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GTC ON-DEMAND

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Abstract:
We'll talk about health care deep learning initiatives we're pursuing to better serve our patients. These include improving the process of prior authorization, which is not only costly, but takes time that can affect patients' conditions and customer satisfaction. We'll discuss how we're applying deep learning to enable real-time processing of prior authorizations. We'll cover how we're using deep learning to more effectively detect medical claims fraud. Instead of traditional unsupervised outlier detection, deep learning can predict the provider or member's unique features, and use those to detect abnormal medical claims and reduce false positives. And we'll also explain how we're using deep learning for multiple disease imputation and prediction. Based on a patient's historical EHR, we can accurately impute multiple medical conditions as well as predict future conditions with an eye toward intervention.
We'll talk about health care deep learning initiatives we're pursuing to better serve our patients. These include improving the process of prior authorization, which is not only costly, but takes time that can affect patients' conditions and customer satisfaction. We'll discuss how we're applying deep learning to enable real-time processing of prior authorizations. We'll cover how we're using deep learning to more effectively detect medical claims fraud. Instead of traditional unsupervised outlier detection, deep learning can predict the provider or member's unique features, and use those to detect abnormal medical claims and reduce false positives. And we'll also explain how we're using deep learning for multiple disease imputation and prediction. Based on a patient's historical EHR, we can accurately impute multiple medical conditions as well as predict future conditions with an eye toward intervention.  Back
 
Topics:
Medical Imaging & Radiology, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9399
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Abstract:

In this session, we'll discuss the application of OpenSeq2Seq, an Nvidia Research project directed at speech and text processing, to telephone use cases in Healthcare. We will begin by describing the OpenSeq2Seq project and its goals. We will then cover the speech to text use cases in healthcare, related to member interactions with customer service representatives. Finally, we'll discuss our application of OpenSeq2Seq to the datasets, including normalization and modification to the OpenSeq2Seq code (e.g. in order to enable transfer learning) as well as our results

In this session, we'll discuss the application of OpenSeq2Seq, an Nvidia Research project directed at speech and text processing, to telephone use cases in Healthcare. We will begin by describing the OpenSeq2Seq project and its goals. We will then cover the speech to text use cases in healthcare, related to member interactions with customer service representatives. Finally, we'll discuss our application of OpenSeq2Seq to the datasets, including normalization and modification to the OpenSeq2Seq code (e.g. in order to enable transfer learning) as well as our results

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Topics:
AI in Healthcare, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8230
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Abstract:
We'll present a use case of applying machine learning and deep learning to the task of imputing/predicting a medical patient diagnosis based on data elements of their member, medical, and pharmacy claims. We'll introduce deep learning approaches, a side by side comparison of machine learning models vs. deep learning models, and illustrate the operation and business value of deep learning models.
We'll present a use case of applying machine learning and deep learning to the task of imputing/predicting a medical patient diagnosis based on data elements of their member, medical, and pharmacy claims. We'll introduce deep learning approaches, a side by side comparison of machine learning models vs. deep learning models, and illustrate the operation and business value of deep learning models.  Back
 
Topics:
AI in Healthcare, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7154
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Abstract:

The need for helping elderly individuals or couples remain in their home is increasing as our global population ages. Cognitive processing offers opportunities to assist the elderly by processing information to identify opportunities for caregivers to offer assistance and support.  This project seeks to demonstrate means to improve the elderlys' ability to age at home through understanding of daily activities inferred from passive sensor analysis. This project is an exploration of the IBM Watson Cloud and Edge docker-based Blue Horizon platforms for the use of high-fidelity, low-latency, private sensing and responding at the edge using a RaspberryPi, including deep learning using NVIDIA DIGITS software, K80 GPU servers in the IBM Cloud, and Jetson TX2 edge computing.

The need for helping elderly individuals or couples remain in their home is increasing as our global population ages. Cognitive processing offers opportunities to assist the elderly by processing information to identify opportunities for caregivers to offer assistance and support.  This project seeks to demonstrate means to improve the elderlys' ability to age at home through understanding of daily activities inferred from passive sensor analysis. This project is an exploration of the IBM Watson Cloud and Edge docker-based Blue Horizon platforms for the use of high-fidelity, low-latency, private sensing and responding at the edge using a RaspberryPi, including deep learning using NVIDIA DIGITS software, K80 GPU servers in the IBM Cloud, and Jetson TX2 edge computing.

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Topics:
AI in Healthcare, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7857
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