Course Overview:

Remote patient monitoring (RPM) is emerging as a global paradigm to improve patients’ health and provide home care. It uses digital technologies to allow healthcare systems to remotely detect the early stages of a disease and monitor the symptom progression. This data allows doctors to provide customized care plans and better educate patients on self-care. Through continuous remote monitoring of the streaming medical data from patients, RPM solutions ensure timely intervention, faster & efficient diagnosis, and treatment of the condition. Consequently, RPM reduces patient visits in hospitals and automatically alerts the concerned family members/physicians in case of emergency.

In this presentation, I will demonstrate how the Internet of Things (IoT) analytics together with RPM can achieve better patient outcomes by improving diagnosis, treatment, and patient self-care.

You will learn:

• How to use RPM to quantify and monitor aspects of the MDS-UPDRS (Movement Disorders Society – Unified Parkinson’s Disease Rating Scale) questionnaire used in chartering Parkinson’s disease symptoms.
• How to efficiently stream and analyze movement data from individuals (with Parkinson’s disease and without).
• How a machine learning model classifies and identifies patients with Parkinson’s disease based on the movement data collected over several months.
• How in real-time a model tracks changes in tremors right after the intake of medicine and monitors symptoms’ progression over time.
• About the SAS solutions (SAS Visual Data Mining and Machine Learning, Model Manager, and Event Stream Processing) that implement RPM and run in Kubernetes in the Azure Cloud Platform.
• To implement the same architecture for other medical illnesses leveraging IoT.

Presenter Bio:

Dr. Divya Gupta brings over 13 years of experience in cloud computing, distributed systems, fault tolerance, big data analytics, technical consulting, and business collaborations. Divya is a Senior Solutions Architect at SAS Global IoT Division. Her current work involves the integration of SAS Solutions in the cloud/on-premises platforms using Kubernetes and Containers, providing architecture solutions to customers to solve their business problems using various technology solutions and bridging the IoT edge to the clouds. She holds a Ph.D. in Computer Science, Cloud Computing, from Université Grenoble Alpes, France.

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