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Showing 6 matches for the following: Inventor: "Sendak, Mark" ×
MEWS: a clinical model to predict probability of maternal sepsis trigger in obstetrics patients
Unmet Need Sepsis is a life-threatening emergency characterized by an overactive and improper inflammatory response by the body to an infection or injury leading to tissue damage, organ failure and death if not treated promptly.…
Reducing emergency department crowding through machine learning technology
Unmet Need Over the last 40 years, the number of hospitals and hospital beds in the U.S. has declined, while the demand for emergency department services has increased. As a result, emergency department crowding has…
At-home acute hospital care guidebook
Unmet Need Over the last 40 years, the number of hospitals and hospital beds in the U.S. has declined, while the demand for emergency department services has increased. As a result, a reported 90% of…
Pallialytics, a machine learning model to determine palliative-care eligible patients
Unmet Need Palliative care is specialized medical care that is focused on providing seriously ill patients with relief from symptoms, pain and stress. Palliative care can be provided at any time during an illness with…
A machine learning model to assess colorectal cancer risk using whole blood samples
Unmet Need Colorectal cancer is the third most commonly occurring cancer in men and the second in women. Early detection and treatment have contributed to an overall dropping of the death rate for several decades,…
Pythia – building a surgical database
Value Proposition Despite major advances in surgical care, complications arise in 15% of all US surgical procedures performed, with high-risk surgeries having complications in up to 50% of cases. Targeted preoperative intervention for high-risk individuals…