Talk Highlight — ‘Automated Machine-learning Based Differential Diagnosis’

The speaker presented about an application of leveraging machine learning techs to diagnose heart diseases automatically. Currently those diagnoses mainly rely on the experience of physicians, and it is usually slow for emergency visits. Machine learning techniques can help to speed this up, however, currently there are no differential diagnosis models for heart diseases available for emergency departments. The speaker proposed a tree-based model that is capable of being built from structural data. By leveraging this rule-based model the speaker successfully differentiated the 6 types of heart diseases with health record data with NLP techniques included. From the results presented, it is a model with high potential to be used in real conditions, however, currently I think the recall of this model is still subject to improvement.

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