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GTC ON-DEMAND

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Abstract:
We'll present work in progress on a deep learning system that extracts expert-level knowledge from the published and less formal medical literature. Using a large curated source of 5 million biomedical journal articles, disease encyclopedias such as The Merck Manuals and The Mayo Clinic's Guide to Diseases and Conditions, as well as hospital-based physician reference material, we'll demonstrate that it's possible to infer existing medical concepts such as disease-disease, disease symptom, and disease-drug relationships with an unsupervised deep learning model. We'll extend this model to show that it's capable of answering multiple-choice medical questions that are typically given to medical students as part of the licensing examination.
We'll present work in progress on a deep learning system that extracts expert-level knowledge from the published and less formal medical literature. Using a large curated source of 5 million biomedical journal articles, disease encyclopedias such as The Merck Manuals and The Mayo Clinic's Guide to Diseases and Conditions, as well as hospital-based physician reference material, we'll demonstrate that it's possible to infer existing medical concepts such as disease-disease, disease symptom, and disease-drug relationships with an unsupervised deep learning model. We'll extend this model to show that it's capable of answering multiple-choice medical questions that are typically given to medical students as part of the licensing examination.  Back
 
Topics:
Healthcare and Life Sciences, AI in Healthcare, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7653
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