Ensemble machine-learning models to detect respiratory syndromes
AssigneeThe COVID Detection Foundation
InventorAmil Khanzada
Life of this patent
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Abstract
Provided is a process including: obtaining, with one or more processors, a set of data comprising a plurality of patient records, selecting a subset of the plurality of parameters for inputs into a machine learning system, generating a classifier using the machine learning system based on the training data and the subset of the plurality of parameters for inputs; receiving, with one or more processors, patient record of a first user; performing an analysis, with one or more processors, to identify acoustic measures from a voice sample of the first user.
Claims20
A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:
- obtaining, with a computer system, a trained machine learning model configured to infer whether users have a respiratory illness based on both audio and an image captured by mobile computing devices of the users, wherein the trained machine learning model is trained by:
- obtaining a training set comprising a plurality of training records, wherein:
- each training record in the training set includes a plurality of parameters and corresponding values for a respective person;
- each training record in the training set includes audio of the respective person's voice and an image of at least part of the respective person;
- and
- each training record in the training set includes an indicator indicating whether or not the respective person has been diagnosed with a respiratory illness;
- and
- training the machine learning model on the training set to infer whether users have the respiratory illness based on both the audio and images;
- after obtaining the trained machine learning model, receiving, with the computer system, a first user record of a first user, the first user record comprising an audio file or stream of a voice of the first user and an image of at least part of the first user;
- inferring, with the computer system, that the first user has the respiratory illness based on the audio file or stream of a voice of the first user and an image of at least part of the first user;
- and
- storing, with the computer system, an indication that the first user has the respiratory illness in memory.
A method, comprising:
- obtaining, with a computer system, a trained machine learning model configured to infer whether users have a respiratory illness based on both audio and an image captured by mobile computing devices of the users, wherein the trained machine learning model is trained by:
- obtaining a training set comprising a plurality of training records, wherein:
- each training record in the training set includes a plurality of parameters and corresponding values for a respective person;
- each training record in the training set includes audio of the respective person's voice and an image of at least part of the respective person;
- and
- each training record in the training set includes an indicator indicating whether or not the respective person has been diagnosed with a respiratory illness;
- and
- training the machine learning model on the training set to infer whether users have the respiratory illness based on both the audio and images;
- after obtaining the trained machine learning model, receiving, with the computer system, a first user record of a first user, the first user record comprising an audio file or stream of a voice of the first user and an image of at least part of the first user;
- inferring, with the computer system, that the first user has the respiratory illness based on the audio file or stream of a voice of the first user and an image of at least part of the first user;
- and
- storing, with the computer system, an indication that the first user has the respiratory illness in memory.
Dependencies are inferred from the claim text.