2026· BioNLP@ACL· pp. 992-996· 0 citations· 23 references
Computer Science
TL;DR
DBt_mimic_omop is a free, open-source resource that converts the MIMIC-IV dataset to the Observational Medical Outcomes Partner-ship (OMOP) common data model (CDM) format on consumer level hardware and generates a more comprehensive dataset than existing alternatives.
Abstract
dbt_mimic_omop is a free, open-source resource that converts the MIMIC-IV dataset to the Observational Medical Outcomes Partner-ship (OMOP) common data model (CDM) format on consumer level hardware. CDM approaches are increasingly adopted in both industry and academia due to the need for interoperability and reproducibility, including in clinical NLP tasks such as cohort selection, information extraction, and retrieval-augmented generation. The MIMIC-IV database is among the most widely used critical care research datasets, yet existing pipelines to transform it to OMOP depend on enterprise database infrastructure and complex orchestration, limiting accessibility for practitioners and resource-constrained researchers. We further integrate free-text clinical notes (195.6M clinical annotations) and chest radiographs into the OMOP note_nlp and imaging extension tables, making all MIMIC-IV modalities (structured data, free-text, and imaging) accessible through a common data model. This resource generates a more comprehensive dataset than existing alternatives and is intended to be used to aid in system development, testing, and evaluation.
Getting clinical data from different sources to “talk” to each other within the OMOP Common Data Model (CDM) is arguably the most tedious part of multi-center research. While this integration is essential, the transformation process is frequently a manual grind, requiring a rare overlap of deep clinical knowledge and technical expertise. In this paper, we present a framework designed to alleviate some of the burden on the researcher by automating data harmonization through two distinct steps: structural schema mapping and terminological standardization. For the structural piece, we moved away from “black box” logic in favor of a stateful workflow managed by large language models (LLMs) and directed acyclic graphs. By profiling EHR data at the source, our system generates context-aware dictionaries that offer ranked mapping suggestions alongside confidence scores. While our benchmarking showed a 97.5% agreement rate at the schema level and an 84% agreement rate at the value level when compared with human experts, the system appears most effective when treated as a “co-pilot” rather than a total replacement for human oversight. To handle value-level standardization, we implemented a hybrid search strategy that pairs the semantic depth of SapBERT embeddings with the literal precision of fuzzy string matching. By using FAISS for rapid similarity retrieval, the engine attempts to resolve messy or “noisy” clinical descriptions to standard OMOP concepts. This approach seems particularly promising for handling the non-standardized labels that often plague smaller, local datasets. Ultimately, our results suggest that this guided approach can shift the timeline for OHDSI-compliant warehousing from weeks of manual curation to a more manageable and scalable pipeline, potentially lowering the barrier to entry for smaller research teams.
Nishu Nehra, Rohit Swami, Dharani Dadi et al.· bioRxiv· 0 citations
This work introduces a scalable, resource-efficient, and high-performance information extraction pipeline that leverages large language models (LLMs) to address challenges of free-text clinical records and develops a multi-dimensional assessment for deployment in data extraction tasks.
A. Y. Ong, Quang Nguyen, I. Barai et al.· npj Digital Medicine· 1 citation
VITA's advantages in accuracy and completeness persisted under the neutral judge; its communication scores were lower, and this results indicate that a purpose-built clinical RAG system remains competitive with frontier LLMs on an open benchmark, consistent with corpus specificity as a design variable that improves grounding at some cost to communication polish.
Praveen Reddy, C. Mandke, Suvrankar Datta et al.· 0 citations
A novel benchmark comprising over 9,000 real-world, point-of-care, multilingual, and multimodal clinical question-answer pairs sourced from frontline health workers in Nigeria reveals several critical insights into the suitability of LLMs as clinical decision support systems in low-resource contexts.
Tobi Olatunji, C. Aka, C. Okocha et al.· medRxiv· 0 citations
CoMedBench is introduced, a reproducible benchmark that evaluates a family of generators under a common clinical-validity framework and one shared training and evaluation engine, spanning static tabular and temporal downstream tasks on established critical-care datasets.
Oncology notes contain the richest clinical detail, yet they remain largely inaccessible at scale because extracting structured phenotypes requires either substantial language model infrastructure, curated training data, or cloud computing under regulatory constraints. We developed OncoRAG, combining ontology enrichment, knowledge graph construction, graph-diffusion reranking, and structured prompting with a locally deployed 14B-parameter language model without model weight fine-tuning. Applied to three cohorts—triple-negative breast cancer (TNBC; 104 patients, 42 features; primary development), recurrent high-grade glioma (RiCi; 191 patients, 19 features; cross-lingual and cross-disease evaluation with cohort-specific configuration), and MIMIC-IV (100 patients, 10 features; limited external evaluation on overlapping features)—OncoRAG achieved F1 scores of 0.80, 0.79, and 0.84, improving over direct large language model (LLM) prompting and naive retrieval-augmented generation (RAG) baselines by 0.19–0.22 and 0.17–0.19 F1, and outperforming direct prompting with a 5× larger 70B model by 0.09–0.10 F1. In an exploratory survival analysis (12 events), both feature sets showed close point estimates of the C-index (0.77 vs 0.76), but equivalence cannot be statistically confirmed given the limited event count. OncoRAG enables accurate clinical phenotyping from multilingual oncology notes using a locally deployable mid-size model, without model weight fine-tuning or external data sharing.
P. Salome, Maximilian Knoll, David Walz et al.· npj Digital Medicine· 0 citations
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