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