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Geisinger, IBM develop new predictive algorithm to detect sepsis risk

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Geisinger and IBM this week announced this week that they've co-created a new predictive model to help clinicians flag sepsis risk using data from the integrated health system's electronic health record. WHY IT MATTERS The new algorithm created with help from IBM Data Science Elite will help Geisinger can create more personalized clinical care plans for at-risk sepsis patients, according to the health system, which can increase the chances of recovery by helping caregivers pay closer attention to key factors linked to sepsis deaths. Dr. Shravan Kethireddy led a team of scientists to create a new model based on EHR data. Partnering with the IBM Data Science and AI Elite teams, researchers assembled a six-person team to develop a model to predict sepsis mortality as well as a tool to keep the team on top of the latest sepsis research. The researchers used open source technology from IBM Watson to build a predictive model that would ingest clinical data from thousands of de-identified sepsis patients spanning a decade, then used that model to predict patient mortality during the hospitalization period or during the 90 days following their hospital stay, officials say.


THCB Spotlights: Jeremy Orr, CEO of Medial EarlySign - The Health Care Blog

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Today on THCB Spotlights, Matthew speaks with Jeremy Orr, CEO of Medial EarlySign. Medial EarlySign does complex algorithmic detection of serious diseases, working on early detection of cancer and the progression of chronic disease such as diabetes. Tune in to hear more about this AI/ML company that has been working on their algorithms since before many had even heard about machine learning, what they've been doing with Kaiser Permanente and Geisinger, and where they are going next. Filmed at the HLTH Conference in Las Vegas, October 2019.


New Study Shows EarlySign's Machine Learning Algorithm Can Predict Which Cardiac Patients are at High-Risk Following Discharge

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Medial EarlySign (earlysign.com), a leader in machine-learning based solutions to aid in early detection and prevention of high-burden diseases, today announced the results of new research with Mayo Clinic assessing the effectiveness of machine learning for predicting cardiac patients' future risk trajectories following hospital discharge. 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. "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.


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.


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.