Journal of the American Medical Informatics Association最新文献

{"title":"Use of calibration to improve the precision of estimates obtained from All of Us data.","authors":"Vivian Hsing-Chun Wang, Julie Holm, José A Pagán","doi":"10.1093/jamia/ocae181","DOIUrl":"https://doi.org/10.1093/jamia/ocae181","url":null,"abstract":"<p><strong>Objectives: </strong>To highlight the use of calibration weighting to improve the precision of estimates obtained from All of Us data and increase the return of value to communities from the All of Us Research Program.</p><p><strong>Materials and methods: </strong>We used All of Us (2017-2022) data and raking to obtain prevalence estimates in two examples: discrimination in medical settings (N = 41 875) and food insecurity (N = 82 266). Weights were constructed using known population proportions (age, sex, race/ethnicity, region of residence, annual household income, and home ownership) from the 2020 National Health Interview Survey.</p><p><strong>Results: </strong>About 37% of adults experienced discrimination in a medical setting. About 20% of adults who had not seen a doctor reported being food insecure compared with 14% of adults who regularly saw a doctor.</p><p><strong>Conclusions: </strong>Calibration using raking is cost-effective and may lead to more precise estimates when analyzing All of Us data.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564953","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":"Fair prediction of 2-year stroke risk in patients with atrial fibrillation.","authors":"Jifan Gao, Philip Mar, Zheng-Zheng Tang, Guanhua Chen","doi":"10.1093/jamia/ocae170","DOIUrl":"https://doi.org/10.1093/jamia/ocae170","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups.</p><p><strong>Materials and methods: </strong>Our study utilized structured electronic health records (EHR) data from the All of Us Research Program. Machine learning models (LightGBM) were utilized to capture the relations between stroke risks and the predictors used by the widely recognized CHADS2 and CHA2DS2-VASc scores. We mitigated the racial disparity by creating a representative tuning set, customizing tuning criteria, and setting binary thresholds separately for subgroups. We constructed a hold-out test set that not only supports temporal validation but also includes a larger proportion of Black/African Americans for fairness validation.</p><p><strong>Results: </strong>Compared to the original CHADS2 and CHA2DS2-VASc scores, significant improvements were achieved by modeling their predictors using machine learning models (Area Under the Receiver Operating Characteristic curve from near 0.70 to above 0.80). Furthermore, applying our disparity mitigation strategies can effectively enhance model fairness compared to the conventional cross-validation approach.</p><p><strong>Discussion: </strong>Modeling CHADS2 and CHA2DS2-VASc risk factors with LightGBM and our disparity mitigation strategies achieved decent discriminative performance and excellent fairness performance. In addition, this approach can provide a complete interpretation of each predictor. These highlight its potential utility in clinical practice.</p><p><strong>Conclusions: </strong>Our research presents a practical example of addressing clinical challenges through the All of Us Research Program data. The disparity mitigation framework we proposed is adaptable across various models and data modalities, demonstrating broad potential in clinical informatics.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499494","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":"Reducing diagnostic delays in acute hepatic porphyria using health records data and machine learning.","authors":"Balu Bhasuran, Katharina Schmolly, Yuvraaj Kapoor, Nanditha Lakshmi Jayakumar, Raymond Doan, Jigar Amin, Stephen Meninger, Nathan Cheng, Robert Deering, Karl Anderson, Simon W Beaven, Bruce Wang, Vivek A Rudrapatna","doi":"10.1093/jamia/ocae141","DOIUrl":"10.1093/jamia/ocae141","url":null,"abstract":"<p><strong>Background: </strong>Acute hepatic porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of 15 years on average. The advent of electronic health records (EHR) data and machine learning (ML) may improve the timely recognition of rare diseases like AHP. However, prediction models can be difficult to train given the limited case numbers, unstructured EHR data, and selection biases intrinsic to healthcare delivery. We sought to train and characterize models for identifying patients with AHP.</p><p><strong>Methods: </strong>This diagnostic study used structured and notes-based EHR data from 2 centers at the University of California, UCSF (2012-2022) and UCLA (2019-2022). The data were split into 2 cohorts (referral and diagnosis) and used to develop models that predict (1) who will be referred for testing of acute porphyria, among those who presented with abdominal pain (a cardinal symptom of AHP), and (2) who will test positive, among those referred. The referral cohort consisted of 747 patients referred for testing and 99 849 contemporaneous patients who were not. The diagnosis cohort consisted of 72 confirmed AHP cases and 347 patients who tested negative. The case cohort was 81% female and 6-75 years old at the time of diagnosis. Candidate models used a range of architectures. Feature selection was semi-automated and incorporated publicly available data from knowledge graphs. Our primary outcome was the F-score on an outcome-stratified test set.</p><p><strong>Results: </strong>The best center-specific referral models achieved an F-score of 86%-91%. The best diagnosis model achieved an F-score of 92%. To further test our model, we contacted 372 current patients who lack an AHP diagnosis but were predicted by our models as potentially having it (≥10% probability of referral, ≥50% of testing positive). However, we were only able to recruit 10 of these patients for biochemical testing, all of whom were negative. Nonetheless, post hoc evaluations suggested that these models could identify 71% of cases earlier than their diagnosis date, saving 1.2 years.</p><p><strong>Conclusions: </strong>ML can reduce diagnostic delays in AHP and other rare diseases. Robust recruitment strategies and multicenter coordination will be needed to validate these models before they can be deployed.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472084","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":"All of whom? Limitations encountered using All of Us Researcher Workbench in a Primary Care residents secondary data analysis research training block.","authors":"Fred Willie Zametkin LaPolla, Marco Barber Grossi, Sharon Chen, Tai Wei Guo, Kathryn Havranek, Olivia Jebb, Minh Thu Nguyen, Sneha Panganamamula, Noah Smith, Shree Sundaresh, Jonathan Yu, Gabrielle Mayer","doi":"10.1093/jamia/ocae162","DOIUrl":"https://doi.org/10.1093/jamia/ocae162","url":null,"abstract":"<p><strong>Objectives: </strong>The goal of this case report is to detail experiences and challenges experienced in the training of Primary Care residents in secondary analysis using All of Us Researcher Workbench. At our large, urban safety net hospital, Primary Care/Internal Medicine residents in their third year undergo a research intensive block, the Research Practicum, where they work as a team to conduct secondary data analysis on a dataset with faculty facilitation. In 2023, this research block focused on use of the All of Us Researcher Workbench for secondary data analysis.</p><p><strong>Materials and methods: </strong>Two groups of 5 residents underwent training to access the All of Us Researcher Workbench, and each group explored available data with a faculty facilitator and generated original research questions. Two blocks of residents successfully completed their research blocks and created original presentations on \"social isolation and A1C\" levels and \"medical discrimination and diabetes management.\"</p><p><strong>Results: </strong>Departmental faculty were satisfied with the depth of learning and data exploration. In focus groups, some residents noted that for those without interest in performing research, the activity felt extraneous to their career goals, while others were glad for the opportunity to publish. In both blocks, residents highlighted dissatisfaction with the degree to which the All of Us Researcher Workbench was representative of patients they encounter in a large safety net hospital.</p><p><strong>Discussion: </strong>Using the All of Us Researcher Workbench provided residents with an opportunity to explore novel questions in a massive data source. Many residents however noted that because the population described in the All of Us Researcher Workbench appeared to be more highly educated and less racially diverse than patients they encounter in their practice, research may be hard to generalize in a community health context. Additionally, given that the data required knowledge of 1 of 2 code-based data analysis languages (R or Python) and work within an idiosyncratic coding environment, residents were heavily reliant on a faculty facilitator to assist with analysis.</p><p><strong>Conclusion: </strong>Using the All of Us Researcher Workbench for research training allowed residents to explore novel questions and gain first-hand exposure to opportunities and challenges in secondary data analysis.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452050","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":"Disparities in ABO blood type determination across diverse ancestries: a systematic review and validation in the All of Us Research Program.","authors":"Kiana L Martinez, Andrew Klein, Jennifer R Martin, Chinwuwanuju U Sampson, Jason B Giles, Madison L Beck, Krupa Bhakta, Gino Quatraro, Juvie Farol, Jason H Karnes","doi":"10.1093/jamia/ocae161","DOIUrl":"https://doi.org/10.1093/jamia/ocae161","url":null,"abstract":"<p><strong>Objectives: </strong>ABO blood types have widespread clinical use and robust associations with disease. The purpose of this study is to evaluate the portability and suitability of tag single-nucleotide polymorphisms (tSNPs) used to determine ABO alleles and blood types across diverse populations in published literature.</p><p><strong>Materials and methods: </strong>Bibliographic databases were searched for studies using tSNPs to determine ABO alleles. We calculated linkage between tSNPs and functional variants across inferred continental ancestry groups from 1000 Genomes. We compared r2 across ancestry and assessed real-world consequences by comparing tSNP-derived blood types to serology in a diverse population from the All of Us Research Program.</p><p><strong>Results: </strong>Linkage between functional variants and O allele tSNPs was significantly lower in African (median r2 = 0.443) compared to East Asian (r2 = 0.946, P = 1.1 × 10-5) and European (r2 = 0.869, P = .023) populations. In All of Us, discordance between tSNP-derived blood types and serology was high across all SNPs in African ancestry individuals and linkage was strongly correlated with discordance across all ancestries (ρ = -0.90, P = 3.08 × 10-23).</p><p><strong>Discussion: </strong>Many studies determine ABO blood types using tSNPs. However, tSNPs with low linkage disequilibrium promote misinference of ABO blood types, particularly in diverse populations. We observe common use of inappropriate tSNPs to determine ABO blood type, particularly for O alleles and with some tSNPs mistyping up to 58% of individuals.</p><p><strong>Conclusion: </strong>Our results highlight the lack of transferability of tSNPs across ancestries and potential exacerbation of disparities in genomic research for underrepresented populations. This is especially relevant as more diverse cohorts are made publicly available.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452052","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":"Deep learning with noisy labels in medical prediction problems: a scoping review.","authors":"Yishu Wei, Yu Deng, Cong Sun, Mingquan Lin, Hongmei Jiang, Yifan Peng","doi":"10.1093/jamia/ocae108","DOIUrl":"10.1093/jamia/ocae108","url":null,"abstract":"<p><strong>Objectives: </strong>Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included.</p><p><strong>Methods: </strong>Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include \"noisy label AND medical/healthcare/clinical,\" \"uncertainty AND medical/healthcare/clinical,\" and \"noise AND medical/healthcare/clinical.\"</p><p><strong>Results: </strong>A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided.</p><p><strong>Discussion: </strong>From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141174561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pneumonia diagnosis performance in the emergency department: a mixed-methods study about clinicians' experiences and exploration of individual differences and response to diagnostic performance feedback.","authors":"Jorie M Butler, Teresa Taft, Peter Taber, Elizabeth Rutter, Megan Fix, Alden Baker, Charlene Weir, McKenna Nevers, David Classen, Karen Cosby, Makoto Jones, Alec Chapman, Barbara E Jones","doi":"10.1093/jamia/ocae112","DOIUrl":"10.1093/jamia/ocae112","url":null,"abstract":"<p><strong>Objectives: </strong>We sought to (1) characterize the process of diagnosing pneumonia in an emergency department (ED) and (2) examine clinician reactions to a clinician-facing diagnostic discordance feedback tool.</p><p><strong>Materials and methods: </strong>We designed a diagnostic feedback tool, using electronic health record data from ED clinicians' patients to establish concordance or discordance between ED diagnosis, radiology reports, and hospital discharge diagnosis for pneumonia. We conducted semistructured interviews with 11 ED clinicians about pneumonia diagnosis and reactions to the feedback tool. We administered surveys measuring individual differences in mindset beliefs, comfort with feedback, and feedback tool usability. We qualitatively analyzed interview transcripts and descriptively analyzed survey data.</p><p><strong>Results: </strong>Thematic results revealed: (1) the diagnostic process for pneumonia in the ED is characterized by diagnostic uncertainty and may be secondary to goals to treat and dispose the patient; (2) clinician diagnostic self-evaluation is a fragmented, inconsistent process of case review and follow-up that a feedback tool could fill; (3) the feedback tool was described favorably, with task and normative feedback harnessing clinician values of high-quality patient care and personal excellence; and (4) strong reactions to diagnostic feedback varied from implicit trust to profound skepticism about the validity of the concordance metric. Survey results suggested a relationship between clinicians' individual differences in learning and failure beliefs, feedback experience, and usability ratings.</p><p><strong>Discussion and conclusion: </strong>Clinicians value feedback on pneumonia diagnoses. Our results highlight the importance of feedback about diagnostic performance and suggest directions for considering individual differences in feedback tool design and implementation.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.","authors":"Sharon Jiang, Barbara D Lam, Monica Agrawal, Shannon Shen, Nicholas Kurtzman, Steven Horng, David R Karger, David Sontag","doi":"10.1093/jamia/ocae092","DOIUrl":"10.1093/jamia/ocae092","url":null,"abstract":"<p><strong>Objective: </strong>Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.</p><p><strong>Materials and methods: </strong>We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment.</p><p><strong>Results: </strong>The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user.</p><p><strong>Discussion: </strong>Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings.</p><p><strong>Conclusion: </strong>EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140872278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility.","authors":"Thomas R Campion, Catherine K Craven, David A Dorr, Elmer V Bernstam, Boyd M Knosp","doi":"10.1093/jamia/ocae111","DOIUrl":"10.1093/jamia/ocae111","url":null,"abstract":"<p><strong>Objectives: </strong>Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility.</p><p><strong>Materials and methods: </strong>We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts.</p><p><strong>Results: </strong>EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science.</p><p><strong>Discussion: </strong>As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied.</p><p><strong>Conclusion: </strong>This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141082136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a unified understanding of drug-drug interactions: mapping Japanese drug codes to RxNorm concepts.","authors":"Yukinobu Kawakami, Takuya Matsuda, Noriaki Hidaka, Mamoru Tanaka, Eizen Kimura","doi":"10.1093/jamia/ocae094","DOIUrl":"10.1093/jamia/ocae094","url":null,"abstract":"<p><strong>Objectives: </strong>Linking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products that use international controlled vocabularies remains limited. This study mapped YJ codes to RxNorm ingredient classes, providing new insights by comparing Japanese and international drug-drug interaction (DDI) information using a case study methodology.</p><p><strong>Materials and methods: </strong>Tables linking YJ codes to RxNorm concepts were created using the application programming interfaces of the Kyoto Encyclopedia of Genes and Genomes and the National Library of Medicine. A comparative analysis of Japanese and international DDI information was thus performed by linking to an international DDI KB.</p><p><strong>Results: </strong>There was limited agreement between the Japanese and international DDI severity classifications. Cross-tabulation of Japanese and international DDIs by severity showed that 213 combinations classified as serious DDIs by an international KB were missing from the Japanese DDI information.</p><p><strong>Discussion: </strong>It is desirable that efforts be undertaken to standardize international criteria for DDIs to ensure consistency in the classification of their severity.</p><p><strong>Conclusion: </strong>The classification of DDI severity remains highly variable. It is imperative to augment the repository of critical DDI information, which would revalidate the utility of fostering collaborations with global KBs.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140960525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}