Case Study: Revolutionising Dialysis, how a NVIDIA AI Model can predict Heart Failure risk with 90% accuracy
Overview
Taipei Veterans General Hospital (TVGH) in Taiwan is deploying real-time AI risk prediction during kidney dialysis procedures to improve outcomes for the country’s nearly 85,000 dialysis patients, aiming to mitigate the risk of heart failure, the leading cause of death among dialysis patients.
Taipei Veterans General Hospital is analysing streaming data during dialysis procedures with the NVIDIA Jetson edge AI platform.
The Challenge
Kidney dialysis patients face the risk of heart failure during the procedure due to complications such as inaccurate dry weight estimation, which can lead to hypertension or dehydration. Monitoring multiple data points during dialysis is crucial to prevent these complications.
The Solution
TVGH developed an AI risk assessment tool that processes various physiological and dialysis machine values, medical records, blood test results, and medication information to predict heart failure risk in real-time during dialysis. The tool utilises AI algorithms and was trained on a dataset of 3 million health records. It leverages NVIDIA Jetson Xavier NX for real-time AI inference and edge computing power, allowing for immediate data analysis during dialysis sessions.
Benefits & Results
TVGH’s AI model has achieved 90% accuracy in predicting heart failure risk and has reduced the deviation rate in clinicians’ assessment of dry weight by 80%, minimising the risk of complications for dialysis patients. The adoption of real-time AI inference using NVIDIA Jetson Xavier NX ensures timely detection of abnormal patterns in patient data, potentially saving lives and improving patient outcomes. To read more about the TVGH’s AI model please visit the NVIDIA blog.
Testimonial
“A half-hour window between data analysis still left gaps where a patient may begin experiencing complications that can lead to heart failure, so our team worked to find a real-time solution that could receive and compute data generated by dialysis machines within milliseconds.”
– Shou-Ming Ou, visiting staff in the nephrology division at TVGH
This case study first appeared on nvidia.com
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