US 11,972,869 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 Nov. 1, 2023, as Appl. No. 18/386,056.
Application 18/386,056 is a continuation of application No. 17/552,246, filed on Dec. 15, 2021.
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 2024/0062905 A1, Feb. 22, 2024
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
OG exemplary drawing
 
1. A method for diagnosing a health condition based on patient time series data, wherein the method comprises:
receiving, using one or more hardware processors, patient time series data, wherein the patient time series data comprises an electrocardiogram (ECG) waveform;
identifying, using the one or more hardware processors, a training set of health records, wherein:
the training set of health records comprises health records of patients who have been diagnosed with a health condition of interest and training data including ECG waveforms of the patients correlated to the health condition of interest; and
identifying the training set of health records comprises identifying one or more cohorts of the patients;
training, using the one or more hardware processors, a plurality of neural network models for each cohort of the patients using the training set of health records;
selecting, using the one or more hardware processors, one or more highest performing models from the plurality of trained neural network models;
executing, using the one or more hardware processors, the one or more highest performing models as a function of the patient time series data, wherein executing the one or more highest performing models comprises:
preprocessing the time series data, wherein preprocessing the time series data comprises:
extracting one or more discrete metrics as a function of the time series data, wherein the one or more discrete metrics comprises an interval of an ECG waveform; and
predicting, using the one or more hardware processors, a health condition as a function of the patient time series data, the interval of the ECG waveform, and the one or more highest performing models.