International Journal of Medical Informatics最新文献

{"title":"Automated extraction of fluoropyrimidine treatment and treatment-related toxicities from clinical notes using natural language processing","authors":"Xizhi Wu ,&nbsp;Madeline S. Kreider ,&nbsp;Philip E. Empey ,&nbsp;Chenyu Li ,&nbsp;Yanshan Wang","doi":"10.1016/j.ijmedinf.2026.106276","DOIUrl":"10.1016/j.ijmedinf.2026.106276","url":null,"abstract":"<div><h3>Objective</h3><div>Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxicity documentation is often embedded in clinical notes, we aimed to develop and evaluate natural language processing (NLP) methods to extract treatment and toxicity information.</div></div><div><h3>Materials and methods</h3><div>We constructed a gold-standard dataset of 236 clinical notes from 204,165 adult oncology patients. Domain experts annotated categories related to treatment regimens and toxicities. We developed rule-based, machine learning-based (Random Forest [RF], Support Vector Machine [SVM], Logistic Regression [LR]), deep learning-based (BERT, ClinicalBERT), and large language models (LLM)-based NLP approaches (zero-shot and error analysis prompting). A 5-fold cross validation were conducted to validate each model.</div></div><div><h3>Results</h3><div>Error analysis prompting achieved optimal precision, recall, and F1 scores for treatment (F1 = 1.000) and toxicities extraction (F1 = 0.965), whereas zero-shot perform moderately (treatment F1 = 0.889, toxicities extraction F1 = 0.854) Rule-based reached F1 = 1.000 for treatment and F1 = 0.904 for toxicities extraction. LR and SVM ranked second and fourth for toxicities extraction (LR F1 = 0.914, SVM F1 = 0.903). Deep learning and RF underperformed, with performance of BERT reached F1 = 0.792 for treatment and F1 = 0.837 for toxicities extraction.,ClinicalBERT reached F1 = 0.797 for treatment and F1 = 0.884 for toxicities extraction). RF reached F1 = 0.745 for treatment and F1 = 0.853 for toxicities extraction.</div></div><div><h3>Discussion</h3><div>LMM-based error analysis outperformed all others, followed by machine learning methods. Machine learning and deep learning methods were limited by small training data and showed limited generalizability, particularly for rare categories.</div></div><div><h3>Conclusion</h3><div>LLM-based error analysis most effectively extracted fluoropyrimidine treatment and toxicity information from clinical notes, and has strong potential to support oncology research and pharmacovigilance.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106276"},"PeriodicalIF":4.1,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An old disease, a new linguistic challenge for large language models: patient education on psoriasis and psoriatic arthritis in an underrepresented medical language","authors":"Ahmet Ugur Atilan,&nbsp;Niyazi Cetin","doi":"10.1016/j.ijmedinf.2025.106246","DOIUrl":"10.1016/j.ijmedinf.2025.106246","url":null,"abstract":"<div><h3>Objective</h3><div>Large Language Models (LLMs) are increasingly applied to patient education, yet their performance in languages that are relatively underrepresented in medical-domain corpora and large language model training datasets remains underexplored. Psoriasis and psoriatic arthritis (PsA) are chronic, immune-mediated diseases requiring lifelong patient engagement, making them suitable conditions to evaluate the clarity, reliability, and inclusivity of AI-generated educational content. To assess the comprehensibility, scientific reliability, and patient-centered communication of Turkish patient education materials for psoriasis vulgaris and PsA generated by seven state-of-the-art LLMs.</div></div><div><h3>Methods</h3><div>A cross-sectional analysis compared outputs from ChatGPT-4o, Gemini 2.0 Flash, Claude 3.7 Sonnet, Grok 3, Qwen 2.5, DeepSeek R1, and Mistral Large 2. Brochures were produced using standardized zero-shot prompts and evaluated via the Ateşman readability index and the DISCERN instrument. Overall differences in DISCERN scores across the seven models were assessed using a Friedman test, followed by Bonferroni-adjusted Wilcoxon signed-rank post-hoc analyses.</div></div><div><h3>Results</h3><div>Readability scores ranged from 61.6 to 80.2 (mean = 71.3 ± 6.9), with ChatGPT-4o and Qwen 2.5 generating the most accessible texts. DISCERN reliability scores ranged from 38.5 to 60.5, with Claude 3.7 Sonnet and Gemini 2.0 Flash showing the highest accuracy. Models prioritizing factual precision produced denser language, while conversational models favored fluency but sacrificed depth. Notable variation was observed, with only Claude 3.7 Sonnet and Gemini 2.0 Flash consistently reflecting patient-centered perspectives.</div></div><div><h3>Conclusion</h3><div>LLMs showed observable differences in balancing clarity and reliability when generating health education leaflets in Turkish. Most outputs appeared to lack explicit psychosocial framing and emphasis on shared decision-making, which may suggest the need for more culturally adaptive training, clinician oversight, and locally grounded validation frameworks to support safe and inclusive AI-based patient education.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106246"},"PeriodicalIF":4.1,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering caregivers of individuals with autism spectrum disorder through sensor-based monitoring of emotional dysregulation: A scoping review","authors":"Moid Sandhu ,&nbsp;Siddique Latif ,&nbsp;Andrew Bayor ,&nbsp;Wei Lu ,&nbsp;Mahnoosh Kholghi ,&nbsp;Deepa Prabhu ,&nbsp;David Silvera-Tawil","doi":"10.1016/j.ijmedinf.2026.106262","DOIUrl":"10.1016/j.ijmedinf.2026.106262","url":null,"abstract":"<div><div><em>Objective:</em> This paper critically reviews existing work in sensor-based emotional dysregulation monitoring to support caregivers of individuals diagnosed with autism spectrum disorder (ASD).</div><div><em>Methods:</em> A systematic literature search was conducted across six databases (Google Scholar, IEEE Xplore, Scopus, ACM Digital Library, Web of Science, and PubMed) covering publications from January 1, 2016, to September 30, 2025.</div><div><em>Results:</em> Thirty-two studies met inclusion criteria, comprising 27 focused on sensor-based emotional dysregulation detection and 5 addressing intervention or support mechanisms. These studies suggest that sensor-based technologies have potential for continuous physiological monitoring, facilitating early detection and intervention to support emotional dysregulation episodes. Critical deficiencies were identified in real-time alerting capabilities, autonomous intervention deployment, self-regulation framework integration, system reliability, long-term sustainability, user interface design, and cross-environment scalability.</div><div><em>Conclusion:</em> There is a significant need to develop real-time emotion monitoring systems to empower caregivers in delivering timely, targeted interventions for individuals diagnosed with ASD. Future research should prioritise the development of real-time alert systems, autonomous intervention protocols, and solutions optimised for reliability, sustainability, usability, and adaptability across heterogeneous care settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106262"},"PeriodicalIF":4.1,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From promise to practice: strengthening evidence for AI conversational agents in healthcare","authors":"Yang Gao,&nbsp;Yingjie Lu,&nbsp;Xiaofei Li","doi":"10.1016/j.ijmedinf.2026.106264","DOIUrl":"10.1016/j.ijmedinf.2026.106264","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106264"},"PeriodicalIF":4.1,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the safety of patient-centred discharge medication instructions generated by an AI model","authors":"Michael Tang ,&nbsp;Kristina Markova ,&nbsp;Kristian Stanceski ,&nbsp;Sharleen Zhong ,&nbsp;Marguerite Tracy ,&nbsp;Linda Koria ,&nbsp;Sarita Lo ,&nbsp;Xumou Zhang ,&nbsp;Angela Pan ,&nbsp;Jinman Kim ,&nbsp;Julie Ayre ,&nbsp;Adam G. Dunn","doi":"10.1016/j.ijmedinf.2026.106272","DOIUrl":"10.1016/j.ijmedinf.2026.106272","url":null,"abstract":"<div><h3>Background</h3><div>The use of AI to support patient-centred communication could improve health outcomes but little is known about the equity of AI tools. We evaluated the completeness and accuracy of an AI tool that produces patient-centred medication information for patients following discharge from hospital, for different patient groups.</div></div><div><h3>Methods</h3><div>We evaluated differences in the completeness and safety of AI-generated (GPT-4o) patient-centred medication instructions across age groups, patient complexity, and insurance type. AI-generated medication instructions were evaluated by clinical experts for the proportion of medications that were correctly represented, described in Universal Medication Schedule (UMS) form, and presence of safety issues. We tested for significant differences in completeness and safety between groups in 140 discharge summaries sampled from the Medical Information Mark for Intensive Care (MIMIC) database.</div></div><div><h3>Results</h3><div>The proportion of patient-centred discharge instructions where all medications were included was 95 % (133/140) with a median of 6.0 medications (IQR 3.0–10.0). For most of the 140 cases, all medications from the discharge summary were correctly included (median 100 % included, IQR 83.3 %–100 %) and new medications were rarely added by AI, but a lower proportion of medications were presented in UMS format (median 22.5 %, IQR 0.0 %–92.5 %). Despite most medications being included, potential safety issues were identified in 69.3 % (97/140). There was no evidence of a difference in the correctness of included medications across age groups (p = 0.70), patient complexity (p = 0.72), or insurance type (p = 0.70). There was no evidence of a difference in proportion of medications in UMS format across age groups (p = 0.88), patient complexity (p = 0.94), or insurance type (p = 0.49). There was evidence of a difference in the proportion of cases with at least one potential safety issue across age groups (p = 0.031), patient complexity (p &lt; 0.001) and insurance types (p = 0.047).</div></div><div><h3>Conclusions</h3><div>We found evidence of a difference in safety issues in AI-generated medication instructions for older, more complex patients, and patients with certain types of insurance. Health system and contextual differences could create unexpected variations in AI-generated outputs. Studies of AI-generated messaging for patients should consider the severity and likelihood of safety issues, localised trials, and ongoing auditing.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106272"},"PeriodicalIF":4.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable machine learning-based prediction of liver metastasis risk in elderly patients with small cell lung Cancer: A study based on the SEER database and external validation in a Chinese cohort","authors":"Hang Chen ,&nbsp;Wenchao Dai ,&nbsp;Jun Yang ,&nbsp;Xin Dang ,&nbsp;Li Jiang","doi":"10.1016/j.ijmedinf.2026.106274","DOIUrl":"10.1016/j.ijmedinf.2026.106274","url":null,"abstract":"<div><h3>Purpose</h3><div>Small cell lung cancer (SCLC) is a highly aggressive malignancy with a high incidence of liver metastases, particularly among elderly patients, which significantly worsens survival outcomes. However, efficient predictive tools targeting this population remain scarce. This study aimed to develop and validate an interpretable machine learning-based model to re-stratify the risk of liver metastasis in elderly patients with SCLC after completion of routine staging evaluation at initial diagnosis.</div></div><div><h3>Methods</h3><div>A total of 10,080 patients aged ≥60 years with histologically confirmed SCLC were included from the SEER database (2010–2017) and the Affiliated Hospital of North Sichuan Medical College, China (2010–2024). Patients from SEER were randomly assigned to a training set (n = 7719) and an internal validation set (n = 1930), while 431 patients from China comprised the external validation set. Feature selection was performed using the Boruta algorithm, identifying 11 key variables. Seven ML models, namely, Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, XGBoost, and LightGBM, were developed to compare their predictive performance. The optimal model was further interpreted using SHAP (SHapley Additive exPlanations).</div></div><div><h3>Results</h3><div>The incidence of liver metastasis was approximately 32.89%, 35.39%, and 32.71% in the training, internal validation, and external validation sets, respectively. Comparative analysis across models demonstrated that, in the internal validation set, XGBoost achieved the best overall discriminative performance, with an AUC of 0.820, slightly outperforming LightGBM (0.819), logistic regression (0.813), and random forest (0.811). In the external validation set, the performance of all models declined. Given its relatively superior predictive performance, XGBoost was selected as the final model for interpretability analyses. SHAP analysis indicated that LDS/EDS, tumor stage, bone metastasis, and brain metastasis were the most influential features contributing to the model predictions.</div></div><div><h3>Conclusion</h3><div>The XGBoost-based model exhibited moderate predictive value and satisfactory interpretability in assessing the risk of liver metastasis in patients with SCLC, suggesting its potential utility as an adjunctive decision-support tool following initial diagnostic staging. Nevertheless, its generalizability across different populations requires further validation, and localized recalibration may be necessary prior to broader clinical implementation.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106274"},"PeriodicalIF":4.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating patient history into the insulin sensitivity prediction in intensive care by feedforward neural network models","authors":"Bálint Szabó ,&nbsp;J.Geoffrey Chase ,&nbsp;Balázs Benyó","doi":"10.1016/j.ijmedinf.2026.106273","DOIUrl":"10.1016/j.ijmedinf.2026.106273","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Insulin sensitivity prediction is crucial for model-based treatment in Intensive Care Unit patients, particularly those with hyperglycemia. However, predicting insulin sensitivity is challenging due to inter- and intra-patient variability.</div></div><div><h3>Methods</h3><div>Different neural network models are proposed and compared for predicting insulin sensitivity, including recurrent and feedforward versions of the Classification Deep Neural Network and Mixture Density Network models. These models were trained using 1879 patient records containing 123,988 insulin sensitivity values from three intensive care patient cohorts in three different countries.</div></div><div><h3>Results</h3><div>Results show that using patient history in prediction models can improve the accuracy of insulin sensitivity predictions. The Mixture Density Network model provided more accurate predictions, measured by a problem-specific metric that expresses clinical requirements. We demonstrated that even using up to 12 h of historical data can improve prediction accuracy.</div></div><div><h3>Conclusion</h3><div>This study highlights the potential of recurrent neural network models in predicting insulin sensitivity in Intensive Care Unit patients. Our findings suggest that using recurrent neural networks and incorporating patient history can lead to more accurate predictions. These results are generalizable due to the large and diverse dataset employed, which included patients from three different cohorts in three care settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106273"},"PeriodicalIF":4.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models’ Performances regarding logical observation identifiers names and codes mapping in laboratory medicine: A comparative analysis of ChatGPT-4.0, Gemini, and Perplexity","authors":"Shinae Yu ,&nbsp;Eun-Jung Cho ,&nbsp;Sollip Kim ,&nbsp;Kuenyoul Park ,&nbsp;Min-Sun Kim ,&nbsp;YeJin Oh ,&nbsp;Hyejin Ryu","doi":"10.1016/j.ijmedinf.2026.106270","DOIUrl":"10.1016/j.ijmedinf.2026.106270","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to assess the feasibility and practical utility of using large language models (LLMs) for Logical Observation Identifiers Names and Codes (LOINC) mapping to standardise healthcare data in the field of laboratory medicine. We evaluated the accuracy and applicability of three LLMs—ChatGPT-4.0 (OpenAI), Gemini 1.5 (Google DeepMind), and Perplexity AI (<span><span>Perplexity.ai</span><svg><path></path></svg></span>)—in mapping laboratory test items, which typically require considerable institutional-level standardisation efforts.</div></div><div><h3>Methods</h3><div>A total of 75 representative laboratory test items, including 55 clinical chemistry and 20 hematology tests commonly used in clinical practice, were selected. Six board-certified clinical pathologists independently mapped each test item to its appropriate LOINC code. A consensus mapping was established by the experts and used as the gold standard. Each LLM’s output was compared to this consensus, and the results were categorised as complete match (CM), partial match (PM), or mismatch (MM) based on agreement with the reference.</div></div><div><h3>Results</h3><div>Overall paired ordinal analyses demonstrated a significant difference in LOINC code-mapping performance among the three models, with Gemini performing significantly worse than both ChatGPT-4.0 and Perplexity AI, and no significant difference between ChatGPT-4.0 and Perplexity AI. ChatGPT-4.0 achieved the highest CM rate in clinical chemistry (58.2%), whereas Perplexity AI performed best in hematology (55.0%). Gemini showed the highest MM rates, particularly in hematology (80.0%), while partial matches were largely attributable to method-related discrepancies rather than fully incorrect mappings.</div></div><div><h3>Conclusion</h3><div>Structured inputs, localisation to domestic laboratory practices, and expert oversight are critical to improving the reliability of LLM-generated LOINC mappings. While LLMs can reduce workload by generating candidate mappings, human validation remains essential to ensure clinical accuracy. Future improvements should focus on algorithmic refinement, error feedback integration, and adaptation to diverse laboratory settings to enhance accuracy and generalisability in real-world laboratory settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106270"},"PeriodicalIF":4.1,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145935900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between the platelet to white blood cell ratio and short term mortality in critically ill patients with atherosclerotic cardiovascular disease: A retrospective study and machine learning with external validation","authors":"Zhantao Cao ,&nbsp;Hewei Qin ,&nbsp;Yue Niu ,&nbsp;Zilu Zhang ,&nbsp;Guoju Dong","doi":"10.1016/j.ijmedinf.2026.106267","DOIUrl":"10.1016/j.ijmedinf.2026.106267","url":null,"abstract":"<div><h3>Background</h3><div>The platelet to white blood cell ratio (PWR) has shown prognostic value in many diseases. Yet its predictive utility for patients with atherosclerotic cardiovascular disease (ASCVD) who receive care in the intensive care unit (ICU) remains uncertain. We examined whether PWR at ICU admission is associated with short term all cause mortality among ICU patients with ASCVD.</div></div><div><h3>Methods</h3><div>We used the MIMIC IV and eICU databases to study the association between PWR and 30 day all cause mortality in critically ill patients with ASCVD. Patients were grouped by PWR quartiles. Collinearity was checked with the variance inflation factor (VIF). Nonlinearity was assessed with restricted cubic splines(RCS). Survival was compared with Kaplan Meier(KM) curves and the log rank test. Hazard ratios were estimated with stratified and adjusted Cox models. We also built machine learning models that included PWR and clinical features selected with the Boruta algorithm to predict 30 day mortality. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). External validation in the eICU database was used to assess generalizability.</div></div><div><h3>Results</h3><div>A total of 10 943 ICU patients with ASCVD were included, 62 % were men, and the median age was 71 years. The highest PWR quartile had a lower 30 day all cause mortality than the lowest quartile, 11 % versus 15 % (<em>p</em> &lt; 0.001). In multivariable Cox models the highest quartile had a lower risk (HR 0.80, 95 % CI 0.67 to 0.95, <em>p</em> = 0.012). RCS suggested a nonlinear association. Age modified the association, with a stronger protective effect in patients younger than 70 years (HR 0.64, 95 % CI 0.47 to 0.87, interaction <em>p</em> &lt; 0.001). The best machine learning model achieved an AUC of 0.812 in internal validation and 0.80 in external validation. SHAP analysis showed that higher PWR was linked to a lower predicted risk of death.</div></div><div><h3>Conclusion</h3><div>PWR independently predicts 30 day all cause mortality in ICU patients with ASCVD. These findings support the use of PWR for risk stratification and to inform management in critical care for ASCVD.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106267"},"PeriodicalIF":4.1,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A topic modeling analysis of stigma dimensions, social, and related behavioral circumstances in clinical notes among patients with HIV","authors":"Ziyi Chen ,&nbsp;Yiyang Liu ,&nbsp;Mattia Prosperi ,&nbsp;Krishna Vaddiparti ,&nbsp;Robert L. Cook ,&nbsp;Jiang Bian ,&nbsp;Yi Guo ,&nbsp;Yonghui Wu","doi":"10.1016/j.ijmedinf.2026.106269","DOIUrl":"10.1016/j.ijmedinf.2026.106269","url":null,"abstract":"<div><h3>Objective</h3><div>To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV (PLWHs) seeking care, using natural language processing methods applied to a large collection of electronic health record (EHR) clinical notes from a large integrated health system in the southeast United States.</div></div><div><h3>Methods</h3><div>We identified a cohort of PLWHs from the University of Florida (UF) Health Integrated Data Repository and performed topic modeling analysis using Latent Dirichlet Allocation (LDA) to uncover stigma-related dimensions and related social and behavioral contexts. Domain experts created a seed list of HIV-related stigma keywords, then applied a snowball strategy to iteratively review notes for additional terms until saturation was reached. To identify more target topics, we tested three keyword-based filtering strategies. The detected topics were evaluated using three widely used metrics and manually reviewed by specialists. Word frequency analysis was used to highlight the prevalent terms associated with each topic. In addition, we conducted topic variation analysis among subgroups to examine differences across age- and sex-specific demographics.</div></div><div><h3>Results</h3><div>We identified 9,140 PLWHs at UF Health and collected 2.9 million clinical notes. Through the iterative keyword approach, we generated a list of 91 keywords associated with HIV-related stigma. Topic modeling on sentences containing at least one keyword uncovered a wide range of topic themes associated with HIV-related stigma, social, and related behaviors circumstances, including “Mental Health Concern and Stigma”, “Social Support and Engagement”, “Limited Healthcare Access and Severe Illness”, “Missed Appointments and HIV Care Monitoring”, “Treatment Refusal and Isolation”, “Intimate Partner Violence and Relationship Concerns”, “Fear of Falling and Physical Health Concerns”, “Substance Abuse”, and “Food Insecurity and Resource Scarcity”. Topic variation analysis across sex and age subgroups revealed no substantial difference between males and females; however, there were differences were observed among different ages. For example, “Fear of Falling and Physical Health Concerns” was notably more prevalent among older adults.</div></div><div><h3>Conclusion</h3><div>Extracting and understanding the HIV-related stigma and associated social and behavioral circumstances from EHR clinical notes enables scalable, time-efficient assessment and overcoming the limitations of traditional questionnaires. Findings from this research provide actionable insights to inform patient care and interventions to improve HIV-care outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106269"},"PeriodicalIF":4.1,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940467","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}