A Bootstrap Machine Learning Approach to Identify Rare Disease Patients from Electronic Health Records
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Rare diseases are very difficult to identify among large number of other possible diagnoses. Better availability of patient data and improvement in machine learning algorithms empower us to tackle this problem computationally. In this paper, we target one such rare disease - cardiac amyloidosis. We aim to automate the process of identifying potential cardiac amyloidosis patients with the help of machine learning algorithms and also learn most predictive factors. With the help of experienced cardiologists, we prepared a gold standard with 73 positive (cardiac amyloidosis) and 197 negative instances. We achieved high average cross-validation F1 score of 0.98 using an ensemble machine learning classifier. Some of the predictive variables were: Age and Diagnosis of cardiac arrest, chest pain, congestive heart failure, hypertension, prim open angle glaucoma, and shoulder arthritis. Further studies are needed to validate the accuracy of the system across an entire health system and its generalizability for other diseases.
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