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Rare diseases are very difficult to identify among large number of otherpossible diagnoses. Better availability of patient data and improvement inmachine learning algorithms empower us to tackle this problem computationally.In this paper, we target one such rare disease - cardiac amyloidosis. We aim toautomate the process of identifying potential cardiac amyloidosis patients withthe help of machine learning algorithms and also learn most predictive factors.With the help of experienced cardiologists, we prepared a gold standard with 73positive (cardiac amyloidosis) and 197 negative instances. We achieved highaverage cross-validation F1 score of 0.98 using an ensemble machine learningclassifier. Some of the predictive variables were: Age and Diagnosis of cardiacarrest, chest pain, congestive heart failure, hypertension, prim open angleglaucoma, and shoulder arthritis. Further studies are needed to validate theaccuracy of the system across an entire health system and its generalizabilityfor other diseases.

A Bootstrap Machine Learning Approach to Identify Rare Disease Patients from Electronic Health Records