A cascade of classifiers for extracting medication information from discharge summaries

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.

Recognition of medication information from discharge summaries using ensembles of classifiers

“…The first task was treated as a sequence labeling task and fields were considered as named entities [10,13]. In this paper, for the sake of convenience, we refer to the term “named entities” or “entities” as fields which have the same meaning as [10,13].…”

Integrating Patient-Related Entities Using Hospital Information System Data and Automatic Analysis of Free Text

“…Another important extraction task is the automatic recognition of drugs and dosages, which occur in the patient record texts. State-of-the-art results reported for English are: sensitivity/recall for drug names 88,5% and for dosage 90,8%; precision for drug names 91,2% and for dosage 96,6% [11]. A measure that combines the sensitivity (recall) and the precision is their harmonic mean f-score; another highly successful extraction system is MedEx [12] which extracts drug names with f-score 93,2%, and achieves f-scores 94,5% for dosage, 93,9% for route and 96% for frequency.…”

Extracting Information about Medication Use from Veterinary Discussions

“…Previous work on extracting medication information from text has primarily focused on clinical medical text, such as discharge summaries (e.g., Halgrim et al, 2010;Doan et al, 2012;Tang et al, 2013;Segura-Bedmar et al, 2013)). The Third and Fourth i2b2 Shared Tasks included medication detection from clinical texts (Uzuner et al, 2010;Uzuner et al, 2011), and the Fourth i2b2 Shared Task also included relation classification between treatments (including medications), problems, and tests.…”

“…Many methods have been used for medication extraction, including rule based approaches (Levin et al, 2007;, machine learning (Patrick and Li, 2010;Tang et al, 2013), and hybrid methods (Halgrim et al, 2010;Meystre et al, 2010). Rule based and hybrid approaches typically rely on manually created lexicons and rules.…”