JAMIA Open最新文献

{"title":"Identification of obesity risk factors in 3-12-year-old children and adolescents with prior respiratory tract infections via interpretable machine and deep learning models.","authors":"Xiao-Qian Wang, Fang-Jie-Yi Zheng, Qiong Wang, Che Li, Wen-Qian Zhang, Zhi-Xin Zhang, Wen-Quan Niu","doi":"10.1093/jamiaopen/ooag061","DOIUrl":"10.1093/jamiaopen/ooag061","url":null,"abstract":"<p><strong>Backgrounds and objectives: </strong>Childhood obesity and respiratory tract infections (RTIs) are 2 major global public health issues that frequently co-occur and are closely interrelated. Early detection of children with prior RTIs who are at high obesity risk is crucial for targeted interventions. This study integrates interpretable machine learning (ML) models and a deep learning network to develop an obesity risk prediction model in a large pediatric cohort.</p><p><strong>Methods: </strong>Cross-sectional data from 6509 children and adolescents aged 3-12 years with prior RTIs in Beijing and Tangshan were fed to 12 ML models to predict childhood obesity (versus children with normal weight). Bayesian optimization was applied to fine-tune model hyperparameters. Prediction performance was assessed using 8 metrics. Key predictive features were identified by SHapley Additive exPlanations (SHAP). The validity of the optimal ML model was verified by the sequential neural network model.</p><p><strong>Results: </strong>Of 12 ML models, LightGBM achieved the optimal performance (accuracy: 0.8844, area under the curve [AUC]: 0.9491). SHAP analysis identified 20 key predictors, including child age, paternal body mass index (BMI), maternal BMI, birth length, birthweight, gestational age, eating speed, complementary feeding initiation age, screen time, breastfeeding duration, bedtime, maternal age, nighttime sleep, outdoor activity, dental caries, sedentary time, food allergies, family history of diabetes, and sex. The deep learning sequence network model further validated the predictive value of these features (accuracy: 0.8023, AUC: 0.8117), and the SHAP-driven feature importance rankings were in close line with LightGBM.</p><p><strong>Conclusions: </strong>Our LightGBM-based model enables effective prediction of obesity risk in children aged 3-12 years with prior RTIs, and the key features identified can inform early screening and facilitate personalized interventions.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag061"},"PeriodicalIF":4.7,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148685979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A phenome-wide association study of rurality in the <i>All of Us</i> Research Program.","authors":"Anas H Awan, Katharine S Chaillet, Cassia Y Williams-Rogers, Yamna Channa, Dayo A Shittu, Bennett J Waxse, David J Schlueter, Tracey M Ferrara, Joshua C Denny, Huan Mo","doi":"10.1093/jamiaopen/ooag086","DOIUrl":"10.1093/jamiaopen/ooag086","url":null,"abstract":"<p><strong>Objectives: </strong>Disease profiles and access to care differ between rural and urban populations. The aim of this study is to examine the association of rurality with electronic health record-derived disease profiles and heart failure risk.</p><p><strong>Materials and methods: </strong>We conducted a retrospective observational study of 242 758 participants enrolled in the <i>All of Us</i> Research Program. Rurality status was estimated using 2010 Rural-Urban Commuting Area codes from participant home 3-digit zip code prefixes, and participants were grouped into rural, mixed, and urban arms. We compared the disease profiles with phenome-wide association studies and the risks of heart failure between arms.</p><p><strong>Results: </strong>The final cohort included 5581 participants in the rural arm, 62 287 in the mixed arm, and 174 890 in the urban arm. Compared to the urban setting, 70 phecodes were significantly enriched in the rural arm including obesity (odds ratio [OR], 1.42, 95% confidence interval [CI], 1.34-1.51) and related diagnoses. Cancer screening-, dermatological-, and eye care-related diagnoses were significantly depleted in the rural arm. The rural arm also showed a greater risk of heart failure (adjusted hazard ratio [HR], 1.20, 95% CI, 1.10-1.31).</p><p><strong>Discussion and conclusion: </strong>With the national dataset in <i>All of Us</i>, we found that rural participants had significantly higher risks for obesity-related diseases and heart failure in this study. Depleted phecodes in the rural participants suggested a lack of access to cancer screening, stressing the potential importance of targeted cancer disease management in rural communities.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag086"},"PeriodicalIF":4.7,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computable phenotypes for research using real-world data: experiences from the NIH pragmatic trials collaboratory.","authors":"Marisa L Conte, Judith M Schlaeger, Guilherme Del Fiol, James R Campbell, Andrea L Cheville, Keith Marsolo, Kari A Stephens, Miriam O Ezenwa, Andrew D Boyd, Juanita E Darby, Nadine S Matthie, P Michael Ho, Alice Pressman, Morgan Justice, Carol Reynolds Geary, Keturah R Faurot, Alex Fist, Karen Staman, Hulin Wu, Rachel L Richesson","doi":"10.1093/jamiaopen/ooag158","DOIUrl":"10.1093/jamiaopen/ooag158","url":null,"abstract":"<p><strong>Objectives: </strong>Embedded pragmatic clinical trials (ePCTs) are conducted as part of routine clinical care and therefore use data collected from real-world data sources, such as electronic health record systems and administrative claims. A common approach for using these types of data across all phases of trial conduct is to create a computable phenotype (an explicitly defined data query data, including specified data types, data codes, and logical parameters) to capture patients with a clinical condition, exposure, symptom, characteristic, treatment, or outcome of interest.</p><p><strong>Materials and methods: </strong>The Electronic Health Record (EHR) Core Working Group of the NIH Pragmatic Trials Collaboratory captured the experiences of investigators in developing, adapting, and applying computable phenotype definitions in their studies.</p><p><strong>Results: </strong>Four case studies describe different approaches to developing and using computable phenotypes in pragmatic trials.</p><p><strong>Discussion: </strong>We recommend: (1) developing computable phenotypes as part of a team with multiple areas of expertise; (2) appropriate validation; (3) dissemination of salient details regarding phenotype creation; and (4) capturing and reporting any modifications made to phenotypes during the conduct of the trial.</p><p><strong>Conclusion: </strong>A range of issues and decisions influence how computable phenotypes are developed and used in pragmatic clinical trials. Multidisciplinary teams that understand the context of the data, including the reason the data are collected and potential sources of bias, are best suited for the development and validation of phenotypes for ePCTs.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag158"},"PeriodicalIF":4.7,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emoji in clinical messaging: results of a focus group study.","authors":"Colin M E Halverson, Haley Echols, Lauren Kang, Samantha Vershaw, Joy L Lee","doi":"10.1093/jamiaopen/ooag159","DOIUrl":"10.1093/jamiaopen/ooag159","url":null,"abstract":"<p><strong>Background: </strong>Clinicians are increasingly using emoji in digital communication, but limited qualitative work has examined how they assess the appropriateness, risks, and benefits of this practice.</p><p><strong>Objective: </strong>To characterize clinicians' reported utilization of and attitudes toward emoji in clinical messaging.</p><p><strong>Design: </strong>Qualitative study using focus groups and a survey from August to October 2025.</p><p><strong>Participants: </strong>Twenty-nine clinicians at a large academic health system were recruited from 4 specialties and included physicians and advanced practice providers, genetic counselors, medical students, and other healthcare workers.</p><p><strong>Approach: </strong>Rapid qualitative analysis was used to identify both the benefits and drawbacks of emoji use in clinical messaging.</p><p><strong>Key results: </strong>Emoji were seen as clarifying tone, building rapport, softening directives, and reducing notification fatigue. However, participants also reported concerns about ambiguity, informality, and potential medicolegal risk due to messages' discoverability. Their assessment of appropriateness was context-dependent, including power hierarchies, personal familiarity, clinical gravity, generational differences, and the technical affordances of specific platforms. Emoji were viewed as more acceptable in informal contexts and among peers with whom they had established relationships, and as inappropriate in high-stakes and sensitive clinical contexts. Participants felt formal guidelines could be seen as condescending but suggested modifications to promote the clear and effective use of emoji in professional settings.</p><p><strong>Conclusions: </strong>Clinicians view emoji as useful but context-sensitive communication tools that should be used conscientiously. They disprefer universal, prescriptive guidelines. However, they promote explicit conversations about emoji use at onboarding and the modification of which emoji are available in local messaging platforms.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag159"},"PeriodicalIF":4.7,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From prediction to practice: machine learning models and real-world interventions for magnetic resonance imaging appointment no-shows in a hospital setting.","authors":"Mark McMahon, Jonas Kluckert, Olivio F Donati, Nicolas Andres Perez Gonzalez, Laura Kinkead, Christian Blüthgen, Andreas Hötker, Thomas Frauenfelder, Michael Krauthammer","doi":"10.1093/jamiaopen/ooag151","DOIUrl":"10.1093/jamiaopen/ooag151","url":null,"abstract":"<p><strong>Objective: </strong>To develop a machine learning (ML) model to predict magnetic resonance imaging (MRI) appointment no-shows and test the effectiveness of a targeted phone call intervention in reducing the no-show rate.</p><p><strong>Materials and methods: </strong>Outpatient MRI appointments from a university hospital (2015-2021) were used to train and compare Logistic Regression, Random Forest, and XGBoost models. The best-performing model underwent a 13-month prospective evaluation, followed by a 9-month intervention study where high-risk patients were randomized to a phone call reminder or a control group.</p><p><strong>Results: </strong>Our dataset included 38 141 appointments (16.6% no-show) in total. On a held-out validation dataset, XGBoost performed best (AUROC 0.65; AUPRC 0.29). In a prospective evaluation round (5882 appointments), performance decreased (AUROC 0.61; AUPRC 0.24), due in part to observed data drift in appointment types and reasons. The intervention study (274 patients called, 158 answered, 116 not reached; 460 control appointments) showed no overall change in no-show rate (no-show: 21.9% intervention vs 22.0% control; <i>P</i> = .48). Within the intervention group, answering the call was associated with lower no-show rates (18.4% of those reached vs 26.7% not reached).</p><p><strong>Discussion: </strong>Models identified high-risk patients, but translating predictions into attendance improvements proved challenging. Effectiveness was limited by data drift and intervention reach. Results suggest value in dual-prediction strategies (including potential intervention responsiveness) and continuous model monitoring.</p><p><strong>Conclusion: </strong>ML can identify likely no-shows, but reducing missed appointments requires robust, up-to-date models and interventions that reliably reach patients; more nuanced, targeted engagement may yield greater impact than phone calls alone.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag151"},"PeriodicalIF":4.7,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13437018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validating medical digital twins for clinical decision support: beyond predictive accuracy.","authors":"Alexandre Vallée","doi":"10.1093/jamiaopen/ooag160","DOIUrl":"10.1093/jamiaopen/ooag160","url":null,"abstract":"<p><strong>Objective: </strong>To clarify how validation requirements should be specified for medical digital twins used in clinical decision support, particularly when such systems are intended to compare interventions, treatment timings, dosages, or sequential care strategies.</p><p><strong>Perspective: </strong>Medical digital twins are heterogeneous systems that may combine prediction, simulation, mechanistic modeling, machine learning, data assimilation, uncertainty quantification, and decision-support functions. Their evaluation should therefore be driven by their intended use rather than by a single definition of what a digital twin is. For digital twins used primarily for visualization, monitoring, or short-term forecasting, predictive accuracy, calibration, discrimination, and robustness may be the central validation targets. However, when digital twins are used to support intervention-oriented clinical decisions, retrospective accuracy under historical clinical practice is insufficient on its own.</p><p><strong>Key message: </strong>Intervention-oriented digital twins address action-conditioned questions: what is predicted to happen under specified alternative actions, assumptions, time horizons, and clinical contexts. Their validation should therefore extend beyond scalar performance metrics to include uncertainty representation, updating stability, robustness under regime change, action-regime validity, counterfactual consistency, clinically weighted error, and decision-level consequences. This requires drawing on established traditions in forecast verification, causal inference, uncertainty quantification, model verification and validation, decision theory, control theory, and post-deployment monitoring. The level of causal or mechanistic support required should match the clinical claim being made, whether at the genotype, phenotype, physiological, or care-process level.</p><p><strong>Conclusion: </strong>The scientific-instrument framing is proposed as a pragmatic validation lens for intervention-oriented digital twins, not as a universal definition of digital twins. It helps define the scope within which their outputs can support clinical reasoning. Medical digital twins should be accompanied by explicit validation statements specifying their target population, prediction horizon, supported interventions, uncertainty bounds, and known failure conditions.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag160"},"PeriodicalIF":4.7,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A tabular prior-data fitted network-based prediction model integrating ferritin and total iron-binding capacity for 30-day all-cause mortality in critically ill cancer patients.","authors":"Lin-Lin Liu, Lei Han, Zuo-Lin Xiang","doi":"10.1093/jamiaopen/ooag148","DOIUrl":"10.1093/jamiaopen/ooag148","url":null,"abstract":"<p><strong>Objectives: </strong>Accurately predicting outcomes for critically ill cancer patients remains challenging. This study aimed to integrate biologically relevant iron metabolism markers into short-term mortality prediction and to develop an interpretable, externally validated model based on a tabular prior-data fitted network (TabPFN) for risk stratification in this population.</p><p><strong>Materials and methods: </strong>We conducted a retrospective cohort study using data from Medical Information Mart for Intensive Care IV (MIMIC-IV) (training and internal validation) and eICU Collaborative Research Database (eICU-CRD) (external validation), including critically ill adult patients with cancer. Associations between iron metabolism markers (ferritin, serum iron, and total iron-binding capacity [TIBC]) and 30-day all-cause mortality were assessed using the Kaplan-Meier curves, multivariable Cox regression, and restricted cubic splines. A TabPFN-based prediction model was developed and compared with 8 other machine-learning algorithms, with the least absolute shrinkage and selection operator used for feature selection. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), calibration, and decision curve analysis. SHapley Additive exPlanation values provided model interpretability.</p><p><strong>Results: </strong>Among 1137 patients in the MIMIC-IV cohort, 293 (26.0%) died within 30 days. Elevated ferritin (hazard ratio [HR] = 1.18, 95% CI, 1.09-1.27) and decreased TIBC (HR = 0.92, 95% CI, 0.85-0.99) were independently associated with mortality and exhibited non-linear relationships. Serum iron showed no prognostic value. The TabPFN model achieved the best performance, with an AUROC of 0.865 (95% CI, 0.812-0.919) in the internal validation set and 0.772 (95% CI, 0.744-0.814) in the external validation set, along with good calibration (Brier score = 0.121) and clinical net benefit. SHapley Additive exPlanation analysis identified lymphocyte count, platelet count, lactate, and ferritin as the most influential predictors.</p><p><strong>Conclusion: </strong>Iron metabolism dysregulation-particularly altered ferritin and TIBC levels-has important prognostic value in critically ill cancer patients. By integrating these biomarkers with a TabPFN framework, this study provides an accurate, interpretable, and externally validated tool for 30-day all-cause mortality prediction that may support clinical decision-making.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag148"},"PeriodicalIF":4.7,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13425414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-world evaluation of a transformer-based natural language processing system for identifying social determinants of health from routine clinical documentation.","authors":"Xiangren Wang, Jingchuan Guo, Yi Guo, Yao An Lee, Mattia Prosperi, Jiang Bian, Noah Hammarlund","doi":"10.1093/jamiaopen/ooag147","DOIUrl":"10.1093/jamiaopen/ooag147","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the real-world performance of a transformer-based natural language processing (NLP) system for extracting Social Determinants of Health (SDoH) from clinical notes, using survey-based SDoH measures a reference comparators.</p><p><strong>Materials and methods: </strong>This study was conducted at the University of Florida Health in adults with at least 2 clinical encounters in the prior year. A research survey was completed by 1001 participants; sampling targeted 50% Black patients to support subgroup analyses. Comparative analyses were restricted to the 414 participants who also had Epic SDoH survey data and clinical notes available for NLP extraction. We compared concepts extracted by the SOcial DeterminAnts (SODA) NLP pipeline against the independently administered research survey, which served as the primary reference standard, and against the structured Epic-embedded SDoH questionnaire. Nine domains were evaluated: abuse, alcohol use, drug use, education, financial constraints, housing, physical activity, social cohesion, and transportation. Sensitivity, specificity, positive predictive value, negative predictive value, and F1 scores were calculated by domain.</p><p><strong>Results: </strong>The NLP pipeline more consistently aligned with negative survey responses than with patient-reported social needs, although performance was lower in some domains, especially alcohol use and financial constraints. Sensitivity was higher only for alcohol use (55%); the lowest values were for abuse (5%), drug use (0%), and financial constraints (16%). These results cannot be attributed to SODA extraction alone. They reflect some combination of social information not being recorded in clinical notes, content that was recorded but not extracted, and mismatches between extracted concepts and survey definitions, and the present analysis cannot separate these contributions. The 2 surveys agreed only modestly with each other, so no single instrument provides a definitive ground truth.</p><p><strong>Conclusion: </strong>The pipeline more consistently aligned with negative survey responses than with patient-reported social needs. Because the 2 surveys agreed only modestly, the reference itself is imperfect, and apparent NLP performance depends in part on which survey is used as the comparator. Apparent gaps in NLP performance reflect both how social risks are recorded in clinical notes and how patients disclose them across different survey settings, in addition to limits of the extraction pipeline. Improving documentation practices, integrating locally tuned large language models, and monitoring subgroup performance may all be needed to make SDoH identification tools reliably detect social needs across patient populations.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag147"},"PeriodicalIF":4.7,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of classification models for adolescent's emotional and behavioral problems.","authors":"Akhyt Tilyeubai, Javzmaa Tsend, Nyamdavaa Uugandavaa, Bayarmaa Vaanchindorj, Purevdolgor Luvsantseren, Ajnai Luvsan-Ish, Baasandorj Chilkhaasuren, Galbadrakh Chuluunbaatar, Enkh-Urel Enkhjargal","doi":"10.1093/jamiaopen/ooag144","DOIUrl":"10.1093/jamiaopen/ooag144","url":null,"abstract":"<p><strong>Objectives: </strong>Mental disorders are very common among children and adolescents around the world. In Mongolia, the international Strengths and Difficulties Questionnaire (SDQ) is used to detect mental disorders in adolescents. In this article, different types of classification methods were compared to determine adolescent emotional and behavioral problems using the SDQ.</p><p><strong>Materials and methods: </strong>Data were collected from teenagers, teachers, and parents in Govi-Altai Province, and the databases were created for each group. The teenager database was divided into 10 folds using cross-validation, and the models were developed using classification methods and evaluated using performance measures. The results were mainly analyzed using the Bayes model.</p><p><strong>Results: </strong>The teenagers have emotional and behavioral problems due to emotional and peer interactions, but they were at risk of developing disorders due to hyperactivity and behavioral changes.</p><p><strong>Conclusion: </strong>Future work will examine the difficulties encountered in the creation of emotional and behavioral problems among the teenagers involved in the study using a progressive classification method.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag144"},"PeriodicalIF":4.7,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language model-based extraction of rheumatoid arthritis clinical disease activity index from clinical notes.","authors":"Reid Weisberg, Ruoqi Yang, Iram Kamdar, Justin Chestang, Joan Bathon, Jon T Giles, Chunhua Weng, Elizabeth Park","doi":"10.1093/jamiaopen/ooag152","DOIUrl":"10.1093/jamiaopen/ooag152","url":null,"abstract":"<p><strong>Objectives: </strong>We extracted a validated disease activity measure in rheumatoid arthritis (RA), the Clinical Disease Activity Index (CDAI), from a large tertiary academic medical center electronic health record (EHR) using an automated large language model (LLM)-based approach without requiring model pretraining.</p><p><strong>Materials and methods: </strong>The New York Presbyterian/Columbia University Medical Center Clinical Data Warehouse contains EHR data for over 4.5 million patients. RA patients were identified using International Classification of Disease-9 (ICD-9) and ICD-10 codes. Expert-curated CDAI keywords were extracted from unstructured notes using an automated natural language processing (NLP) pipeline leveraging GPT-4o API, a HIPAA-compliant, institutionally approved LLM platform. Performance was evaluated against expert chart review.</p><p><strong>Results: </strong>Among 2756 RA patients with notes, 1038 (37.7%) were seropositive, 796 (28.9%) were seronegative, and 922 (33.4%) had unknown serostatus. Clinical Disease Activity Index and its components were extracted in 15.4% (160/1038) of seropositive patients indicating remission or low disease activity. Clinical Disease Activity Index documentation was more frequent among patients with multiple notes and among faculty, with high extraction accuracy (precision/recall/F1 = 0.97).</p><p><strong>Discussion: </strong>This represents the first attempt to employ a zero-shot, ChatGPT-powered LLM platform to extract RA disease activity measures from real-world EHR data. Although a low prevalence of documentation was noted, important distinctions were observed when patients were subgrouped by serostatus, level of training, and number of visits.</p><p><strong>Conclusion: </strong>An LLM-based pipeline accurately extracted CDAI from a single large academic EHR, revealing infrequent real-world documentation.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 4","pages":"ooag152"},"PeriodicalIF":4.7,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}