  Accurate and timely detection of medical adverse events (AEs) from free-text
medical narratives is challenging. Natural language processing (NLP) with deep
learning has already shown great potential for analyzing free-text data, but
its application for medical AE detection has been limited. In this study we
proposed deep learning based NLP (DL-NLP) models for efficient and accurate hip
dislocation AE detection following total hip replacement from standard
(radiology notes) and non-standard (follow-up telephone notes) free-text
medical narratives. We benchmarked these proposed models with a wide variety of
traditional machine learning based NLP (ML-NLP) models, and also assessed the
accuracy of International Classification of Diseases (ICD) and Current
Procedural Terminology (CPT) codes in capturing these hip dislocation AEs in a
multi-center orthopaedic registry. All DL-NLP models out-performed all of the
ML-NLP models, with a convolutional neural network (CNN) model achieving the
best overall performance (Kappa = 0.97 for radiology notes, and Kappa = 1.00
for follow-up telephone notes). On the other hand, the ICD/CPT codes of the
patients who sustained a hip dislocation AE were only 75.24% accurate, showing
the potential of the proposed model to be used in largescale orthopaedic
registries for accurate and efficient hip dislocation AE detection to improve
the quality of care and patient outcome.
