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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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Abstract:

There are 14 million new cancer cases and 8.2 million cancer-related deaths worldwide per year.Innovation in the fight against cancer requires a multi-faceted approach. As patients and as stakeholders, healthcare ecosystem experts in genomics, proteomics, imaging, medicine and data sciences are cooperating in new ways. GPU computing, integrated data and novel algorithms enable the use of deep learning and artificial intelligence to transform cancer research and care. Dr. Jerry S.H. Lee, Whitehouse Cancer Moonshot Deputy Director for Research and Technology, will facilitate a thought provoking panel discussion on leveraging Artificial Intelligence to fight cancer.

There are 14 million new cancer cases and 8.2 million cancer-related deaths worldwide per year.Innovation in the fight against cancer requires a multi-faceted approach. As patients and as stakeholders, healthcare ecosystem experts in genomics, proteomics, imaging, medicine and data sciences are cooperating in new ways. GPU computing, integrated data and novel algorithms enable the use of deep learning and artificial intelligence to transform cancer research and care. Dr. Jerry S.H. Lee, Whitehouse Cancer Moonshot Deputy Director for Research and Technology, will facilitate a thought provoking panel discussion on leveraging Artificial Intelligence to fight cancer.

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Topics:
Healthcare and Life Sciences
Type:
Panel
Event:
GTC Washington D.C.
Year:
2016
Session ID:
DCS16179
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Abstract:

This talk focuses on advances in deep learning applied to precision medicine and, especially, on "deep patient", a general-purpose patient representation derived from the electronic health records (EHRs) that facilitates clinical predictive modeling. Precision medicine raises big challenges in dealing with large and massive data from heterogeneous sources, such as EHRs, genomics, and wearables. Deep learning provides a unique opportunity to retrieve information from these complex and heterogeneous sources. Here, in particular, we show how a deep architecture was able to process aggregated EHRs from the Mount Sinai Health System data warehouse to derive domain-free patient representations that can improve automatic medical predictions given the patient clinical status.

This talk focuses on advances in deep learning applied to precision medicine and, especially, on "deep patient", a general-purpose patient representation derived from the electronic health records (EHRs) that facilitates clinical predictive modeling. Precision medicine raises big challenges in dealing with large and massive data from heterogeneous sources, such as EHRs, genomics, and wearables. Deep learning provides a unique opportunity to retrieve information from these complex and heterogeneous sources. Here, in particular, we show how a deep architecture was able to process aggregated EHRs from the Mount Sinai Health System data warehouse to derive domain-free patient representations that can improve automatic medical predictions given the patient clinical status.

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Topics:
HPC and AI, HPC and AI
Type:
Talk
Event:
GTC Washington D.C.
Year:
2016
Session ID:
DCS16115
Streaming:
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