ArticlePloS one2017
ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Fine-Tuning Large Language Models for Effective Nutrition Support in Residential Aged Care: A Domain Expertise Approach.Healthcare (Basel, Switzerland) · 2025Article
- Automating the extraction of otology symptoms from clinic letters: a methodological study using natural language processing.BMC medical informatics and decision making · 2025Article
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- Knowledge discovery of diseases symptoms and rehabilitation measures in Q&A communities.Scientific reports · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- pyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical records.JAMIA open · 2025Article
- Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.The journal of prevention of Alzheimer's disease · 2025Article
- Development of a text mining algorithm for identifying adverse drug reactions in electronic health records.JAMIA open · 2024Article
- Article
- A risk identification model for detection of patients at risk of antidepressant discontinuation.Frontiers in artificial intelligence · 2023Article
- Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT.International journal of environmental research and public health · 2022Article
- Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership.JMIR formative research · 2022Article
- Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes.JCO clinical cancer informatics · 2022Article
- Identifying adverse drug reactions from free-text electronic hospital health record notes.British journal of clinical pharmacology · 2022Article
- Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach.JMIR medical informatics · 2021Article
- Clozapine-induced myocarditis: electronic health register analysis of incidence, timing, clinical markers and diagnostic accuracy.The British journal of psychiatry : the journal of mental science · 2021Article
- Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.Patterns (New York, N.Y.) · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient's quality of life and present a substantial burden on healthcare. Although Electronic health records (EHRs) document a wealth of information relating to ADEs, they are frequently stored in the unstructured or semi-structured free-text narrative requiring Natural Language Processing (NLP) techniques to mine the relevant information. Here we present a rule-based ADE detection and classification pipeline built and tested on a large Psychiatric corpus comprising 264k patients using the de-identified EHRs of four UK-based psychiatric hospitals. The pipeline uses characteristics specific to Psychiatric EHRs to guide the annotation process, and distinguishes: a) the temporal value associated with the ADE mention (whether it is historical or present), b) the categorical value of the ADE (whether it is assertive, hypothetical, retrospective or a general discussion) and c) the implicit contextual value where the status of the ADE is deduced from surrounding indicators, rather than explicitly stated. We manually created the rulebase in collaboration with clinicians and pharmacists by studying ADE mentions in various types of clinical notes. We evaluated the open-source Adverse Drug Event annotation Pipeline (ADEPt) using 19 ADEs specific to antipsychotics and antidepressants medication. The ADEs chosen vary in severity, regularity and persistence. The average F-measure and accuracy achieved by our tool across all tested ADEs were 0.83 and 0.83 respectively. In addition to annotation power, the ADEPT pipeline presents an improvement to the state of the art context-discerning algorithm, ConText.
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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.