Machine learning for hepatitis c
AssigneeThe Regents of the University of Michigan
InventorsMonica A. Konerman, Ulysses Balis, Peter Higgins, Ji Zhu, Anna Lok, Akbar Waljee, Yiwei Zhang
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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.
Claims20
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.
A computing device for identifying disease progression in Hepatitis C patients, the computing device comprising:
- a communication network,
- one or more processors;
- and
- a non-transitory computer-readable memory coupled to the one or more processors and storing thereon instructions that, when executed by the one or more processors, cause the computing device to:
- obtain 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;
- receive, via the communication network, 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;
- compare 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
- cause, via the communication network, 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.
Dependencies are inferred from the claim text.