To the Editor, We thank Yu and colleagues for their thoughtful and constructive comments on our article, Breaking data silos: incorporating the DICOM imaging standard into the OMOP CDM to enable multimodal research.1 We appreciate their careful reading and insightful reflections, which underscore both the strengths of our approach and important directions for future research. We share their goal of advancing semantic interoperability between imaging and clinical data to support reproducible multimodal studies. We agree that additional detail regarding metadata filtering will aid reproducibility. As described in the Methods, we extracted the full metadata header from one representative image in each series, where each series corresponds to a unique sequence within a study. No de-duplication was performed, as each series-level instance is globally unique. We then applied sequential filtering steps summarized in Table 1. Private (manufacturer-specific) tags were excluded because our DICOM concepts were derived from standard Attributes defined in DICOM Part 6.2 These could be incorporated in future work by referencing vendors’ DICOM conformance statements or implementation manuals. A practical consideration for excluding private tags is that OMOP CDM is frequently used in federated, multi-site networks where vendor-specific fields may not generalize across institutions.

OBJECTIVE: To develop an ontology-driven framework that standardizes heterogeneous local radiology procedure names by decomposing them into semantic components and aligning them to LOINC/RSNA Radiology Playbook codes using constrained large language model (LLM)-based parsing and selection. METHODS: Radiology procedure names from two tertiary hospitals in Korea were parsed into semantic components using LLM prompting with retrieval-augmented generation, aligned with the LOINC/RSNA Radiology Playbook (version 2.80). Ontology-based similarity scoring quantify correspondence between parsed components and Playbook candidates' attributes, and retrieve the Top 10 candidates, followed by LLM-based selection within this candidate set. Performance was evaluated against direct Playbook code name-based mapping and conventional similarity metrics using a radiologist-curated gold reference. RESULTS: A total of 3,326 local procedure names were analyzed. Ontology-based mapping approach substantially outperformed direct Playbook code name mapping across all evaluation metrics. At the candidate retrieval stage, ontology-based attribute matching achieved recall@5 of up to 0.78 (internal) and 0.89 (external), compared with 0.51 and 0.47 for direct mapping. After LLM-based selection, the ontology-based approach achieved a final selection recall@1 of up to 0.70 (internal) and 0.81 (external), exceeding direct mapping (0.48 and 0.50) more than 20 percentage points in both settings (p < 0.001). CONCLUSION: Decomposing procedure names into ontology-grounded semantic components enables robust handling of heterogeneous local terminology, while constraining LLM reasoning to structured selection tasks mitigates hallucination and preserves semantic fidelity. Ontology-driven knowledge encoding provides a scalable and reliable approach to standardizing radiology procedure names, supporting cross-institutional interoperability and secondary data use of imaging research.

OBJECTIVES: This study develops and validates the confidence-linked and uncertainty-based staged (CLUES) framework by integrating large language models (LLMs) with uncertainty quantification to assist manual chart review while ensuring reliability through a selective human review. MATERIALS AND METHODS: The CLUES framework assesses stroke-related hospitalizations using imaging reports for 1739 patients across 24 Korean hospitals (2011-2022). Uncertainty was quantified via entropy from LLM-derived confidence values. Our framework operated in 3 stages: (1) zero-shot prompting with ensemble averaging, where high-uncertainty cases advanced to stage 2, (2) few-shot prompting using retrieved low-uncertainty cases, with remaining high-uncertainty cases proceeding to stage 3, and (3) manual chart review for final uncertain cases. Performance was evaluated against physician-labeled data using F1-score and Cohen's Kappa. RESULTS: Among 1072 test cases, stage 1 classified 507 cases as low uncertainty, while 565 were high uncertainty. Stage 2 reclassified 280 cases as low uncertainty, leaving 285 for manual review. Low-uncertainty cases consistently outperformed high-uncertainty cases in both stages (weighted F1-scores: 0.94 vs 0.57 in stage 1 and 0.82 vs 0.58 in stage 2). The overall framework performance showed a progressive improvement in F1-scores from 0.840 (stage 1) to 0.878 (stage 2) to 0.955 (stage 3). DISCUSSION: The CLUES framework reduced manual review burden by 75% while maintaining high accuracy. By integrating uncertainty quantification with selective human oversight, it provides an efficient and reliable approach to phenotype validation. CONCLUSION: This framework demonstrates the effective integration of LLMs into clinical workflows while ensuring human oversight, enhancing both accuracy and efficiency.

OBJECTIVE: Cause of death is used as an important outcome of clinical research; however, access to cause-of-death data is limited. This study aimed to develop and validate a machine-learning model that predicts the cause of death from the patient's last medical checkup. MATERIALS AND METHODS: To classify the mortality status and each individual cause of death, we used a stacking ensemble method. The prediction outcomes were all-cause mortality, 8 leading causes of death in South Korea, and other causes. The clinical data of study populations were extracted from the national claims (n = 174 747) and electronic health records (n = 729 065) and were used for model development and external validation. Moreover, we imputed the cause of death from the data of 3 US claims databases (n = 994 518, 995 372, and 407 604, respectively). All databases were formatted to the Observational Medical Outcomes Partnership Common Data Model. RESULTS: The generalized area under the receiver operating characteristic curve (AUROC) of the model predicting the cause of death within 60 days was 0.9511. Moreover, the AUROC of the external validation was 0.8887. Among the causes of death imputed in the Medicare Supplemental database, 11.32% of deaths were due to malignant neoplastic disease. DISCUSSION: This study showed the potential of machine-learning models as a new alternative to address the lack of access to cause-of-death data. All processes were disclosed to maintain transparency, and the model was easily applicable to other institutions. CONCLUSION: A machine-learning model with competent performance was developed to predict cause of death.

Journal of the American Medical Informatics Association, 27(8), 2020, 1268–1277; doi: 10.1093/jamia/ocaa124 Upon the original publication of this article, several author corrections to the reference section were inadvertently left out, making literature referencing in the article inaccurate. This error has now been corrected online. The publisher apologises for the error.

OBJECTIVES: To demonstrate the application of the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) principles described in our companion article to hypertension treatments and assess internal and external validity of the generated evidence. MATERIALS AND METHODS: LEGEND defines a process for high-quality observational research based on 10 guiding principles. We demonstrate how this process, here implemented through large-scale propensity score modeling, negative and positive control questions, empirical calibration, and full transparency, can be applied to compare antihypertensive drug therapies. We assess internal validity through covariate balance, confidence-interval coverage, between-database heterogeneity, and transitivity of results. We assess external validity through comparison to direct meta-analyses of randomized controlled trials (RCTs). RESULTS: From 21.6 million unique antihypertensive new users, we generate 6 076 775 effect size estimates for 699 872 research questions on 12 946 treatment comparisons. Through propensity score matching, we achieve balance on all baseline patient characteristics for 75% of estimates, observe 95.7% coverage in our effect-estimate 95% confidence intervals, find high between-database consistency, and achieve transitivity in 84.8% of triplet hypotheses. Compared with meta-analyses of RCTs, our results are consistent with 28 of 30 comparisons while providing narrower confidence intervals. CONCLUSION: We find that these LEGEND results show high internal validity and are congruent with meta-analyses of RCTs. For these reasons we believe that evidence generated by LEGEND is of high quality and can inform medical decision-making where evidence is currently lacking. Subsequent publications will explore the clinical interpretations of this evidence.

Evidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due to residual confounding. Other concerns include P hacking and publication bias. In response, the Observational Health Data Sciences and Informatics international collaborative launched the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative. Its mission is to generate evidence on the effects of medical interventions using observational health-care databases while addressing the aforementioned concerns by following a recently proposed paradigm. We define 10 principles of LEGEND that enshrine this new paradigm, prescribing the generation and dissemination of evidence on many research questions at once; for example, comparing all treatments for a disease for many outcomes, thus preventing publication bias. These questions are answered using a prespecified and systematic approach, avoiding P hacking. Best-practice statistical methods address measured confounding, and control questions (research questions where the answer is known) quantify potential residual bias. Finally, the evidence is generated in a network of databases to assess consistency by sharing open-source analytics code to enhance transparency and reproducibility, but without sharing patient-level information. Here we detail the LEGEND principles and provide a generic overview of a LEGEND study. Our companion paper highlights an example study on the effects of hypertension treatments, and evaluates the internal and external validity of the evidence we generate.

OBJECTIVE: Birth month and climate impact lifetime disease risk, while the underlying exposures remain largely elusive. We seek to uncover distal risk factors underlying these relationships by probing the relationship between global exposure variance and disease risk variance by birth season. MATERIAL AND METHODS: This study utilizes electronic health record data from 6 sites representing 10.5 million individuals in 3 countries (United States, South Korea, and Taiwan). We obtained birth month-disease risk curves from each site in a case-control manner. Next, we correlated each birth month-disease risk curve with each exposure. A meta-analysis was then performed of correlations across sites. This allowed us to identify the most significant birth month-exposure relationships supported by all 6 sites while adjusting for multiplicity. We also successfully distinguish relative age effects (a cultural effect) from environmental exposures. RESULTS: Attention deficit hyperactivity disorder was the only identified relative age association. Our methods identified several culprit exposures that correspond well with the literature in the field. These include a link between first-trimester exposure to carbon monoxide and increased risk of depressive disorder (R = 0.725, confidence interval [95% CI], 0.529-0.847), first-trimester exposure to fine air particulates and increased risk of atrial fibrillation (R = 0.564, 95% CI, 0.363-0.715), and decreased exposure to sunlight during the third trimester and increased risk of type 2 diabetes mellitus (R = -0.816, 95% CI, -0.5767, -0.929). CONCLUSION: A global study of birth month-disease relationships reveals distal risk factors involved in causal biological pathways that underlie them.