Natalia Grabar

Linguistic approach for identification of medication names and related information in clinical narratives

BACKGROUND Pharmacotherapy is an integral part of any medical care process and plays an important role in the medical history of most patients. Information on medication is crucial for several tasks such as pharmacovigilance, medical decision or biomedical research. OBJECTIVES Within a narrative text, medication-related information can be buried within other non-relevant data. Specific methods, such as those provided by text mining, must be designed for accessing them, and this is the objective of this study. METHODS The authors designed a system for analyzing narrative clinical documents to extract from them medication occurrences and medication-related information. The system also attempts to deduce medications not covered by the dictionaries used. RESULTS Results provided by the system were evaluated within the framework of the I2B2 NLP challenge held in 2009. The system achieved an F-measure of 0.78 and ranked 7th out of 20 participating teams (the highest F-measure was 0.86). The system provided good results for the annotation and extraction of medication names, their frequency, dosage and mode of administration (F-measure over 0.81), while information on duration and reasons is poorly annotated and extracted (F-measure 0.36 and 0.29, respectively). The performance of the system was stable between the training and test sets.

Combining an Expert-Based Medical Entity Recognizer to a Machine-Learning System: Methods and a Case Study

Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system are often combined with data-driven systems. We present here a case study where an existing expert-based medical entity recognition system, Ogmios, is combined with a data-driven system, Caramba, based on a linear-chain Conditional Random Field (CRF) classifier. Our case study specifically highlights the risk of overfitting incurred by an expert-based system. We observe that it prevents the combination of the 2 systems from obtaining improvements in precision, recall, or F-measure, and analyze the underlying mechanisms through a post-hoc feature-level analysis. Wrapping the expert-based system alone as attributes input to a CRF classifier does boost its F-measure from 0.603 to 0.710, bringing it on par with the data-driven system. The generalization of this method remains to be further investigated.