Predicting animal health outcomes from laboratory test monitoring
By applying machine learning models in animal testing, analyzing animal historical laboratory data and predicting their health results, it solves the problem that clinicians have difficulty integrating and analyzing large amounts of data, and improves the efficiency and accuracy of animal care.
CN119731738APending Publication Date: 2025-03-28LABORATORY CORPORATION OF AMERICA HOLDINGS INC
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Patent Information
- Application Number
- CN202380060371.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2022-08-18
- Filing Date
- 2023-08-17
- Publication Date
- 2025-03-28
AI Technical Summary
Technical Problem
In animal testing, it is difficult for clinicians to effectively integrate and analyze large amounts of laboratory data, which may ignore important diagnostic information and patterns.
Method used
Using a machine learning-based approach, trained models to predict animal health outcomes using historical clinical laboratory test data from animals, including predicting whether veterinary attention, unplanned death, or administration treatment is required.
Benefits of technology
Improved data analytics, providing better animal care, helping veterinarians identify potential health issues and take appropriate measures in a timely manner.
✦ Generated by Eureka AI based on patent content.
Abstract
The present disclosure relates to techniques for predicting animal health outcomes from laboratory test monitoring using machine learning models. In particular, aspects relate to obtaining a data set for a plurality of animal subjects over a period of time. The data set includes clinical observation data, weight measurement data, outcome status data, or any combination thereof. The data set is processed as a numerical training set. A machine learning model is trained on the training set to predict whether a health condition of an animal subject is normal, whether a veterinarian may focus on the animal subject during an upcoming period of time, or whether the animal subject may have an out-of-plan death result during an upcoming period of time. And outputting the machine learning model.
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