ArticleJournal of biomedical semantics2011
A cascade of classifiers for extracting medication information from discharge summaries.
Article in Journal of biomedical semantics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks.Molecules (Basel, Switzerland) · 2022Article
- Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Fine-grained information extraction from German transthoracic echocardiography reports.BMC medical informatics and decision making · 2015Article
- Detecting concept mentions in biomedical text using hidden Markov model: multiple concept types at once or one at a time?Journal of biomedical semantics · 2014Article
- Mining the pharmacogenomics literature--a survey of the state of the art.Briefings in bioinformatics · 2012Article
- Recognition of medication information from discharge summaries using ensembles of classifiers.BMC medical informatics and decision making · 2012Article
- A study of actions in operative notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
- Louhi 2010: Special issue on Text and Data Mining of Health Documents.Journal of biomedical semantics · 2011Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundExtracting medication information from clinical records has many potential applications, and recently published research, systems, and competitions reflect an interest therein. Much of the early extraction work involved rules and lexicons, but more recently machine learning has been applied to the task.
methodsWe present a hybrid system consisting of two parts. The first part, field detection, uses a cascade of statistical classifiers to identify medication-related named entities. The second part uses simple heuristics to link those entities into medication events.
resultsThe system achieved performance that is comparable to other approaches to the same task. This performance is further improved by adding features that reference external medication name lists.
conclusionsThis study demonstrates that our hybrid approach outperforms purely statistical or rule-based systems. The study also shows that a cascade of classifiers works better than a single classifier in extracting medication information. The system is available as is upon request from the first author.
Identifiers
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.