Machine Learning System for Veterinary Disease Diagnosis
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Solution Overview
Problem
Existing machine learning diagnostic systems are inadequate for handling the variability in animal patient data across species, breed, and geographic location, and struggle with large amounts of unstructured data, leading to potential diagnostic errors in veterinary care.
Innovation Solution
A method involving non-human patient-centric machine learning systems that filter patient data by species, breed, or geographic location, separate structured and unstructured data, train machine learning models to extract structured data from unstructured data, and combine them to predict disease diagnosis, using unsupervised and supervised learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning models are used for veterinary diagnosis, then the system is simple to implement, but it cannot handle large amounts of unstructured data and varies poorly across animal species, breed, and geographic location
Solution Approach 1:
The patent segments the machine learning system into multiple specialized models, each trained on specific animal species, breeds, or geographic regions. This segmentation allows each model to specialize in handling the unique characteristics and variability of particular animal groups, thereby improving adaptability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimension of adaptability by training separate machine learning models for different animal species, breeds, and geographic locations. This dimensional expansion allows the system to capture and process the unique patterns and variations specific to each animal group, significantly improving versatility across diverse veterinary cases.
2Measurement precision
If machine learning models process all patient data including unstructured data, then diagnostic accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and separates unstructured data from the overall patient data set, then applies specialized machine learning models specifically designed to process unstructured information. This extraction approach enables the system to capture valuable diagnostic insights from unstructured data while using efficient processing methods, thereby improving diagnostic accuracy without proportionally increasing processing time.
Solution Approach 2:
The patent performs preliminary processing and filtering of patient data before main analysis, organizing and pre-processing both structured and unstructured data to optimize subsequent model processing. This preliminary action reduces the computational burden during actual diagnosis, allowing comprehensive data analysis while minimizing processing time.
3Reliability
If veterinarians manually analyze all medical records and test results, then diagnostic thoroughness improves, but time consumption and human error increase
Solution Approach 1:
The patent implements machine learning models that automatically analyze medical records, test results, and other patient data without requiring manual veterinary review of every detail. The system performs self-service diagnostic analysis by processing and interpreting data autonomously, thereby maintaining high diagnostic reliability through comprehensive analysis while significantly improving diagnosis speed and reducing human error.
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning system continuously learns from diagnostic outcomes and refines its analysis. This feedback loop improves diagnostic reliability over time by capturing patterns and insights that might be missed in manual review, while maintaining high processing speed through automated operations.
Data Source
AI summary
A clinical diagnostic system for predicting disease diagnosis from animal patient data is described. The system includes instructions for training first stage and second stage machine learning models for different species and breeds of animals. The first stage machine learning model is trained on unstructured data in the animal patient data to extract structured data. The extracted structured data is combined with other structured data included in the animal patient data to train one or more second stage machine learning models. The trained first and second stage machine learning models are applied, in sequence, on new patient medical record data to predict disease diagnosis.