phase III trials in patients with mild-to-moderate AD. Long-term trajectories for the Mini-Mental State Examination (MMSE), 11- and 14-item versions of the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog11, ADAS-Cog14), and Clinical Dementia Rating-Sum of Boxes (CDR-SB) were predicted from their short-term data (≤80 weeks). Trajectories were compared between subgroups defined by six baseline factors (age, sex, <em>apolipoprotein E ε4</em> [<em>APOE ε4</em>] status, years of education, years from diagnosis, and years from disease onset) using the area under the curve (AUC).</div></div><div><h3>Results</h3><div>Longer years of education (≥13 years) was the most robust predictor associated with faster progression across all four outcomes (e.g., for 20-year ADAS-Cog11, AUC ratio, 1.11, p < 0.001). Younger age (<74 years) was associated with a faster decline in MMSE and ADAS-Cog scores, but not in CDR-SB. <em>APOE ε4</em> status, sex, years from diagnosis, and years from disease onset were not significantly associated with long-term progression.</div></div><div><h3>Conclusions</h3><div>Baseline educational level and age were significant prognostic factors of long-term cognitive decline. These findings will help optimize patient stratification in future clinical trials on AD.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106337"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190933","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}

引用次数: 0

Development and temporal validation of machine learning models for predicting clinically relevant medication reconciliation discrepancies at the emergency department: A single-center retrospective study 用于预测急诊科临床相关药物调和差异的机器学习模型的开发和时间验证：一项单中心回顾性研究

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-01-24 DOI: 10.1016/j.ijmedinf.2026.106309

Greet Van De Sijpe , Tuur Schrooten , Sabrina De Winter , Lorenz Van der Linden , Peter Vanbrabant , Annabel Dompas , Bo Bertels , Maarten De Vos , Isabel Spriet

Objective

Medication discrepancies at hospital admission are common and can cause preventable patient harm. Predictive models can help prioritize medication reconciliation for high-risk patients. This study aimed to develop and validate machine learning (ML) models for predicting clinically relevant medication reconciliation discrepancies in emergency department (ED) patients, and to compare their performance with logistic regression.

Methods

We conducted a single-center, retrospective study at UZ Leuven. The dataset included patients admitted to the ED between 2017 and 2019 (development set) and 2021–2022 (temporal validation set). The outcome variable was the presence of at least one clinically relevant medication discrepancy, defined by expert panel adjudication. Variables were extracted from the electronic health record, with care to avoid data leakage. Three models – logistic regression, random forest, and eXtreme Gradient Boosting – were developed using tailored variable selection strategies, and validated temporally. Model performance was assessed via discrimination, calibration, and classification metrics. Clinical utility was assessed using decision curve analysis.

Results

The development and validation cohorts included 817 and 349 patients, respectively. LR and RF models demonstrated moderate discrimination on temporal validation (AUROC 0.67–0.68). The XGBoost model showed lower discrimination (AUROC 0.63). Calibration was comparable across models. Decision curve analysis showed only small differences in net benefit between models across clinically relevant threshold probabilities.

Conclusion

ML models provided no clear improvement over logistic regression, which achieved similar predictive performance and greater interpretability. These findings highlight both the potential and the limitations of ML for supporting targeted medication reconciliation in ED workflows. Future research should explore the added value of richer data sources, such as unstructured clinical narratives.

目的：住院用药不一致是常见的，可造成可预防的患者伤害。预测模型可以帮助对高危患者的药物调节进行优先排序。本研究旨在开发和验证机器学习（ML）模型，用于预测急诊科（ED）患者的临床相关药物调和差异，并将其性能与逻辑回归进行比较。方法：我们在鲁汶大学进行了一项单中心回顾性研究。该数据集包括2017年至2019年（发展集）和2021年至2022年（时间验证集）期间入住急诊科的患者。结果变量是存在至少一种临床相关的药物差异，由专家小组裁决确定。从电子健康记录中提取变量，小心避免数据泄露。使用量身定制的变量选择策略开发了三个模型-逻辑回归，随机森林和极端梯度增强，并进行了时间验证。通过判别、校准和分类指标评估模型性能。采用决策曲线分析评估临床效用。结果：开发和验证队列分别包括817例和349例患者。LR和RF模型在时间验证上表现出中度差异（AUROC为0.67-0.68）。XGBoost模型具有较低的判别性（AUROC为0.63）。各模型的校准具有可比性。决策曲线分析显示，在临床相关阈值概率的模型之间，净收益只有很小的差异。结论：与逻辑回归相比，ML模型没有明显的改进，预测性能相似，可解释性更强。这些发现强调了ML在支持ED工作流程中靶向药物调节方面的潜力和局限性。未来的研究应探索更丰富的数据来源的附加价值，如非结构化的临床叙述。

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引用次数: 0

Machine learning models for identifying urinary incontinence in women with a history of hysterectomy using basic demographic and clinical characteristics: A cross-sectional study 使用基本人口学和临床特征识别子宫切除术史女性尿失禁的机器学习模型：一项横断面研究。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ijmedinf.2026.106334

Lu Liu , Wei Chen , Lili Li , Ping Zhang

Background

Urinary incontinence (UI) in women with a history of hysterectomy represents a significant global health concern. It is crucial to clarify the association between hysterectomy for benign indications and UI to avoid unnecessary surgery.

Objective

This study aimed to develop a machine learning (ML) model to identify factors associated with UI in women with a history of hysterectomy.

Methods

We analyzed 2021 patients from the National Health and Nutrition Examination Survey (NHANES) database who underwent hysterectomy for benign indications as our derivation cohort. Thirteen demographic and clinical features were evaluated: age, educational, anthropometric measurements (height, weight, waist), medical history diabetes mellitus (DM), and reproductive history. Six ML algorithms were employed: logistic regression (LR), naïve Bayes (NB), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). External validation was performed on a cohort consisting of 556 patients from the Second Qilu Hospital of Shandong University. To improve interpretability, the predictive process was graphically illustrated employing a nomogram and SHapley Additive exPlanations (SHAP). Finally, the model was deployed as an online clinical decision support platform for applications.

Results

A comparison of receiver operating characteristic (ROC) curves using LR as the reference model revealed no statistically significant differences across the six ML algorithms. In the internal validation cohorts, the models achieved area-under-the-curve (AUC) values of 0.753–0.763 and accuracies between 0.627 and 0.664. This predictive performance was sustained in the external-validation cohort, with AUC values ranging from 0.702 to 0.718 and accuracies ranging from 0.661 to 0.697.

Conclusion

Our findings demonstrated that ML models could effectively identify UI in women with a history of hysterectomy. This approach, facilitated by the nomogram and online tool, enhanced the feasibility and accessibility of identifying women at risk.

背景：子宫切除术史女性尿失禁（UI）是一个重要的全球健康问题。为了避免不必要的手术，明确良性子宫切除术与尿失禁之间的关系是至关重要的。目的：本研究旨在开发一种机器学习（ML）模型，以识别子宫切除术史女性尿失禁的相关因素。方法：我们分析了来自国家健康和营养检查调查（NHANES）数据库中因良性适应症接受子宫切除术的2021例患者作为我们的衍生队列。评估了13项人口统计学和临床特征：年龄、教育程度、人体测量（身高、体重、腰围）、糖尿病病史和生殖史。采用了六种机器学习算法：逻辑回归（LR）、naïve贝叶斯（NB）、多层感知器（MLP）、极端梯度增强（XGBoost）、随机森林（RF）和支持向量机（SVM）。外部验证对象为来自山东大学齐鲁第二医院的556例患者。为了提高可解释性，预测过程以图形方式说明采用nomogram和SHapley Additive explanation （SHAP）。最后，将该模型部署为应用程序的在线临床决策支持平台。结果：以LR为参考模型的受试者工作特征（ROC）曲线比较显示，六种ML算法之间无统计学差异。在内部验证队列中，模型的曲线下面积（AUC）值为0.753-0.763，精度在0.627 - 0.664之间。这种预测性能在外部验证队列中保持不变，AUC值范围为0.702至0.718，准确度范围为0.661至0.697。结论：我们的研究结果表明，ML模型可以有效地识别子宫切除术史女性的尿失禁。在nomogram和在线工具的推动下，这种方法提高了识别处于危险中的妇女的可行性和可及性。

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引用次数: 0

A systematic review of the causes of morbidity data quality issues 系统回顾发病原因的数据质量问题。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ijmedinf.2026.106333

Sam Yan , Jessica Dickson , Brandon Cheong , Heather Grain , John Oldroyd

Background

The quality of hospital morbidity data collected with the International Classification of Diseases is unknown. A systematic review of the causes of morbidity data quality issues is urgently needed.

Objectives

We aimed to systematically identify and investigate the root causes of issues associated with hospital morbidity data collected using the International Classification of Diseases 10th edition, Australian Modification (ICD-10-AM) and Australian Classification of Health Interventions (ACHI).

Methods

This review included studies related to morbidity data collection issues arising from using ICD-10-AM and ACHI from Scopus, Embase, Medline and other data sources from 2017 to January 2025 in English. The quality of included studies was assessed using SQUIRE and STROBE checklists. A narrative synthesis was undertaken with themes and sub-categories of issues identified. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Statement.

Results

Fifty-two studies were included, 37 from Australia, 3 from Canada, 2 each from Ireland and New Zealand, and 1 each from France, Germany, Turkey, US. Four themes were identified: 1) quality issues in standards, 2) technology, 3) education and training, and 4) issues related to clinical practice. There exists ambiguity in standards due to optional guidelines in data processing and jurisdictional differences. The standards do not provide sufficient granularity for precise disease identification. The standards are not capable of linking complex diagnostic, causal and procedural relationships and are leading to technical and other categories of issues. The complexity of issues associated with the standard leads to insufficient training resources for staff worldwide. Fragmented information structure and changes in clinical documentation rules lead to inconsistent coding.

Interpretation.

The root causes of the morbidity data collection errors are mainly associated with the quality of the standards. Further research is needed to address the root causes of morbidity data quality issues, including the structure of data capture systems and the use of more consistent approaches to standards writing, such as those applied by the International Organisation for Standardisation (ISO), which is not investigated by this research.

背景：使用国际疾病分类收集的医院发病率数据的质量尚不清楚。迫切需要对发病原因、数据质量问题进行系统审查。目的：我们旨在系统地识别和调查与使用国际疾病分类第10版、澳大利亚修订（ICD-10-AM）和澳大利亚健康干预分类（ACHI）收集的医院发病率数据相关的问题的根本原因。方法：本综述纳入2017年至2025年1月Scopus、Embase、Medline等数据源中使用ICD-10-AM和ACHI引起的发病率数据收集问题的相关研究。采用SQUIRE和STROBE检查表评估纳入研究的质量。对确定的主题和问题分类别进行了叙述综合。该综述遵循了系统评价和荟萃分析（PRISMA） 2020声明的首选报告项目。结果：共纳入52项研究，其中澳大利亚37项，加拿大3项，爱尔兰和新西兰各2项，法国、德国、土耳其、美国各1项。确定了四个主题：1)标准中的质量问题，2)技术，3)教育和培训，以及4)与临床实践相关的问题。由于数据处理的可选准则和管辖权差异，标准存在歧义。这些标准没有为精确的疾病识别提供足够的粒度。这些标准不能将复杂的诊断、因果和程序关系联系起来，并导致技术和其他类别的问题。与该标准有关的问题的复杂性导致全世界工作人员的培训资源不足。信息结构的碎片化和临床文件规则的变化导致编码不一致。解释：发病率数据收集错误的根本原因主要与标准的质量有关。需要进一步的研究来解决发病率数据质量问题的根本原因，包括数据捕获系统的结构和使用更一致的标准编写方法，例如国际标准化组织（ISO）所应用的方法，本研究未对此进行调查。

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引用次数: 0

The state of standardized musculoskeletal terminology for healthcare reuse:A scoping review 医疗保健重用的标准化肌肉骨骼术语的现状：范围审查。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ijmedinf.2026.106318

Melinda Wassell , Kerryn Butler-Henderson , Peter McCann , Henry Pollard , Salma Arabi , Wei Wang , Karin Verspoor

Objective

Standardizing terminology offers opportunities for improved communication and care outcomes. With increasing adoption of clinical terminologies, questions remain about whether they adequately capture the scope of musculoskeletal (MSK) primary care practice. This scoping review examines global development efforts on MSK-relevant standardized terminology and its implementation in clinical practice.

Methods

A scoping review was conducted of 6 databases to May 2025. Identified studies (n = 3668) were included (n = 60) if they addressed standardized terminology relevant to the MSK primary care professions of chiropractic, osteopathy, and physiotherapy. Data were extracted on use cases, documentation of MSK information, alignment with national interoperability standards, and implementation status.

Results

Global development efforts span diverse MSK domains across condition types. Five studies achieved consensus around domain-specific terms (including tendinopathies, groin pain, and weight-bearing rehabilitation); in contrast, many studies developed extensive clinical terminology sets. Most studies (82.4%) address the development of terminologies, with few yet addressing how they have been implemented into clinical practice (2.7%).

Analysis revealed MSK clinicians require documentation beyond existing core interoperability data groups, including 1) function and movement, 2) pain characteristics, 3) psychosocial factors, 4) social determinants of health (environmental factors and participation barriers), 5) intervention effectiveness and clinical outcomes, and 6) person-centered factors.

Multiple barriers emerged, including technical (EHR integration, cognitive burden), workflow (time requirements, clinical value), professional (training, profession-specific terminology), and knowledge gaps (impact on care quality).

Conclusion

Extensive terminology development has begun yet gaps exist between development and clinical adoption. Terms evolve as research evolves; therefore, MSK professions should actively engage with interoperability groups to establish hierarchical ontologies that incorporate the identified data groups and balance standardization at higher conceptual levels with flexible lexicons to enable terminology growth over time. Establishing feedback mechanisms with EHR vendors to minimize clinicians’ cognitive burden will accelerate adoption and maximize clinical value.

目的：标准化术语为改善沟通和护理结果提供了机会。随着越来越多的临床术语的采用，问题仍然存在，他们是否充分捕捉肌肉骨骼（MSK）初级保健实践的范围。这一范围审查审查了msk相关标准化术语的全球发展努力及其在临床实践中的实施。方法：对截至2025年5月的6个数据库进行范围综述。确定的研究（n = 3668）被纳入（n = 60），如果它们涉及与MSK初级保健专业（脊椎指压疗法、整骨疗法和物理疗法）相关的标准化术语。从用例、MSK信息文档、与国家互操作性标准的一致性和实施状态中提取数据。结果：全球发展努力跨越不同条件类型的MSK领域。五项研究围绕特定领域的术语（包括肌腱病变、腹股沟疼痛和负重康复）达成了共识；相比之下，许多研究开发了广泛的临床术语集。大多数研究（82.4%）涉及术语的发展，很少有研究涉及如何将术语应用于临床实践（2.7%）。分析显示，MSK临床医生需要的文件超出了现有的核心互操作性数据组，包括1)功能和运动，2)疼痛特征，3)心理社会因素，4)健康的社会决定因素（环境因素和参与障碍），5)干预效果和临床结果，以及6)以人为中心的因素。出现了多种障碍，包括技术（EHR整合、认知负担）、工作流程（时间要求、临床价值）、专业（培训、专业特定术语）和知识差距（对护理质量的影响）。结论：广泛的术语开发已经开始，但在开发和临床应用之间存在差距。术语随着研究的发展而演变；因此，MSK专业人员应积极参与互操作性组，以建立包含已识别数据组的分层本体，并在更高的概念级别上平衡标准化和灵活的词汇，以支持术语随着时间的推移而增长。与电子病历供应商建立反馈机制，以减少临床医生的认知负担，将加速采用和最大化临床价值。

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引用次数: 0

A personalized and complex mHealth intervention for the universal prevention of Perinatal mental Disorders in routine maternal Care: Design and development of e-Perinatal app 个性化和复杂的移动健康干预在常规产妇护理中普遍预防围产期精神障碍：电子围产期应用程序的设计和开发

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-01-31 DOI: 10.1016/j.ijmedinf.2026.106290

Company-Córdoba Rosalba , Caffieri Alessia , Barquero-Jimenez Carlos , Cruz-Cabrera Roberto , De-Juan-Iglesias Paula , Gil-Cosano José J. , Goossens Lennert , Nieto-Casado Francisco J. , Ureña-Lorenzo Amalia , Gómez-Gómez Irene , Motrico Emma

Background

Perinatal Mental Disorders (PMDs) are common during pregnancy and the first postpartum year, with negative consequences for women, their partners, and infants, as well as broader societal costs. While numerous interventions have been developed to prevent PMDs, there remains a need for a universal, personalized, and cost-effective solution integrated into routine maternal care. The e-Perinatal study aimed to address this gap. This paper describes the design of the e-Perinatal intervention, delivered via a dedicated mobile health app.

Methods

Guided by the Medical Research Council framework, the e-Perinatal app integrates Self-Determination Theory, Normalization Process Theory, and Patient and Public Involvement perspectives. Existing evidence was reviewed, and stakeholders participated in the co-development of digital micro-interventions (DMs). A clinical rule-based algorithm was implemented to generate personalized recommendations across four pathways (1) weekly content delivery, (2) user preferences, (3) individual risk profile, and (4) PMD monitoring.

Results

The e-Perinatal app includes: 1) DMs focused on psychological, physical activity, and healthy lifestyle domains; 2) a personalized recommendation engine; 3) a social support section; 4) mental health monitoring; 5) an ‘SOS’ button for assistance; and 6) an appointment reminder tool. In total, 332 evidence-based DMs were developed for women and their partners and delivered in text, audio, and video formats. A clinical rule-based algorithm tailors recommendations according to user characteristics and perinatal stage, employing adaptive content filtering to optimize personalization.

Conclusion

the e-Perinatal app is a personalized mHealth intervention to prevent PMDs within routine maternal care. The intervention combines evidence-based strategies, personalized recommendations, and adaptive digital content to prevent PMDs. Future research will assess effectiveness, implementation, and real-world impact of e-Perinatal intervention for PMD prevention.

背景：围产期精神障碍（PMDs）在怀孕期间和产后第一年很常见，对妇女、其伴侣和婴儿产生负面影响，并带来更广泛的社会成本。虽然已经制定了许多预防经pmd的干预措施，但仍需要将一种普遍、个性化和具有成本效益的解决方案纳入常规孕产妇保健。e-围产期研究旨在解决这一差距。本文描述了电子围产期干预的设计，通过一个专用的移动健康应用程序提供。方法：在医学研究委员会框架的指导下，电子围产期应用程序整合了自决理论、规范化过程理论以及患者和公众参与的观点。审查了现有证据，利益相关者参与了数字微干预（DMs）的共同开发。实施了一种基于临床规则的算法，通过四个途径生成个性化建议(1)每周内容交付，(2)用户偏好，(3)个人风险概况，(4)PMD监测。结果：电子围产期app包括：1)关注心理、身体活动和健康生活方式领域的dm；2)个性化推荐引擎；3)社会支持科；4)心理健康监测；5)求救“SOS”按钮；6)预约提醒工具。总共为妇女及其伴侣开发了332份基于证据的指导文件，并以文本、音频和视频形式提供。一种基于临床规则的算法根据用户特征和围产期阶段量身定制推荐，采用自适应内容过滤优化个性化。结论：电子围产期应用程序是一种个性化的移动健康干预措施，可在常规孕产妇保健中预防经前综合症。干预措施结合了循证策略、个性化建议和自适应数字内容，以预防pmd。未来的研究将评估电子围产期干预对PMD预防的有效性、实施和现实世界的影响。

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引用次数: 0

Biometric Data in Post-Traumatic Stress Disorder Detection: A Scoping Review of Digital Health Applications 创伤后应激障碍检测中的生物特征数据：数字健康应用的范围审查。

IF 4.1 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics

Pub Date : 2026-05-01 Epub Date: 2026-01-15 DOI: 10.1016/j.ijmedinf.2026.106289

Phue Thet Khaing, Masaharu Nakayama

Context

Post-traumatic stress disorder (PTSD) is mainly assessed through self-reports and clinician interviews, which can delay recognition and limit reach. Biometric markers captured using digital technologies may enable earlier and more objective detections.

Purpose

To map biometric modalities used for PTSD detection in digital health, identify underused markers, characterise machine learning (ML)/artificial intelligence (AI) approaches, and assess sex-related analyses.

Methods

Guided by PRISMA-ScR, a protocol on the Open Science Framework was pre-registered and searches in PubMed, IEEE Xplore, and Google Scholar (2015–2025) were conducted. The full search string was: (“post-traumatic stress disorder” OR “PTSD”) AND (“biometric data” OR “biosensor” OR “wearable technology”) AND (“detection” OR “screening” OR “diagnosis” OR “monitoring”) AND (“digital health” OR “mobile health” OR “AI-based” OR “machine learning”). Peer-reviewed human studies using biometric data with digital tools and/or ML/AI for PTSD detection were eligible. Of 3,312 records, 89 underwent full-text review, and 18 studies met the inclusion criteria.

Analysis

Data were categorised by biometric modality, digital platform (wearable devices, mobile applications, ML/AI systems), study population, and performance metrics (area under the curve, sensitivity, specificity). Findings were grouped thematically (physiological, neuroimaging, behavioural, genetic, multimodal) and synthesised narratively to identify trends, gaps, and the application of sex-stratified modelling.

Results

Most studies focused on physiological (e.g., heart rate, sleep) and neuroimaging (functional magnetic resonance imaging, electroencephalography) signals; behavioural and genetic modalities were underexplored. Data were frequently captured via wearables and mobile platforms, with ML commonly applied. Performance reporting was uneven, sex-stratified analyses were rare, and several promising modalities (e.g., eye-tracking, electrodermal activity) remain underused.

Conclusion

Digital biometric approaches can detect PTSD; however, progress has been slowed by heterogeneous study designs, inconsistent reporting, and limited attention to sex differences. Establishing common reporting standards, evaluating multimodal models in real-world settings, and developing algorithms incorporating sex for more equitable screening are warranted.

背景：创伤后应激障碍（PTSD）的评估主要通过自我报告和临床医生访谈，这可能会延迟识别和限制到达。使用数字技术捕获的生物特征标记可以实现更早和更客观的检测。目的：绘制用于数字健康中PTSD检测的生物识别模式，识别未充分利用的标记，表征机器学习(ML)/人工智能（AI）方法，并评估与性别相关的分析。方法：在PRISMA-ScR的指导下，预注册开放科学框架协议，并在PubMed、IEEE Xplore和谷歌Scholar（2015-2025）中进行检索。完整的搜索字符串是：（“创伤后应激障碍”或“PTSD”）和（“生物特征数据”或“生物传感器”或“可穿戴技术”）和（“检测”或“筛查”或“诊断”或“监测”）和（“数字健康”或“移动健康”或“基于人工智能”或“机器学习”）。使用生物特征数据与数字工具和/或ML/AI进行创伤后应激障碍检测的同行评审人类研究符合条件。在3312项记录中，89项进行了全文审查，18项研究符合纳入标准。分析：根据生物识别模式、数字平台（可穿戴设备、移动应用程序、ML/AI系统）、研究人群和性能指标（曲线下面积、灵敏度、特异性）对数据进行分类。研究结果按主题分组（生理、神经影像学、行为、遗传、多模态），并以叙事方式综合，以确定趋势、差距和性别分层模型的应用。结果：大多数研究集中在生理（如心率、睡眠）和神经影像学（功能磁共振成像、脑电图）信号；行为和遗传模式尚未得到充分探索。数据经常通过可穿戴设备和移动平台捕获，通常使用ML。绩效报告不平衡，性别分层分析很少，一些有前途的模式（如眼动追踪，皮肤电活动）仍未得到充分利用。结论：数字生物识别方法可以检测创伤后应激障碍；然而，异质性研究设计、不一致的报告以及对性别差异的关注有限，延缓了研究进展。有必要建立共同的报告标准，在现实环境中评估多模式模型，并开发包含性别的算法，以实现更公平的筛查。

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引用次数: 0