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Drug prescriptions are essential information that must be encoded inelectronic medical records. However, much of this information is hidden withinfree-text reports. This is why the medication extraction task has emerged. Todate, most of the research effort has focused on small amount of data and hasonly recently considered deep learning methods. In this paper, we present anindependent and comprehensive evaluation of state-of-the-art neuralarchitectures on the I2B2 medical prescription extraction task both in thesupervised and semi-supervised settings. The study shows the very competitiveperformance of simple DNN models on the task as well as the high interest ofpre-trained models. Adapting the latter models on the I2B2 dataset enables topush medication extraction performances above the state-of-the-art. Finally,the study also confirms that semi-supervised techniques are promising toleverage large amounts of unlabeled data in particular in low resource settingwhen labeled data is too costly to acquire.

Neural Medication Extraction: A Comparison of Recent Models in Supervised and Semi-supervised Learning Settings