Background Standardized registries are commonly built using administrative codes assigned to patient encounters, such as the International Classification of Diseases (ICD) codes. However, fall patients are often coded using subsequent injury codes, such as hip fractures. This necessitates manual screening to ensure the accuracy of data registries. Herein, we aimed to automate the extraction of fall incidents and mechanisms using Natural Language Processing (NLP) and compare this approach with the ICD method.

Methods Clinical notes for patients with fall-induced hip fractures were retrospectively reviewed by medical experts. Fall incidences were detected, annotated, and classified among patients who had fall-induced hip fracture (case group). The control group included patients with hip fracture without any evidence of fall. NLP models were developed using the annotated notes of the study groups to fulfill two separate tasks: fall occurrence detection and fall mechanism classification. The performances of the models were compared using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and area under the ROC curve (AUC-ROC).

Results A total of 1,769 clinical notes were included in the final analysis for the fall occurrence task, and 783 clinical notes were analyzed for the fall mechanism classification task. The highest F1 score using NLP for fall occurrence was 0.97 (specificity=0.96; sensitivity=0.97) and for fall mechanism classification was 0.61 (specificity=0.56; sensitivity=0.62). NLP could detect up to 98% of the fall occurrences and 65% of the fall mechanisms accurately compared to 26% and 12%, respectively, by ICD codes.

Conclusion Our findings showed promising performance with a higher accuracy of NLP algorithms compared to the conventional method for detecting fall occurrence and mechanism in developing disease registries using clinical notes. Our approach can be introduced to other registries that are based on large data and are in need for accurate annotation and classification.

The authors have declared no competing interest.

This study did not receive any funding

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IRB of Massachusetts General Hospital gave ethical approval for this work

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