Geisinger has been granted a patent for a method that predicts a patient’s risk level for a clinical endpoint using echocardiographic video and health record data processed by a trained neural network. The system generates a mortality risk score for medical practitioners or healthcare administrators to view. GlobalData’s report on Geisinger gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Geisinger, was a key innovation area identified from patents. Geisinger's grant share as of February 2024 was 15%. Grant share is based on the ratio of number of grants to total number of patents.
Predicting mortality risk based on echocardiographic video using neural network
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A recently granted patent (Publication Number: US11864944B2) outlines a method involving the use of a trained neural network to generate a mortality risk score based on echocardiographic videos of a patient's heart. The method includes receiving the echocardiographic video, providing it to the neural network, receiving the mortality risk score, and outputting it for review by medical professionals. The neural network consists of multiple layers trained to process the input video data and generate the risk score, which indicates the likelihood of the patient dying within a specific timeframe from when the video was recorded. The training dataset used for the neural network includes various echocardiographic views and electronic health record data associated with heart failure.
Furthermore, the patent details the inclusion of various parameters in the electronic health record dataset, such as demographic information, vital signs, laboratory measurements, echocardiogram-based data, and diagnosis details. The neural network is trained to analyze this information along with the echocardiographic videos to generate a comprehensive mortality risk score. The method also involves generating reports based on the risk score and providing sufficient information to healthcare professionals for making treatment decisions, including eligibility assessments for treatments like cardiac transplantation, mechanical support device implantation, defibrillator placement, palliative care, or hospice. Additionally, a system is described in the patent that includes a processor executing instructions to perform the method steps, emphasizing the integration of neural network analysis with electronic health record data to assess mortality risk accurately.
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