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Machine Learning Algorithm Can Predict Which Cardiac Patients Are High-Risk Post Discharge -

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The peer-reviewed retrospective data study, Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention, published in JACC: Cardiovascular Interventions, evaluated the ability of machine learning models to assess risk for patients who underwent percutaneous coronary intervention (PCI) inside the hospital and following their discharge. The analyzed algorithm was developed by Medial EarlySign data scientists to identify patients at highest risk of complications and hospital readmission after undergoing PCI, one of the most frequently performed procedures in U.S. hospitals. The analysis was based on electronic health records (EHR), demographics, and social data collected from a cohort of 11,709 unique Mayo Clinic patients who underwent 14,349 PCIs during 14,024 hospital admissions. The patients' mean age was 66.9, most were male (71.5%), 45.9% were obese, and 59.8% had a history of heart attacks. The study highlights the potential of AI solutions in supporting cardiology care teams in identifying and treating these high-risk patients.


Using AI to predict the future of cardiovascular diseases SciTech Europa

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Medial EarlySign are a leader in machine-learning based solutions in the early detection and prevention of high burden diseases using AI technology to detect the early warning signals and health risks associated with major diseases. The peer-reviewed retrospective data study, Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention, published in the Journal of the American College of Cardiology: "Cardiovascular Interventions, evaluated the ability of machine learning models to assess risk for patients who underwent percutaneous coronary intervention (PCI) inside the hospital and following their discharge. The analysed algorithm was developed by Medial EarlySign data scientists to identify patients at highest risk of complications and hospital readmission after undergoing PCI, one of the most frequently performed procedures in U.S. hospitals. "Contemporary risk models have traditionally had little success in identifying patients' post-PCI risks for complications, in-patient mortality, and hospital readmission. This study shows that machine learning tools may enable cardiology care teams to identify patients who may be on high-risk trajectories," said Rajiv Gulati, MD, Ph.D., Interventional Cardiologist at Mayo Clinic.


Medial EarlySign Machine Learning Algorithm Predicts Risk for Prediabetics Becoming Diabetic Within 1 year - insideBIGDATA

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Medial EarlySign, a developer of machine learning tools for data-driven medicine, announced the results of its clinical data study on identifying and stratifying prediabetic patients at high risk for progressing to diabetes within one year.


AI-Based Tool Identifies Patients at High Risk of Having Colorectal Cancer -

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Medial EarlySign, a leader in machine-learning (ML) based solutions to improve disease management, has shown successful results for the live, physician-supported implementation of its solution for aiding in earlier colorectal cancer detection, in new research published by the Journal of Oncology CCI. According to a recent study, the solution examined medical records of 79,000 adult Maccabi members who had not been compliant with colorectal cancer (CRC). From these, 688 men and women were identified as higher risk for CRC and recommended for further evaluation. Doctors of flagged individuals were notified and asked to follow up with their patients. Of these, 254 had colonoscopies performed by Maccabi physicians, and 19 colorectal cancers were found, as well as 22 with advanced adenomas (high-risk precancerous lesions).


Geisinger-AI vendor aim to reduce adverse events, avoid readmissions

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Israel-based Medial EarlySign and Geisinger Health System have partnered to apply advanced artificial intelligence and machine learning algorithms to Medicare claims data to predict and improve patient outcomes. An EarlySign-Geisinger proposal has been selected as one of 25 participants to advance to Stage 1 of a technology challenge from the Centers for Medicare and Medicaid Services to accelerate the development of AI and machine learning solutions for healthcare. "Approximately 4.3 million hospital readmissions occur each year in the U.S., costing more than $60 billion, with preventable adverse patient events creating additional clinical and financial burdens for both patients and healthcare systems," says David Vawdrey, Geisinger's chief data informatics officer. "Together with our partner EarlySign, we have forged a dynamic team that is rapidly developing novel solutions to achieve the Quadruple Aim of improving the patient experience of care, improving the health of populations, reducing cost and improving clinical care provider satisfaction," adds Vawdrey. The AI vendor and Danville, Penn.-based regional healthcare provider intend to develop models that predict unplanned hospital and skilled nursing facility admissions within 30 days of discharge and adverse events such as respiratory failure, postoperative pulmonary embolism or deep vein thrombosis, as well as postoperative sepsis before they occur.