ArticleJournal of the American Medical Informatics Association : JAMIA2020
Ensemble method-based extraction of medication and related information from clinical texts.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Large language model ensemble for automated TNM staging from radiology reports.Bioinformatics (Oxford, England) · 2026Article
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Article
- Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.JMIR formative research · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Do LLMs Surpass Encoders for Biomedical NER?Proceedings. IEEE International Conference on Healthcare Informatics · 2025Article
- Comparing generative and extractive approaches to information extraction from abstracts describing randomized clinical trials.Journal of biomedical semantics · 2024Article
- Grammar-constrained decoding for structured information extraction with fine-tuned generative models applied to clinical trial abstracts.Frontiers in artificial intelligence · 2024Article
- Extracting social determinants of health from clinical note text with classification and sequence-to-sequence approaches.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models.BMC medical research methodology · 2023Observational
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Adverse drug event detection using natural language processing: A scoping review of supervised learning methods.PloS one · 2023Article
- Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning.Healthcare (Basel, Switzerland) · 2022Article
- Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Recent Developments in Privacy-Preserving Mining of Clinical Data.ACM/IMS transactions on data science · 2021Article
- Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.Patterns (New York, N.Y.) · 2021Article
- Extracting Drug Names and Associated Attributes From Discharge Summaries: Text Mining Study.JMIR medical informatics · 2021Article
- Hybrid Deep Learning for Medication-Related Information Extraction From Clinical Texts in French: MedExt Algorithm Development Study.JMIR medical informatics · 2021Article
- Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models.JMIR 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
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectiveAccurate and complete information about medications and related information is crucial for effective clinical decision support and precise health care. Recognition and reduction of adverse drug events is also central to effective patient care. The goal of this research is the development of a natural language processing (NLP) system to automatically extract medication and adverse drug event information from electronic health records. This effort was part of the 2018 n2c2 shared task on adverse drug events and medication extraction. MATERIALS AND
methodsThe new NLP system implements a stacked generalization based on a search-based structured prediction algorithm for concept extraction. We trained 4 sequential classifiers using a variety of structured learning algorithms. To enhance accuracy, we created a stacked ensemble consisting of these concept extraction models trained on the shared task training data. We implemented a support vector machine model to identify related concepts.
resultsExperiments with the official test set showed that our stacked ensemble achieved an F1 score of 92.66%. The relation extraction model with given concepts reached a 93.59% F1 score. Our end-to-end system yielded overall micro-averaged recall, precision, and F1 score of 92.52%, 81.88% and 86.88%, respectively. Our NLP system for adverse drug events and medication extraction ranked within the top 5 of teams participating in the challenge.
conclusionThis study demonstrated that a stacked ensemble with a search-based structured prediction algorithm achieved good performance by effectively integrating the output of individual classifiers and could provide a valid solution for other clinical concept extraction tasks.
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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.