Patent ID: 11915833
Assignee: B.G. NEGEV TECHNOLOGIES AND APPLICATIONS LTD., AT BEN-GURION UNIVERSITY
Field: Medical technology (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A method for predicting a disease state and/or progression rate performed on a computing device having a processor, memory, and one or more code sets stored in the memory and executed in the processor, the method comprising:
receiving feature-based representations of a first plurality of patients, the feature-based representations including static and dynamic features;
assigning a second plurality of plurality of patients among a plurality of patient clusters, each of the plurality of second patients represented with said static and dynamic features;
training a cluster-specific classifier for each of the plurality of patient clusters, each respective cluster-specific classifier operative to predict a disease state for a portion of the second plurality of patients of a respective one of the plurality of patient clusters;
iteratively running a training cycle until a performance measure is achieved, the training cycle including:
predicting disease states for the portion of the second plurality of patients with each of the respective cluster-specific classifiers,
reassigning at least one of the plurality of second patients to a different one of the plurality of patient clusters, wherein the respective cluster-specific classifier of the different patient cluster predicts a disease state and/or progression rate best matching a true disease state and/or a progression rate of said at least one of the plurality of second patients, and
retraining each of the respective cluster-specific classifiers using patients associated with each of the plurality of patient clusters after the reassigning;

receiving feature-based representations of a new patient;
assigning the new patient to a particular one of the plurality of patient clusters based on the received feature-based representations of the new patient; and
using the retrained cluster-specific classifier of the particular patient cluster to predict a disease state and/or progression rate of the new patient.