MACHINE LEARNING FOR HEPATITIS C

摘要

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.

主权项

1. A computer-implemented method for identifying disease progression in Hepatitis C patients, the method executed by one or more processors programmed to perform the method, the method comprising:
obtaining, at one or more processors, a set of training data including a first subset having a first plurality of patient variables associated with a first set of patients having Hepatitis C who do not experience adverse health outcomes as a result of Hepatitis C and a second subset having a second plurality of patient variables associated with a second set of patients having Hepatitis C who do experience adverse health outcomes as a result of Hepatitis C; receiving, at the one or more processors, a set of patient data for a patient collected over a period of time, wherein the set of patient data includes a first plurality of patient characteristics collected at a first time and a second plurality of patient characteristics collected at a second time; comparing, by the one or more processors, the set of patient data for the patient to the set of training data to determine a likelihood that the patient will experience adverse health outcomes as a result of Hepatitis C; and causing, by the one or more processors, an indication of the likelihood that the patient will experience adverse health outcomes to be displayed on a user interface of a network-enabled device of a health care provider, wherein the health care provider recommends a course of treatment to the patient according to the determined likelihood.