Detection of Muscle Weakness in Medical Texts Using Natural Language Processing.
Journal:
Studies in health technology and informatics
PMID:
32570367
Abstract
Identifying adverse events in clinical documents is demanded in retrospective clinical research and prospective monitoring of treatment safety and cost-effectiveness. We proposed and evaluated a few methods of semi-automated muscle weakness detection in preoperative clinical notes for a larger project on predicting paresis by images. The combination of semi-expert and machine learning methods demonstrated maximized sensitivity = 0.860 and specificity = 0.919, and largest AUC = 0.943 with a 95% CI [0.874; 0.991], outperforming each method used individually. Our approaches are expected to be effective for autoshaping a well- verified training dataset for supervised machine learning.