Natural Language Processing Framework in Early Detection of Amyloidosis

BACKGROUND: Early diagnosis of systemic amyloidosis (SA) is associated with improved outcomes. However, early diagnosis remains limited by variable, nonspecific, and multisystemic presentation. OBJECTIVES: In this study, we used natural language processing (NLP) to develop models for predicting SA diagnosis. METHODS: Patients with a diagnosis of heart failure (HF) and/or neuropathy and sufficient chart data were identified at Oregon Health & Science University (development cohort) and unstructured text data from electronic health records were used to construct NLP-predictive models for the diagnosis of SA. These models were validated in an independent population with HF at Baylor Scott & White Health System (validation cohort). RESULTS: We identified 109,275 patients with a diagnosis of HF and/or neuropathy (age 57.6 ± 18.6 years, 52.9% females, and 89.6% White) in the development cohort. Of the 420 models evaluated, gradient boosted regression applied to combined notes had the best diagnostic accuracy (area under the curve [AUC] = 0.88), with 73.5% sensitivity, 86.0% specificity, 3.4% positive predictive value, and 99.8% negative predictive value in predicting SA. In the validation cohort (116,658 patients with HF, age 71.6 ± 14.3 years, 47.3% females, 75.8% White), the model had 54.1% sensitivity, 94.0% specificity, 8.8% positive predictive value, and 99.1% negative predictive value, with an AUC of 0.85. CONCLUSIONS: NLP-based prediction models have an excellent diagnostic performance for SA in patients with HF and/or neuropathy. NLP-enabled workflow can potentially lead to earlier diagnosis of amyloidosis, starting therapy and possibly improving outcomes.