Entity extraction for clinical notes, a comparison between metamap and amazon comprehend medical

Extracting meaningful information from clinical notes is challenging due to their semi- or unstructured format. Clinical notes such as discharge summaries contain information about diseases, their risk factors, and treatment approaches associated to them. As such, it is critical for healthcare quality as well as for clinical research to extract those information and make them accessible to other computerized applications that rely on coded data. In this context, the goal of this paper is to compare the automatic medical entity extraction capacity of two available entity extraction tools: MetaMap (MM) and Amazon Comprehend Medical (ACM). Recall, precision and F-score have been used to evaluate the performance of the tools. The results show that ACM achieves higher average recall, average precision, and average F-score in comparison with MM.