ArticleJournal of biomedical informatics2010
Selecting information in electronic health records for knowledge acquisition.
Article in Journal of biomedical informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current approaches to identify sections within clinical narratives from electronic health records: a systematic review.BMC medical research methodology · 2019Pooled it
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Cohort Profile: The Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment Protocol (RIGHT Protocol).International journal of epidemiology · 2020Article
- Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions.Applied clinical informatics · 2018Article
- Detecting Pharmacovigilance Signals Combining Electronic Medical Records With Spontaneous Reports: A Case Study of Conventional Disease-Modifying Antirheumatic Drugs for Rheumatoid Arthritis.Frontiers in pharmacology · 2018Article
- Review
- Discovering adverse drug events combining spontaneous reports with electronic medical records: a case study of conventional DMARDs and biologics for rheumatoid arthritis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records.PloS one · 2017Article
- Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.Biomedical informatics insights · 2017Article
- Evidence in Practice - A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand.Frontiers in veterinary science · 2016Article
- Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register.PloS one · 2015Article
- Unobtrusive sensing and wearable devices for health informatics.IEEE transactions on bio-medical engineering · 2014Review
- Approaches to canine health surveillance.Canine genetics and epidemiology · 2014Review
- Lessons learned from developing a drug evidence base to support pharmacovigilance.Applied clinical informatics · 2013Article
- Mining the pharmacogenomics literature--a survey of the state of the art.Briefings in bioinformatics · 2012Article
- Using text-mining techniques in electronic patient records to identify ADRs from medicine use.British journal of clinical pharmacology · 2012Review
- Building an automated SOAP classifier for emergency department reports.Journal of biomedical informatics · 2012Article
- A context-blocks model for identifying clinical relationships in patient records.BMC bioinformatics · 2011Article
- A textual representation scheme for identifying clinical relationships in patient records.Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications · 2011Article
- Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Knowledge acquisition of relations between biomedical entities is critical for many automated biomedical applications, including pharmacovigilance and decision support. Automated acquisition of statistical associations from biomedical and clinical documents has shown some promise. However, acquisition of clinically meaningful relations (i.e. specific associations) remains challenging because textual information is noisy and co-occurrence does not typically determine specific relations. In this work, we focus on acquisition of two types of relations from clinical reports: disease-manifestation related symptom (MRS) and drug-adverse drug event (ADE), and explore the use of filtering by sections of the reports to improve performance. Evaluation indicated that applying the filters improved recall (disease-MRS: from 0.85 to 0.90; drug-ADE: from 0.43 to 0.75) and precision (disease-MRS: from 0.82 to 0.92; drug-ADE: from 0.16 to 0.31). This preliminary study demonstrates that selecting information in narrative electronic reports based on the sections improves the detection of disease-MRS and drug-ADE types of relations. Further investigation of complementary methods, such as more sophisticated statistical methods, more complex temporal models and use of information from other knowledge sources, is needed.
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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.