University of Pittsburgh

Automated Machine-learning Based Differential Diagnosis of Heart Diseases from Emergency Department Visits

PhD student
Friday, December 7, 2018 - 1:00pm - 1:30pm

Generating differential diagnosis is a subjective process primarily relying on a physician’s experience that could benefit from a machine-learning based decision support tool. However, currently no differential diagnosis models for heart diseases, which can be automatically built from electronic health records, are available for emergency department (ED) settings. To develop and evaluate a decision-tree based differential diagnosis model automatically built from structured and unstructured ED data, We merged 205 heart disease related ICD-9-CM codes into 6 categories of cardiac diseases based on clinical similarity, subjects were emergency department visits to 15 hospitals in the University of Pittsburgh Medical Center (UPMC) Health system with primary diagnosis including one of 205 ICD-9-CM codes for heart disease. We used decision tree algorithm to learn rules for differentiating these 6 categories using unstructured electronic health record data parsed with natural language processing. The result show a decision tree model was able to be automatically built from analyzing structured and unstructured ED data and to perform differential diagnosis of 6 categories of cardiac diseases, we also tried some other algorithms to improve the performance of classic decision tree algorithm.

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