ArticleBiomedical informatics insights2017
Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.
Article in Biomedical informatics insights, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- 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
- LEVERAGING UNSTRUCTURED DATA IN ELECTRONIC HEALTH RECORDS TO DETECT ADVERSE EVENTS FROM PEDIATRIC DRUG USE - A SCOPING REVIEW.medRxiv : the preprint server for health sciences · 2025Article
- Discrepancies in safety reporting for chronic back pain clinical trials: an observational study from ClinicalTrials.gov and publications.BMC medical research methodology · 2025Observational
- Extraction of sleep information from clinical notes of Alzheimer's disease patients using natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Hypertensive events after the initiation of contemporary cancer therapies for breast cancer control.Cancer medicine · 2023Article
- Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes.JCO clinical cancer informatics · 2022Article
- Automated detection of substance use information from electronic health records for a pediatric population.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Finding warning markers: Leveraging natural language processing and machine learning technologies to detect risk of school violence.International journal of medical informatics · 2020Article
- Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Integrating Electronic Health Record Data into the ADEpedia-on-OHDSI Platform for Improved Signal Detection: A Case Study of Immune-related Adverse Events.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Identifying Symptom Information in Clinical Notes Using Natural Language Processing.Nursing researchArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
The objective of this study was to determine whether the Food and Drug Administration's Adverse Event Reporting System (FAERS) data set could serve as the basis of automated electronic health record (EHR) monitoring for the adverse drug reaction (ADR) subset of adverse drug events. We retrospectively collected EHR entries for 71 909 pediatric inpatient visits at Cincinnati Children's Hospital Medical Center. Natural language processing (NLP) techniques were used to identify positive diseases/disorders and signs/symptoms (DDSSs) from the patients' clinical narratives. We downloaded all FAERS reports submitted by medical providers and extracted the reported drug-DDSS pairs. For each patient, we aligned the drug-DDSS pairs extracted from their clinical notes with the corresponding drug-DDSS pairs from the FAERS data set to identify Drug-Reaction Pair Sentences (DRPSs). The DRPSs were processed by NLP techniques to identify ADR-related DRPSs. We used clinician annotated, real-world EHR data as reference standard to evaluate the proposed algorithm. During evaluation, the algorithm achieved promising performance and showed great potential in identifying ADRs accurately for pediatric patients.
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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.