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GTC ON-DEMAND

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Abstract:
Precision medicine initiatives bring tremendous opportunities to speed up scientific discovery and promote quality improvement in medicine. However, it also raises big challenges in dealing with massive data from heterogeneous sources, such as electronic health records (EHRs), -omics, and wearables. Traditional data mining and statistical learning methods tend to favor clean and structured data, which may not be able to effectively utilize the rich information embedded in biomedical data. The latest breakthrough in deep learning technologies provides a unique opportunity to retrieve information from complex and heterogeneous sources. We'll review advances in deep learning applied to precision medicine and next-generation healthcare, with a special focus on Deep Patient, a general-purpose patient representation from EHRs that facilitates clinical predictive modeling and medical analysis.
Precision medicine initiatives bring tremendous opportunities to speed up scientific discovery and promote quality improvement in medicine. However, it also raises big challenges in dealing with massive data from heterogeneous sources, such as electronic health records (EHRs), -omics, and wearables. Traditional data mining and statistical learning methods tend to favor clean and structured data, which may not be able to effectively utilize the rich information embedded in biomedical data. The latest breakthrough in deep learning technologies provides a unique opportunity to retrieve information from complex and heterogeneous sources. We'll review advances in deep learning applied to precision medicine and next-generation healthcare, with a special focus on Deep Patient, a general-purpose patient representation from EHRs that facilitates clinical predictive modeling and medical analysis.  Back
 
Topics:
Healthcare and Life Sciences, AI in Healthcare, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7563
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