Healthcare Informatics Research最新文献

{"title":"Evaluation and Comparison of Machine Learning Methods for Type 2 Diabetes Classification and Associated Factors.","authors":"Masoumeh Dadashpour, Mohammad Yousefi, Sohrab Effati, Somayeh Ghiasi Hafezi","doi":"10.4258/hir.2026.32.3.214","DOIUrl":"10.4258/hir.2026.32.3.214","url":null,"abstract":"<p><strong>Objectives: </strong>Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disorder associated with serious complications, including nephropathy, cardiovascular disease, retinopathy, and neuropathy. Given its increasing incidence and the complexity of associated factors-such as obesity, metabolic syndrome, and sedentary lifestyle-accurate identification is essential. This study aimed to evaluate and compare the performance of several machine learning algorithms to identify key associated factors and detect individuals with T2DM within this dataset.</p><p><strong>Methods: </strong>A publicly available dataset from Kaggle, comprising health records of 99,982 individuals, was used. Five supervised machine learning models were evaluated: Bayesian ridge regression, logistic regression, extreme gradient boosting (XGBoost), artificial neural networks, and random forest. Each model was trained and evaluated to assess classification performance. Performance was measured using the area under the receiver operating characteristic curve (AUC-ROC) and accuracy. SHapley Additive Explanations (SHAP) values were used to interpret model outputs and identify the most influential features.</p><p><strong>Results: </strong>Among the five models, XGBoost demonstrated the highest performance, achieving an accuracy of 96% and an AUC-ROC of 0.98. SHAP analysis identified hemoglobin A1c, blood glucose, age, body mass index, and sex as the most influential predictors of T2DM.</p><p><strong>Conclusion: </strong>XGBoost was the most effective algorithm for identifying individuals with T2DM in this dataset. It also provided insights into the relative importance of clinical features, supporting more precise classification. However, results should be interpreted with caution until validated in independent cohorts.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"214-223"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812885","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":"Machine Learning-Based Classification of Active and Latent Phases of Inherited Retinal Dystrophies Using Synthetic Proteomic Data: A Pathway-Based Application Exercise.","authors":"Alessandro Macchia, Maria Chiara Medori, Ahmad Jainul Abidin, Gabriele Bonetti, Kristjana Dhuli, Luca Ferrari, Sara Feizyab, Jan Miertuš, Benedetto Falsini, Giorgio Placidi, Pietro Chiurazzi, Ornela Gordani, Xhilda Dhamo, Eglantina Kalluci, Dominika Vešelényiová, Stanislav Miertuš, Iveta Dirgová Luptáková, Jiří Pospíchal, Matteo Gregorini, Matteo Bertelli","doi":"10.4258/hir.2026.32.3.285","DOIUrl":"10.4258/hir.2026.32.3.285","url":null,"abstract":"<p><strong>Objectives: </strong>Inherited retinal dystrophies are characterized by high genetic and phenotypic heterogeneity, and their clinical progression may alternate between latent and active phases. Identifying the onset of the active phase may support earlier intervention for inflammatory retinal degeneration. Plasma proteomics has shown potential for characterizing predictive biomarkers in retinal diseases, but its application remains experimental. This study aimed to develop a methodological simulation exercise to evaluate the performance of machine learning (ML) models in distinguishing active and latent phases using an artificially generated dataset.</p><p><strong>Methods: </strong>An artificial dataset of 500 samples was created, and plasma proteomic profiles were generated for each sample using arbitrary values. Sample classification was based on a pathway activation score. Four ML models were tested: support vector machine, random forest, logistic regression, and extreme gradient boosting. Each model was trained across a range of hyperparameters.</p><p><strong>Results: </strong>Logistic regression achieved the best performance, with an accuracy of 0.73, precision of 0.73, and F1-score of 0.73.</p><p><strong>Conclusions: </strong>This simulation study showed that synthetic proteomic datasets can be used to evaluate ML approaches for distinguishing active and latent phases of retinal dystrophies when real data are scarce. Synthetic data can support the creation of targeted datasets for proteins associated with retinal dystrophies, helping to address the limited availability of suitable open-source data.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"285-292"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812887","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":"Comparative Analysis of Regularised Logistic Regression and Random Forest Models for In-hospital or 30-day Post-Discharge Mortality Prediction within the Hospital Standardised Mortality Ratio Framework.","authors":"Mohd Kamarulariffin Kamarudin, Sarbhan Singh Lakha Singh, Nuur Hafizah Md Iderus, Sumarni Mohd Ghazali, Lonny Chen Rong Qi Ahmad, Nur'ain Mohd Ghazali, Mohamad Nadzmi Md Nadzri, Asrul Anuar Zulkifli, Mei Cheng Lim, Chien Huey Teh, Mohd Azahadi Omar","doi":"10.4258/hir.2026.32.3.251","DOIUrl":"10.4258/hir.2026.32.3.251","url":null,"abstract":"<p><strong>Objectives: </strong>The hospital standardised mortality ratio (HSMR) is the ratio of the observed number of hospital deaths to the expected number of deaths, with the latter estimated using statistical models that adjust for available case-mix factors. This study aimed to develop and validate in-hospital or 30-day post-discharge mortality prediction models for 40 diagnosis groups within the HSMR framework, using penalized logistic regression (pLR) and random forest (RF), and to compare the performance of these two approaches.</p><p><strong>Methods: </strong>We analysed 1,144,890 hospital admissions from 14 Malaysian state hospitals between 2012 and 2016. Separate models were developed for each diagnosis group using nine administrative features, including age, comorbidities, and admission category. Model performance was evaluated using mean Brier scores and Cstatistics across multiple bootstrapped datasets to obtain less biased performance estimates. Aggregate expected mortality counts were also compared with observed counts.</p><p><strong>Results: </strong>The overall observed mortality rate was 10.2%. The pLR models consistently showed better discrimination and calibration than the RF models, with lower Brier scores and higher C-statistics across the 40 diagnosis groups. On average, the C-statistic for pLR exceeded that for RF by 0.062. Although the RF model produced aggregate mortality predictions that were numerically closer to the observed counts, it showed high variance and poorer probabilistic calibration than pLR.</p><p><strong>Conclusions: </strong>The pLR model tended to underestimate mortality more than RF but still demonstrated better calibration and discrimination, making it the preferable model for HSMR analysis in this dataset.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"251-261"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812912","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":"Application of Machine Learning Algorithms for Predicting Infant Mortality in India: An Analysis of the National Family Health Survey-5, 2019-2021.","authors":"Jaya Prasad Tripathy","doi":"10.4258/hir.2026.32.3.232","DOIUrl":"10.4258/hir.2026.32.3.232","url":null,"abstract":"<p><strong>Objectives: </strong>Machine learning (ML) techniques have shown strong potential for predicting infant mortality (IM), but their application in the Indian context remains limited. This study aimed to use ML algorithms to predict IM in India using a large national survey database.</p><p><strong>Methods: </strong>Data were analyzed from the National Family Health Survey-5, 2019-2021, a large cross-sectional survey. Random forest, decision tree, adaptive boosting, logistic regression, and naïve Bayes models were implemented using Weka version 3.8.3. Model performance was evaluated using accuracy, precision, F1-score, Matthews correlation coefficient, and area under the curve (AUC).</p><p><strong>Results: </strong>Compared with logistic regression, which achieved 65.2% accuracy, the random forest and decision tree models showed higher predictive accuracy, at 74.1% and 73.2%, respectively. Their AUCs were 0.80 and 0.79, respectively, compared with 0.69 for logistic regression. The models identified birth order, maternal education, twin birth, wealth index, cooking fuel use, and age at first birth as the six strongest predictors of IM.</p><p><strong>Conclusions: </strong>In this analysis, random forest and decision tree models outperformed logistic regression in predicting IM. These findings underscore the relevance of sociodemographic and economic disparities and support the use of ML algorithms for risk prediction and targeted interventions aimed at reducing IM.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"232-241"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812897","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":"Machine Learning Techniques to Predict Fetal Nutritional Status.","authors":"Neema Mduma, Hudson Laizer","doi":"10.4258/hir.2026.32.3.242","DOIUrl":"10.4258/hir.2026.32.3.242","url":null,"abstract":"<p><strong>Objectives: </strong>Malnutrition remains the leading cause of child mortality in Tanzania, with over 34% of children under 5 years of age affected by stunting and approximately 5% experiencing acute malnutrition. This study aimed to develop a machine learning model to predict fetal nutritional status using maternal and clinical data, thereby enabling early risk identification for health workers and parents and facilitating timely intervention. To enhance practical applicability, the model was deployed within a mobile application to provide accessible, real-time predictions that support prompt clinical and behavioral responses.</p><p><strong>Methods: </strong>Using a dataset collected in Tanzania, the performance of multiple binary classification algorithms-logistic regression, multi-layer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM)-was compared using the geometric mean and F-measure. These models were trained on clinical data from 11,703 pregnant women to predict fetal nutritional status based on maternal and clinical variables.</p><p><strong>Results: </strong>The results indicated that the LightGBM algorithm achieved the best overall performance in predicting fetal nutritional status. The most influential predictors included maternal age, weight, fetal age, hemoglobin level, number of meals per day, medical history, and education level. Additionally, 93% of respondents reported satisfaction with the application's predictive functionality, supporting its potential utility for early intervention in low-resource settings.</p><p><strong>Conclusions: </strong>These findings highlight the potential of data-driven approaches to address public health challenges in maternal and child health. The proposed model may enable healthcare providers to make timely, informed decisions that improve maternal and fetal outcomes, ultimately contributing to the reduction of child malnutrition in Tanzania.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"242-250"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812951","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":"Early Detection of Parkinson's Disease Using Automatic Classification of Single-photon Emission Computed Tomography Images.","authors":"Jihad Boucherouite, Abdelilah Jilbab, Atman Jbari","doi":"10.4258/hir.2026.32.3.274","DOIUrl":"10.4258/hir.2026.32.3.274","url":null,"abstract":"<p><strong>Objectives: </strong>Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by central nervous system dysfunction. Early identification may enable prompt treatment and help slow the progression of disabling symptoms. Previous studies have reported that clinical assessments based on visual interpretation may be insufficiently accurate and often miss early PD. Therefore, this study aimed to develop an automated single-photon emission computed tomography-based model for binary classification of healthy controls and patients with early PD.</p><p><strong>Methods: </strong>We analyzed 514 DaTSCAN images from the Parkinson's Progression Markers Initiative database, using one unique scan per individual. The workflow comprised three main stages: image processing; computation of 23 features, including radial, threshold, boundary, and striatal binding ratio features; and image classification using machine learning algorithms.</p><p><strong>Results: </strong>The medium Gaussian support vector machine achieved an accuracy of 97.09% ± 1.53% (95% confidence interval, 95.11%-99.07%), sensitivity of 98.18% ± 1.48%, specificity of 93.85% ± 3.44%, and area under the receiver operating characteristic curve of 98.49% ± 1.31%. This performance was significantly higher than that of the three-dimensional convolutional neural network baseline model (accuracy, 94.55% ± 2.98%; p = 0.045), while requiring substantially less training time.</p><p><strong>Conclusions: </strong>In this dataset, carefully designed feature engineering combined with classical machine learning outperformed the deep-learning baseline when the training data were limited and the selected features aligned with clinical diagnostic criteria. This approach achieved high accuracy for early PD detection and may provide computational efficiency and interpretability suitable for clinical implementation.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"274-284"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812894","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":"Legal Barriers to Implementing AI-Enabled Emergency Medical Services in Korea: Focus on the Medical Service Act, Emergency Medical Service Act, and Personal Information Protection Act.","authors":"Eun-Jeong Lee, Yeon-Woo Choi, Yeail Joo, Min Lee, Hyekyeong Kim, Chaemin Shin","doi":"10.4258/hir.2026.32.3.293","DOIUrl":"10.4258/hir.2026.32.3.293","url":null,"abstract":"<p><strong>Objectives: </strong>In prehospital and in-hospital emergency care, artificial intelligence (AI)-enabled emergency medical services (EMS) systems are reshaping clinical workflows through real-time triage, automated hospital assignment, telemedicine, and biometric monitoring. However, their implementation in Korea has revealed substantial legal and regulatory barriers. This study analyzed the structural incompatibilities between AI-enabled EMS and Korea's current legal framework, with particular attention to the Medical Service Act, Emergency Medical Service Act, and Personal Information Protection Act.</p><p><strong>Methods: </strong>We conducted a statutory review and case analysis of pilot projects, including the Chungbuk Smart EMS Project, the AI HOTline in Ilsan, and Yonsei's AI-Ambulance model. We also performed a comparative assessment of foreign regulatory frameworks, including the US Health Insurance Portability and Accountability Act and the EU General Data Protection Regulation.</p><p><strong>Results: </strong>Key barriers included the restriction of remote care to interphysician communication under Article 34 of the Medical Service Act, the lack of legal mandates for real-time hospital capacity sharing under the Emergency Medical Service Act, and limits on real-time sensitive-data processing caused by vague emergency clauses in the Personal Information Protection Act. These barriers constrain the scalability of AI-enabled EMS in Korea.</p><p><strong>Conclusions: </strong>To support lawful and secure integration of AI-driven emergency care in Korea, we propose legislative revisions that establish emergency exceptions for remote AI-supported guidance, require hospital resource disclosure, and provide safe-harbor protections for healthcare professionals who rely on certified AI tools. A harmonized legal infrastructure is essential for realizing the potential of digital transformation in emergency care.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"293-302"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812924","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":"Development and Evaluation of a Custom GPT-Based Artificial Intelligence Clinical Decision Support System for Emergency Department Interdepartmental Consultation Automation.","authors":"Boram Yoo, Chan Woong Kim, Jong-Hoon Oh","doi":"10.4258/hir.2026.32.3.224","DOIUrl":"10.4258/hir.2026.32.3.224","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop a custom GPT-based, knowledge-guided documentation support system that drafts emergency department (ED) interdepartmental consultation requests from minimal free-text input and evaluate its feasibility, usability, and workflow integration.</p><p><strong>Methods: </strong>The system generates a patient summary, recommended consulting department, structured consultation draft, and clinical reference notes from brief natural language input, including age, chief concern, and key findings. Two public AI Hub datasets, the Essential Medical Knowledge Dataset (Dataset No. 71875) and the Specialized Medical Knowledge Dataset (Dataset No. 71874), were used to guide prompt construction and constrain output structure rather than for model training. Department recommendation concordance was assessed using 22 de-identified ED consultation scenarios purposively selected across multiple specialties. The documented department was removed from the input and compared with the system's recommendation. A post-use survey of 10 emergency physicians and residents assessed workflow utility, clinical appropriateness, usability, reflection of relevant information, perceived patient safety, adoption intention, and editing requirements using 5-point Likert scales.</p><p><strong>Results: </strong>In the 22-case pilot set, the system's recommended department aligned with the historically documented department in all cases under controlled conditions. Given the small sample size and study design, this finding represents preliminary evidence of workflow alignment rather than definitive validation of clinical performance.</p><p><strong>Conclusions: </strong>The proposed system demonstrates the feasibility of large language modelenabled documentation support for generating standardized ED consultation requests, potentially reducing documentation burden. The system is intended to support, not replace, clinical decision-making, and clinician review remains essential. With further validation, this approach may be adaptable to other clinical referral contexts.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"224-231"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812936","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":"De-Identification of Magnetic Resonance Imaging to Protect Patient Privacy in Research Use: A Comprehensive Review.","authors":"Seonguk Kang, Junbeom Jang, Uk-Su Choi, Jeong-Ho Hong, Sang Won Park","doi":"10.4258/hir.2026.32.3.204","DOIUrl":"10.4258/hir.2026.32.3.204","url":null,"abstract":"<p><strong>Objectives: </strong>Brain magnetic resonance imaging (MRI) contains identifiable facial and cranial features, creating privacy risks that can limit secondary research use. This review examines current MRI de-identification technologies, quantitative validation methods, and governance frameworks to identify practical strategies for preserving data utility while protecting patient privacy.</p><p><strong>Methods: </strong>A descriptive narrative review was conducted across technical and policy domains. Studies of facial deidentification were analyzed according to the tools used, validation procedures, and downstream analytic performance. The reviewed approaches included traditional defacing, refacing, and deep-learning-based anonymization. Evaluation frameworks used the structural similarity index measure (SSIM), Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and the paired t-test to quantify both privacy preservation and analytic fidelity. A parallel policy analysis compared the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), Japan's Act on Anonymized Medical Information, Taiwan's Personal Data Protection Act, and South Korea's Personal Information Protection Act and 2024 Health Data Use Guidelines to assess policy convergence and institutional consistency.</p><p><strong>Results: </strong>Visual inspection studies reported that FreeSurfer preserved cortical anatomy but incompletely removed facial features, whereas FSL_deface overmasked some nonfacial regions. Artificial intelligence (AI)-based recognition tests achieved 28%-38% accuracy on defaced data, confirming measurable residual re-identification risk. Quantitative assessments identified segmentation degradation, including a DSC decrease from 0.970 to 0.918, and regional volumetric variability, including a hippocampal ICC of 0.742, with p < 0.05. Generative adversarial network-based refacing improved perceptual similarity, with SSIM values >0.7, but retained subtle facial geometry. The governance analysis indicated that HIPAA and GDPR provide established standards, whereas South Korea's Data Review Board oversight remains discretionary and nonuniform, limiting reproducibility across institutions.</p><p><strong>Conclusions: </strong>MRI de-identification requires integrated pipelines that combine AI-based facial masking and metadata cleansing with standardized evaluation metrics and enforceable review protocols.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"204-213"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812957","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 Operating System to Operating Society: The Next Mission of Medical Informatics.","authors":"Kye Hwa Lee, Seoyeon Baik","doi":"10.4258/hir.2026.32.3.201","DOIUrl":"10.4258/hir.2026.32.3.201","url":null,"abstract":"","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 3","pages":"201-203"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812926","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}