Patent ID: 11862345
Assignee: ROCHE MOLECULAR SYSTEMS, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method, comprising:
obtaining patients data of a plurality of patients, the patients data comprising a plurality of data categories, the data categories related to a patient characteristic of the plurality of patients and an administration of a treatment to the plurality of patients, wherein the data categories related to the administration of the treatment to a patient comprise one or more of a group consisting of: a type of admission of the patient to a hospital, a source of insurance, an operator of the hospital, a type of the hospital, a number of beds of the hospital, and a point of referral, the patients data further comprising treatment metric data of the treatment, wherein the treatment metric data reflects results of the treatment for the plurality of patients;
for each of the plurality of patients, computing a patient features vector based on patient data of the patient, each patient features vector including a plurality of features indicating values of the patient data in the plurality of data categories;
clustering the patient features vectors into a first set of first clusters based on cosine distances between the plurality of features of the patient features vectors;
for each first cluster of the first set of first clusters, computing a cluster features vector based on a respective distribution for each of the plurality of data categories, each respective distribution being determined from the plurality of features among the patient features vectors in the first cluster for the data category corresponding to the respective distribution;
clustering the cluster features vectors into a second set of second clusters based on Euclidean distances, each of the second set of second clusters corresponding to a portion of the first set of first clusters;
for each second cluster of the second set of second clusters, assigning a segment of the patients data corresponding to patients whose patient feature vectors are in the portion of the first set of first clusters corresponding to the second cluster; and
for each second cluster of the second set of second clusters, determining a treatment metric model based on performing supervised machine learning on the patients data in the second cluster using a function having the treatment metric as a dependent variable and a data category as an independent variable, wherein the treatment metric model reflects a relationship among the patient characteristics of patients, administration of the treatment, and the treatment metric data of the treatment for the patients in the second cluster, thereby enabling selection of a particular administration of treatment having optimal treatment metric data.