Dataset Information

2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.

ABSTRACT: OBJECTIVE:This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evaluated 3 tasks: concept extraction, relation classification, and end-to-end systems. We perform an analysis of the results to identify the state of the art in these tasks, learn from it, and build on it. MATERIALS AND METHODS:For all tasks, teams were given raw text of narrative discharge summaries, and in all the tasks, participants proposed deep learning-based methods with hand-designed features. In the concept extraction task, participants used sequence labelling models (bidirectional long short-term memory being the most popular), whereas in the relation classification task, they also experimented with instance-based classifiers (namely support vector machines and rules). Ensemble methods were also popular. RESULTS:A total of 28 teams participated in task 1, with 21 teams in tasks 2 and 3. The best performing systems set a high performance bar with F1 scores of 0.9418 for concept extraction, 0.9630 for relation classification, and 0.8905 for end-to-end. However, the results were much lower for concepts and relations of Reasons and ADEs. These were often missed because local context is insufficient to identify them. CONCLUSIONS:This challenge shows that clinical concept extraction and relation classification systems have a high performance for many concept types, but significant improvement is still required for ADEs and Reasons. Incorporating the larger context or outside knowledge will likely improve the performance of future systems.

SUBMITTER: Henry S

PROVIDER: S-EPMC7489085 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.

Henry Sam S Buchan Kevin K Filannino Michele M Stubbs Amber A Uzuner Ozlem O

Journal of the American Medical Informatics Association : JAMIA 20200101 1

<h4>Objective</h4>This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evaluated 3 tasks: concept extraction, relation classification, and end-to-end systems. We perform an analysis of the results to identify the state of the art in these tasks, learn from it, and build on it.<h4>Materials and methods</h4>For all task ...[more]

PMID: 31584655

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Project description:Importance:Adverse drug events (ADEs) account for up to 16% of emergency department (ED) visits and 7% of hospital admissions. Medication reconciliation is required for hospital accreditation because it can reduce medication discrepancies, but there is no evidence that reducing discrepancies reduces ADEs or other adverse outcomes. Objective:To evaluate whether electronic medication reconciliation reduces ADEs, medication discrepancies, and other adverse outcomes compared with usual care. Design, Setting, and Participants:This cluster randomized trial involved 3491 patients who were discharged from 2 medical units and 2 surgical units at the McGill University Health Centre, Montreal, Quebec, Canada, between October 2014 and November 2016. Data analysis took place from July 2017 to July 2019. Intervention:The RightRx intervention electronically retrieved community drugs from the provincial insurer and aligned them with in-hospital drugs to facilitate reconciliation and communication at care transitions. Main Outcomes and Measures:The primary outcome was ADEs in 30 days after discharge. Secondary outcomes included medication discrepancies, ED visits, hospital readmissions, and a composite outcome of ED visits, readmissions, and death up to 90 days after discharge. Results:Of 4656 eligible patients, 3567 (76.6%) consented to participate (2060 [57.8%] men; mean [SD] age, 69.8 [14.9] years). Overall, 76 patients died during the hospital stay, so 3491 patients were included in the analysis. There was no significant difference in the risk of ADEs between intervention and control groups (76 [4.6%] vs 73 [4.0%]; OR, 0.97; 95% CI, 0.33-1.48), ED visits (433 [26.2%] vs 488 [26.6%]; OR, 0.83; 95% CI, 0.36-1.42), hospital readmission (170 [10.3%] vs 261 [14.2%]; OR, 0.22; 95% CI, 0.06-1.14), or the composite outcome (447 [27.0%] vs 506 [27.6%]; OR, 0.75; 95% CI, 0.34-1.27) at 30 days. Medication discrepancies were significantly reduced in the intervention group compared with the control group (437 [26.4%] vs 1029 [56.0%]; OR, 0.24; 95% CI, 0.12-0.57). Changes made to community medications (OR, 1.05; 95% CI, 1.01-1.10) and new medications (OR, 1.09; 95% CI, 1.01-1.18) were significant risk factors for ADEs. Conclusions and Relevance:Electronic medication reconciliation reduced medication discrepancies but did not reduce ADEs or other adverse outcomes. Hospital accreditation should focus on interventions that reduce the risk of adverse events for patients with multiple changes to community medications. Trial Registration:ClinicalTrials.gov identifier: NCT01179867.

Project description:ObjectiveWe developed medExtractR, a natural language processing system to extract medication information from clinical notes. Using a targeted approach, medExtractR focuses on individual drugs to facilitate creation of medication-specific research datasets from electronic health records.Materials and methodsWritten using the R programming language, medExtractR combines lexicon dictionaries and regular expressions to identify relevant medication entities (eg, drug name, strength, frequency). MedExtractR was developed on notes from Vanderbilt University Medical Center, using medications prescribed with varying complexity. We evaluated medExtractR and compared it with 3 existing systems: MedEx, MedXN, and CLAMP (Clinical Language Annotation, Modeling, and Processing). We also demonstrated how medExtractR can be easily tuned for better performance on an outside dataset using the MIMIC-III (Medical Information Mart for Intensive Care III) database.ResultsOn 50 test notes per development drug and 110 test notes for an additional drug, medExtractR achieved high overall performance (F-measures >0.95), exceeding performance of the 3 existing systems across all drugs. MedExtractR achieved the highest F-measure for each individual entity, except drug name and dose amount for allopurinol. With tuning and customization, medExtractR achieved F-measures >0.90 in the MIMIC-III dataset.DiscussionThe medExtractR system successfully extracted entities for medications of interest. High performance in entity-level extraction provides a strong foundation for developing robust research datasets for pharmacological research. When working with new datasets, medExtractR should be tuned on a small sample of notes before being broadly applied.ConclusionsThe medExtractR system achieved high performance extracting specific medications from clinical text, leading to higher-quality research datasets for drug-related studies than some existing general-purpose medication extraction tools.

Project description:BackgroundMedication errors and adverse drug events are universal problems contributing to patient harm but the magnitude of these problems in Africa remains unclear.ObjectiveThe objective of this study was to systematically investigate the literature on the extent of medication errors and adverse drug events, and the factors contributing to medication errors in African hospitals.MethodsWe searched PubMed, MEDLINE, EMBASE, Web of Science and Global Health databases from inception to 31 August, 2017 and hand searched the reference lists of included studies. Original research studies of any design published in English that investigated adverse drug events and/or medication errors in any patient population in the hospital setting in Africa were included. Descriptive statistics including median and interquartile range were presented.ResultsFifty-one studies were included; of these, 33 focused on medication errors, 15 on adverse drug events, and three studies focused on medication errors and adverse drug events. These studies were conducted in nine (of the 54) African countries. In any patient population, the median (interquartile range) percentage of patients reported to have experienced any suspected adverse drug event at hospital admission was 8.4% (4.5-20.1%), while adverse drug events causing admission were reported in 2.8% (0.7-6.4%) of patients but it was reported that a median of 43.5% (20.0-47.0%) of the adverse drug events were deemed preventable. Similarly, the median mortality rate attributed to adverse drug events was reported to be 0.1% (interquartile range 0.0-0.3%). The most commonly reported types of medication errors were prescribing errors, occurring in a median of 57.4% (interquartile range 22.8-72.8%) of all prescriptions and a median of 15.5% (interquartile range 7.5-50.6%) of the prescriptions evaluated had dosing problems. Major contributing factors for medication errors reported in these studies were individual practitioner factors (e.g. fatigue and inadequate knowledge/training) and environmental factors, such as workplace distraction and high workload.ConclusionMedication errors in the African healthcare setting are relatively common, and the impact of adverse drug events is substantial but many are preventable. This review supports the design and implementation of preventative strategies targeting the most likely contributing factors.

Dataset Information

2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.

Publications

2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.

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