ArticleDigestive diseases and sciences2017
A Text Searching Tool to Identify Patients with Idiosyncratic Drug-Induced Liver Injury.
Article in Digestive diseases and sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- Use of Epic Electronic Health Record System for Health Care Research: Scoping Review.Journal of medical Internet research · 2023Article
- Active Pharmacovigilance of Drug-Induced Liver Injury Using Electronic Health Records.Allergy, asthma & immunology research · 2020Article
- Utility of a Computerized ICD-10 Algorithm to Identify Idiosyncratic Drug-Induced Liver Injury Cases in the Electronic Medical Record.Drug safety · 2020Article
- Novel methodology to measure pre-procedure antimicrobial prophylaxis: integrating text searches with structured data from the Veterans Health Administration's electronic medical record.BMC medical informatics and decision making · 2020Article
- An electronic medical records-based approach to identify idiosyncratic drug-induced liver injury in children.Scientific reports · 2019Article
- Roussel Uclaf Causality Assessment Method for Drug-Induced Liver Injury: Present and Future.Frontiers in pharmacology · 2019Review
- Identifying Drug-Induced Liver Illness (DILI) with Computerized Information Extraction: No More Dilly-Dallying.Digestive diseases and sciences · 2017Article
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3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIdiosyncratic drug-induced liver injury (DILI) is an uncommon but important cause of liver disease that is challenging to diagnose and identify in the electronic medical record (EMR).
aimTo develop an accurate, reliable, and efficient method of identifying patients with bonafide DILI in an EMR system.
methodsIn total, 527,000 outpatient and ER encounters in an EPIC-based EMR were searched for potential DILI cases attributed to eight drugs. A searching algorithm that extracted 200 characters of text around 14 liver injury terms in the EMR were extracted and collated. Physician investigators reviewed the data outputs and used standardized causality assessment methods to adjudicate the potential DILI cases.
resultsA total of 101 DILI cases were identified from the 2564 potential DILI cases that included 62 probable DILI cases, 25 possible DILI cases, nine historical DILI cases, and five allergy-only cases. Elimination of the term "liver disease" from the search strategy improved the search recall from 4 to 19 %, while inclusion of the four highest yield liver injury terms further improved the positive predictive value to 64 % but reduced the overall case detection rate by 47 %. RUCAM scores of the 57 probable DILI cases were generally high and concordant with expert opinion causality assessment scores.
conclusionsA novel text searching tool was developed that identified a large number of DILI cases from a widely used EMR system. A computerized extraction of dictated text followed by the manual review of text snippets can rapidly identify bona fide cases of idiosyncratic DILI.
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