Abstract: To predict which Hepatitis C patients are at high-risk for disease progression or adverse health outcomes, baseline characteristics are measured for patients as well as longitudinal data, including clinical, laboratory and/or biopsy results, which may be collected periodically in follow-up visits with a healthcare professional. A machine learning engine may predict whether a patient is at high-risk for disease progression or adverse health outcomes based on the baseline characteristics and the longitudinal data for the patient.

Abstract: A spray cap for use with an endoscope. The endoscope includes a liquid channel and an open channel. The spray cap includes a body portion connectable with an endoscope having an end wall defining an obstruction portion configured to obstruct the liquid channel of the endoscope and an open portion configured to permit use of the open channel. The spray cap having an orifice system extending through the obstruction portion in fluid communication with the liquid channel to direct fluid flow from the liquid channel into a treatment volume of a patient along a modified flow pattern.

Abstract: To predict which Hepatitis C patients are at high-risk for disease progression or adverse health outcomes, baseline characteristics are measured for patients as well as longitudinal data, including clinical, laboratory and/or biopsy results, which may be collected periodically in follow-up visits with a healthcare professional. A machine learning engine may predict whether a patient is at high-risk for disease progression or adverse health outcomes based on the baseline characteristics and the longitudinal data for the patient.

Abstract: A method for identifying patients with a high risk of liver cancer development includes receiving patient data describing a plurality of patients and executing a patient identification module on the patient data to identify at least some of the plurality of patients as having a high risk of developing liver cancer. The patient identification module is generated based on an application of machine learning techniques to a training data set, and the patient identification module is validated based on both the training data set and an external validation data set. Further, the method includes generating a grouping of the plurality of patients based on the identification of the at least some of the plurality of patients.

Abstract: A method of using a variable set from complete blood counts and blood chemistry panels to generate a machine learned algorithm for determining the effectiveness of thiopurine treatment on inflammatory bowel disease (IBD) patients using CART, boosted trees, random forest classification, RuleFit and/or logistic regression analysis.

Abstract: A method of using a variable set from complete blood counts and blood chemistry panels to generate a machine learned algorithm for determining the effectiveness of thiopurine treatment on inflammatory bowel disease (IBD) patients using CART, boosted trees, random forest classification, RuleFit and/or logistic regression analysis.