Abstract
BackgroundInformation related to patient medication is crucial for health care; however, up to 80% of the information resides solely in unstructured text. Manual extraction is difficult and time-consuming, and there is not a lot of research on natural language processing extracting medical information from unstructured text from French corpora.ObjectiveWe aimed to develop a system to extract medication-related information from clinical text written in French.MethodsWe developed a hybrid system combining an expert rule–based system, contextual word embedding (embedding for language model) trained on clinical notes, and a deep recurrent neural network (bidirectional long short term memory–conditional random field). The task consisted of extracting drug mentions and their related information (eg, dosage, frequency, duration, route, condition). We manually annotated 320 clinical notes from a French clinical data warehouse to train and evaluate the model. We compared the performance of our approach to those of standard approaches: rule-based or machine learning only and classic word embeddings. We evaluated the models using token-level recall, precision, and F-measure.ResultsThe overall F-measure was 89.9% (precision 90.8; recall: 89.2) when combining expert rules and contextualized embeddings, compared to 88.1% (precision 89.5; recall 87.2) without expert rules or contextualized embeddings. The F-measures for each category were 95.3% for medication name, 64.4% for drug class mentions, 95.3% for dosage, 92.2% for frequency, 78.8% for duration, and 62.2% for condition of the intake.ConclusionsAssociating expert rules, deep contextualized embedding, and deep neural networks improved medication information extraction. Our results revealed a synergy when associating expert knowledge and latent knowledge.
Highlights
In 2017, medication consumption in France represented €37.8 billion in spending and 16% of the French health budget [1]
Adverse drug reactions are an important public health problem, representing a major cause of mortality (0.15% in France); one-third of admissions caused by adverse drug reactions are preventable, associated with a poorly reported drug history or rare adverse events [2,3]
Up to 80% of relevant clinical information is present solely in the form of unstructured text, which represents a major barrier to the secondary use of this type of information [5,6]
Summary
In 2017, medication consumption in France represented €37.8 billion (approximately US $45.5 billion) in spending and 16% of the French health budget [1]. Up to 80% of relevant clinical information is present solely in the form of unstructured text, which represents a major barrier to the secondary use of this type of information [5,6]. To overcome this issue, natural language processing techniques can be used to extract, normalize, and restructure drug-related information from clinical texts [6,7] and increase the information available for research and health care. Methods: We developed a hybrid system combining an expert rule–based system, contextual word embedding (embedding for language model) trained on clinical notes, and a deep recurrent neural network (bidirectional long short term memory–conditional random field). Our results revealed a synergy when associating expert knowledge and latent knowledge
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