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挖掘FDA药品标签
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医疗管理
Abstract
Background: Cincinnati Children’s Hospital Medical Center (CCHMC) has built the initial Natural Language
Processing (NLP) component to extract medications with their corresponding medical conditions (Indications,
Contraindications, Overdosage, and Adverse Reactions) as triples of medication-related information ([(1) drug
name]-[(2) medical condition]-[(3) LOINC section header]) for an intelligent database system, in order to improve
patient safety and the quality of health care. The Food and Drug Administration’s (FDA) drug labels are used to
demonstrate the feasibility of building the triples as an intelligent database system task.
Methods: This paper discusses a hybrid NLP system, called AutoMCExtractor, to collect medical conditions
(including disease/disorder and sign/symptom) from drug labels published by the FDA. Altogether, 6,611 medical
conditions in a manually-annotated gold standard were used for the system evaluation. The pre-processing step
extracted the plain text from XML file and detected eight related LOINC sections (e.g. Adverse Reactions, Warnings
and Precautions) for medical condition extraction. Conditional Random Fields (CRF) classifiers, trained on token,
linguistic, and semantic features, were then used for medical condition extraction. Lastly, dictionary-based postprocessing
corrected boundary-detection errors of the CRF step. We evaluated the AutoMCExtractor on manuallyannotated
FDA drug labels and report the results on both token and span levels.
Results: Precision, recall, and F-measure were 0.90, 0.81, and 0.85, respectively, for the span level exact match; for
the token-level evaluation, precision, recall, and F-measure were 0.92, 0.73, and 0.82, respectively.
Conclusions: The results demonstrate that (1) medical conditions can be extracted from FDA drug labels with high
performance; and (2) it is feasible to develop a framework for an intelligent database system.
Keywords: Medical condition, Disease and disorders, Sign and symptoms, cTAKES, NLP, Natural language
processing, IE, Information extraction, CRF, Conditional random fields, FDA drug labels