	US 12,327,638 B2
	Systems and methods for diagnosing a health condition based on patient time series data
Tyler Wagner, Boston, MA (US); Murali Aravamudan, Andover, MA (US); Melwin Babu, Thrissur (IN); Rakesh Barve, Bangalore (IN); Venkataramanan Soundararajan, Andover, MA (US); Ashim Prasad, Bangalore (IN); Corinne Carpenter, Cambridge, MA (US); and Katherine Carlson, Cambridge, MA (US)
Assigned to Anumana, Inc., Cambridge, MA (US)
Filed by Anumana, Inc., Cambridge, MA (US)
Filed on Dec. 15, 2021, as Appl. No. 17/552,246.
Claims priority of provisional application 63/156,531, filed on Mar. 4, 2021.
Claims priority of provisional application 63/126,331, filed on Dec. 16, 2020.
Prior Publication US 2022/0189636 A1, Jun. 16, 2022
Int. Cl. G16H 50/20 (2018.01); G06N 3/08 (2023.01); G16H 10/60 (2018.01)

CPC G16H 50/20 (2018.01) [G06N 3/08 (2013.01); G16H 10/60 (2018.01)]

20 Claims

1. A method comprising:

identifying, by one or more hardware processors, a training set of health records comprising a first set of patient time series data, wherein the first set of patient time series data comprises a preemptive set of patient time series data captured earlier than a predetermined amount of time before a date of a positive diagnosis for a health condition;

training, by the one or more hardware processors, a neural network model using the training set of health records, wherein training the neural network model comprises:

receiving initial parameters of the neural network model, wherein the initial parameters of the neural network model are transfer learned from an independently learned self-supervised network;

dividing the training set of health records into a training set, a validation set and a test set; and

modifying the initial parameters of the neural network model as a function of the training set, the validation set and the test set; and

executing, by the one or more hardware processors, the trained neural network model to diagnose the health condition based on a second set of patient time series data.