	US 12,080,428 B1
	Machine intelligence-based prioritization of non-emergent procedures and visits
Zeeshan Syed, Cupertino, CA (US); Devendra Goyal, San Francisco, CA (US); and Zahoor Elahi, Plano, TX (US)
Assigned to Health at Scale Corporation, San Jose, CA (US)
Filed by Health at Scale Corporation, San Jose, CA (US)
Filed on Sep. 10, 2021, as Appl. No. 17/472,455.
Claims priority of provisional application 63/076,826, filed on Sep. 10, 2020.
Int. Cl. G16H 50/20 (2018.01); G06Q 40/08 (2012.01); G16H 10/60 (2018.01); G16H 40/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)

CPC G16H 50/20 (2018.01) [G06Q 40/08 (2013.01); G16H 10/60 (2018.01); G16H 40/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)]

25 Claims

1. A computer-implemented method for using machine-learning to generate a medical recommendation, comprising:

receiving, by one or more processors, historical patient data and one or more historical risk values associated with a plurality of patients;

modifying, by the one or more processors, the historical patient data by:

computing one or more characteristics missing from the historical patient data, and

adding the computed one or more characteristics to the historical patient data to obtain augmented historical patient data;

training a machine-learning model by:

inputting the augmented historical patient data into the machine-learning model to obtain one or more estimated risk values, and

updating the machine-learning model based on a comparison of the one or more estimated risk values to the one or more historical risk values;

receiving, by the one or more processors, first patient data associated with a first patient;

modifying, by the one or more processors, the retrieved first patient data by:

computing a first set of characteristics missing from the retrieved first patient data, and

adding the computed first set of characteristics to the retrieved first patient data to obtain augmented first patient data;

receiving, by the one or more processors, second patient data associated with a second patient;

modifying, by the one or more processors, the retrieved second patient data by:

computing a second set of characteristics missing form the retrieved second patient data, and

adding the computed second set of characteristics to the retrieved second patient data to obtain augmented second patient data;

inputting the augmented first patient data into the trained machine-learning model to determine a first set of one or more risk values for the first patient;

inputting the augmented second patient data into the trained machine-learning model to determine a second set of one or more risk values for the second patient;

comparing the first set of one or more risk values and the second set of one or more risk values to determine a priority for distributing care to the first patient and the second patient; and

in accordance with the determination that the first patient has priority, generating a medical recommendation based on the priority, wherein the medical recommendation comprises an identification of a treatment for the first patient.