ArticleJournal of clinical and translational science2025
EHRchitect: An open-source software tool for medical event sequences data extraction from Electronic Health Records.
Article in Journal of clinical and translational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Vasopressor Use in Acute Burn Resuscitation: A Retrospective Study.Acta anaesthesiologica Scandinavica · 2026Article
- A machine learning approach to identify at-risk populations for yellow fever vaccination.Journal of clinical and translational science · 2026Article
- Erratum: EHRchitect: An open-source software tool for medical event sequences data extraction from Electronic Health Records - CORRIGENDUM.Journal of clinical and translational science · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Background: Electronic Health Records (EHR) analysis is pivotal in advancing medical research. Numerous real-world EHR data providers offer data access through exported datasets. While enabling profound research possibilities, exported EHR data requires quality control and restructuring for meaningful analysis. Challenges arise in medical events (e.g., diagnoses or procedures) sequence analysis, which provides critical insights into conditions, treatments, and outcomes progression. Identifying causal relationships, patterns, and trends requires a more complex approach to data mining and preparation. Methods: This paper introduces EHRchitect - an application written in Python that addresses the quality control challenges by automating dataset transformation, facilitating the creation of a clean, formatted, and optimized MySQL database (DB), and sequential data extraction according to the user's configuration. Results: The tool creates a clean, formatted, and optimized DB, enabling medical event sequence data extraction according to users' study configuration. Event sequences encompass patients' medical events in specified orders and time intervals. The extracted data are presented as distributed Parquet files, incorporating events, event transitions, patient metadata, and events metadata. The concurrent approach allows effortless scaling for multi-processor systems. Conclusion: EHRchitect streamlines the processing of large EHR datasets for research purposes. It facilitates extracting sequential event-based data, offering a highly flexible framework for configuring event and timeline parameters. The tool delivers temporal characteristics, patient demographics, and event metadata to support comprehensive analysis. The developed tool significantly reduces the time required for dataset acquisition and preparation by automating data quality control and simplifying event extraction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.