Journal of pain and symptom management · 2020
Deep Natural Language Processing to Identify Symptom Documentation in Clinical Notes for Patients With Heart Failure Undergoing Cardiac Resynchronization Therapy.
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Leiter Richard E, Santus Enrico, Jin Zhijing, Lee Katherine C, Yusufov Miryam, Chien Isabel, Ramaswamy Ashwin, Moseley Edward T, Qian Yujie, Schrag Deborah, Lindvall Charlotta
Аннотация
CONTEXT: Clinicians lack reliable methods to predict which patients with congestive heart failure (CHF) will benefit from cardiac resynchronization therapy (CRT). Symptom burden may help to predict response, but this information is buried in free-text clinical notes. Natural language processing (NLP) may identify symptoms recorded in the electronic health record and thereby enable this information to inform clinical decisions about the appropriateness of CRT.
OBJECTIVES: To develop, train, and test a deep NLP model that identifies documented symptoms in patients with CHF receiving CRT.
METHODS: We identified a random sample of clinical notes from a cohort of patients with CHF who later received CRT. Investigators labeled documented symptoms as present, absent, and context dependent (pathologic depending on the clinical situation). The algorithm was trained on 80% and fine-tuned parameters on 10% of the notes. We tested the model on the remaining 10%. We compared the model's performance to investigators' annotations using accuracy, precision (positive predictive value), recall (sensitivity), and F1 score (a combined measure of precision and recall).
RESULTS: Investigators annotated 154 notes (352,157 words) and identified 1340 present, 1300 absent, and 221 context-dependent symptoms. In the test set of 15 notes (35,467 words), the model's accuracy was 99.4% and recall was 66.8%. Precision was 77.6%, and overall F1 score was 71.8. F1 scores for present (70.8) and absent (74.7) symptoms were higher than that for context-dependent symptoms (48.3).
CONCLUSION: A deep NLP algorithm can be trained to capture symptoms in patients with CHF who received CRT with promising precision and recall.
OBJECTIVES: To develop, train, and test a deep NLP model that identifies documented symptoms in patients with CHF receiving CRT.
METHODS: We identified a random sample of clinical notes from a cohort of patients with CHF who later received CRT. Investigators labeled documented symptoms as present, absent, and context dependent (pathologic depending on the clinical situation). The algorithm was trained on 80% and fine-tuned parameters on 10% of the notes. We tested the model on the remaining 10%. We compared the model's performance to investigators' annotations using accuracy, precision (positive predictive value), recall (sensitivity), and F1 score (a combined measure of precision and recall).
RESULTS: Investigators annotated 154 notes (352,157 words) and identified 1340 present, 1300 absent, and 221 context-dependent symptoms. In the test set of 15 notes (35,467 words), the model's accuracy was 99.4% and recall was 66.8%. Precision was 77.6%, and overall F1 score was 71.8. F1 scores for present (70.8) and absent (74.7) symptoms were higher than that for context-dependent symptoms (48.3).
CONCLUSION: A deep NLP algorithm can be trained to capture symptoms in patients with CHF who received CRT with promising precision and recall.