Extraction of measurements from medical reports

A patients' medical record represents their medical history and enclose interesting information about their health status within written reports. These reports usually contain measurements (among other information) that need to be reviewed before any new medical intervention, since they might influence the medical decision regarding the types of drugs that are prescribed or their dosage. In this paper, we introduce a method that extracts measurements automatically from textual medical discharge summaries, admission notes, progress notes, and primary care notes. We don't distinguish between reports belonging to different services. For doing so, we propose a system that uses Grobid-quantities to extract value/unit pairs, uses generated rules from analysis of medical reports and text mining tools to identify candidate measurements. These candidates are then classified using a Long Short Term Memory (LSTM) network trained model to determine which is the corresponding measurement to the value/unit pair. The results are promising: 95.13% accuracy, a precision of 92.38%, a recall of 94.01% and an F1 score of 89.49%.