SYSTEMS AND METHODS FOR DEEP LEARNING-BASED PET HEALTH PREDICTIONS

1 . A method for predicting pet health conditions, the method comprising:

receiving, by at least one processor, activity data generated by at least one sensor device configured to detect activity of a pet;

determining, by the at least one processor, behavior data indicative of a plurality of behaviors of the pet based on the activity data;

receiving, by the at least one processor, a trained neural architecture for a representation model configured to facilitate health condition predictions and another neural architecture for a classification model configured to predict a health condition of the pet based at least in part on the behavior data;

predicting, by the at least one processor using the trained neural architecture, presence of the health condition of the pet; and

causing, by the at least one processor, a user device to display a notification including information identifying the health condition.

2 . The method of claim 1 , wherein the plurality of behaviors comprises at least two of drinking, eating, scratching, licking, and lying down.

3 . The method of claim 1 , wherein the health condition comprises at least one of an ear infection, a renal infection, a dermatological disease, a dermatological disease, a weight-associated disease, a musculoskeletal disease, and arthritis.

4 . The method of claim 1 , wherein the neural architecture for the representation model is generated by:

generating, by the at least one processor, a plurality of preliminary neural architectures;

evaluating, by the at least one processor, each of the preliminary neural architectures using an evaluation metric; and

selecting, by the at least one processor, the neural architecture from the plurality of preliminary neural architecture based on the evaluation metric.

5 . The method of claim 1 , wherein the neural architecture for the representation model is generated by:

performing, by the at least one processor utilizing an autoencoder or other dimensionality reduction model, a dimensionality reduction of training data provided to the machine learning model.

6 . The method of claim 1 , wherein determining the behavior data of the pet based on the activity data is performed using a machine learning model.

7 . The method of claim 1 , further comprising:

receiving, by the at least one processor, demographic data or genetic data including at least one of a gender of the pet, a height of the pet, and a weight of the pet,

wherein predicting the presence of a health condition of the pet is based at least in part on the demographic data.

8 . The method of claim 1 , wherein the notification comprises a recommendation to perform a diagnostic test to confirm the prediction of the health condition.

9 . A computer system for predicting pet health conditions, the system comprising:

at least one memory having processor-readable instructions stored therein; and

at least one processor configured to access the at least one memory and execute the processor-readable instructions, which when executed by the at least one processor cause the at least one processor to perform a plurality of functions, including functions for:

receiving activity data generated by at least one sensor device configured to detect activity of a pet;

determining behavior data indicative of a plurality of behaviors of the pet based on the activity data;

receiving a trained neural architecture for a representation model configured to facilitate health condition predictions and another neural architecture for a classification model configured to predict a health condition of the pet based at least in part on the behavior data;

predicting, using the trained neural architecture, presence of the health condition of the pet; and

causing a user device to display a notification including information identifying the health condition.

10 . The system of claim 9 , wherein the plurality of behaviors comprises at least two of drinking, eating, scratching, licking, and lying down.

11 . The system of claim 9 , wherein the health condition comprises at least one of an ear infection, a renal infection, a dermatological disease, a dermatological disease, a weight-associated disease, a musculoskeletal disease, and arthritis.

12 . The system of claim 9 , wherein the neural architecture for the representation model is generated by:

generating, by the at least one processor, a plurality of preliminary neural architectures;

evaluating, by the at least one processor, each of the preliminary neural architectures using an evaluation metric; and

selecting, by the at least one processor, the neural architecture from the plurality of preliminary neural architecture based on the evaluation metric.

13 . The system of claim 9 , wherein the neural architecture for the representation model is generated by:

performing, by the at least one processor utilizing an autoencoder or other representation model, a dimensionality reduction of training data provided to the machine learning model.

14 . The system of claim 9 , wherein the plurality of functions further include functions for:

receiving, by the at least one processor, demographic data or genetic data including at least one of a gender of the pet, a height of the pet, and a weight of the pet,

wherein predicting the presence of a health condition of the pet is based at least in part on the demographic data.

15 . The system of claim 9 , wherein the notification comprises a recommendation to perform a diagnostic test to confirm the prediction of the health condition.

16 . A non-transitory computer-readable medium configured to store instructions that, when executed by at least one processor of a device for predicting pet health conditions, cause the at least one processor to perform operations comprising:

receiving activity data generated by at least one sensor device configured to detect activity of a pet;

determining behavior data indicative of a plurality of behaviors of the pet based on the activity data;

receiving a trained neural architecture for a representation model configured to facilitate health condition predictions and another neural architecture for a classification model configured to predict a health condition of the pet based at least in part on the behavior data;

predicting, using the trained neural architecture, presence of the health condition of the pet; and

causing a user device to display a notification including information identifying the health condition.

17 . The non-transitory computer-readable medium of claim 16 , wherein the neural architecture is generated by:

generating, by the at least one processor, a plurality of preliminary neural architectures;

evaluating, by the at least one processor, each of the preliminary neural architectures using an evaluation metric; and

selecting, by the at least one processor, the neural architecture from the plurality of preliminary neural architecture based on the evaluation metric.

18 . The non-transitory computer-readable medium of claim 16 , wherein the neural architecture is generated by:

performing, by the at least one processor utilizing an autoencoder or other dimensionality reduction model, a dimensionality reduction of training data provided to the machine learning model.

19 . The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise:

receiving, by the at least one processor, demographic data or genetic data including at least one of a gender of the pet, a height of the pet, and a weight of the pet,

wherein predicting the presence of a health condition of the pet is based at least in part on the demographic data.

20 . The non-transitory computer-readable medium of claim 16 , wherein the notification comprises a recommendation to perform a diagnostic test to confirm the prediction of the health condition.

21 . A computer-implemented method for training a machine learning model to predict a pet health condition, the computer-implemented method comprising:

receiving, by at least one processor, behavior data of a pet and at least one characteristic of the pet;

determining, by the at least one processor, one or more pet cyclical patterns of the pet based on the behavior data, wherein the behavior data includes one or more data points that correspond to a behavior level at a time period;

determining, by the at least one processor via a machine learning model, at least one similar pet that includes at least one similar characteristic to the at least one characteristic of the pet;

retrieving, by the at least one processor, one or more similar pet cyclical patterns from a data store, wherein the one or more similar pet cyclical patterns correspond to the at least one similar pet, and wherein the one or more similar pet cyclical patterns include one or more similar pet data points that each correspond to a similar pet activity level at the time period;

determining, by the at least one processor via the machine learning model, one or more pet cyclical pattern shapes that correspond to a majority of the one or more data points for each of the one or more pet cyclical patterns;

determining, by the at least one processor via the machine learning model, one or more similar pet cyclical pattern shapes that correspond to a majority of the one or more similar pet data points for each of the one or more similar pet cyclical patterns;

analyzing, by the at least one processor via the machine learning model, each of the one or more similar pet cyclical pattern shapes and each of the one or more pet cyclical pattern shapes to determine one or more differentials;

based on the one or more differentials and labels associated with each pet and/or similar pet, performing, by the at least one processor via the machine learning model, a binary classification of the pet to predict one or more health conditions of the pet; and

outputting, by the at least one processor, the binary classification to one or more displays.

22 . The computer-implemented method of claim 21 , wherein the one or more data points correspond to average hourly data across one or more days.

23 . The computer-implemented method of claim 21 , wherein analyzing each of the one or more similar pet cyclical pattern shapes and each of the one or more pet cyclical pattern shapes to determine the one or more differentials includes utilizing an algorithm to compare the one or more similar pet cyclical pattern shapes and each of the one or more pet cyclical pattern shapes.

24 . The computer-implemented method of claim 21 , wherein the analyzing includes determining a shape shifting direction based on the one or more differentials to determine one or more behavior changes.

25 . The computer-implemented method of claim 21 , wherein the analyzing includes determining, by the machine learning model, one or more relationships between the one or more pet cyclical pattern shapes and the one or more similar pet cyclical pattern shapes.

26 . The computer-implemented method of claim 21 , the computer-implemented method further comprising:

outputting, by the at least one processor, the one or more pet cyclical patterns of the pet and the one or more similar pet cyclical patterns of the at least one similar pet to the one or more displays.

27 . The computer-implemented method of claim 26 , the computer-implemented method further comprising:

applying, by the at least one processor, the one or more pet cyclical pattern shapes to the output one or more pet patterns; and

applying, by the at least one processor, the one or more similar pet cyclical pattern shapes to the output one or more similar pet patterns.

28 . The computer-implemented method of claim 21 , wherein the behavior data include activity data, and wherein the activity data is generated by at least one sensor device configured to detect activity of the pet.