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Advancing Healthcare through the Application of Predictive Knowledge Management

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ML Model Outperforms Standard Methods for Detecting Heart Attacks

Researchers at the University of Pittsburgh Medical Center (UPMC) have developed a machine learning (ML) tool capable of using electrocardiogram (ECG) readings to detect and classify heart attacks more quickly and accurately than standard approaches. The model was able to accurately reclassify 1 in 3 patients with chest pain based on heart attack risk.

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  • Back to the Basics, Other Payment Integrity Strategies Post-PHE

    Fraud in healthcare isn’t as black and white as in other industries, so providers need a comprehensive payment integrity strategy after the COVID-19 public health emergency.

    The back-to-the-basics approach is fitting after the expiration of the PHE, especially considering hospitals and physicians have both seemed to develop workflows and manage capacity better than they did early on during the pandemic. 

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  • Personalized Computational Approach Identifies Four Alzheimer's Subtypes

    Researchers presenting at this year’s Society of Nuclear Medicine and Molecular Imaging (SNMMI) Annual Meeting demonstrated that a computational model can accurately identify four subtypes of Alzheimer’s disease, which may help generate insights into the condition’s underlying biology and personalize future treatment methods.

    The model utilizes a combination of genomic and tau PET imaging data, which are analyzed using a sparse canonical correlation analysis (SCCA)-based clustering framework to identify genome variations associated with Alzheimer’s and flag disease subtypes.

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