ML NLP Logic for Veterinary Sample Retention
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Solution Overview
Problem
Current veterinary diagnostic methods face challenges in correlating medical observations with biological sample markers, leading to frustration in identifying conditions due to lack of association between observed clinical signs and sample data.
Innovation Solution
The implementation of machine-learning natural language processing logic trained on labeled medical records to extract keywords from medical information and determine if detected characteristics in biological samples are outside a configurable threshold, thereby deciding whether to retain the samples for further testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual correlation methods are used to associate medical observations with biological sample markers, then veterinarians can review sample data, but the process is time-consuming and inefficient leading to frustration in identifying conditions
Solution Approach 1:
The patent replaces the manual mechanical process of correlating medical observations with sample markers with an automated machine learning system. The ML model automatically extracts features from both medical records and sample data, performs correlation analysis, and generates associations without human intervention, thereby dramatically improving efficiency and eliminating time loss associated with manual review
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between medical observations and biological sample markers. This intermediary automatically processes and correlates the two data types, generating associations that would be difficult or time-consuming for veterinarians to establish manually, thus resolving the efficiency contradiction
2Reliability
If comprehensive testing of all biological samples is performed, then all potential conditions can be identified, but the cost and complexity of sample management increases significantly
Solution Approach 1:
The patent applies local quality by directing comprehensive analysis only to samples that show potential relevance based on initial screening. The system identifies specific subsets of samples warranting further investigation rather than uniformly processing all samples, thereby maintaining high reliability for condition identification while reducing overall management complexity and costs
Solution Approach 2:
The patent performs preliminary filtering and prioritization of biological samples before comprehensive testing. By pre-screening samples using automated methods and ranking them by potential relevance to identified conditions, the system prepares only the most promising samples for detailed analysis, reducing complexity while preserving identification accuracy
3Manufacturing precision
If biological samples are retained for extended periods for further testing, then more thorough analysis can be performed, but storage costs and resource requirements increase
Solution Approach 1:
The patent applies partial action by retaining only the subset of biological samples that show potential relevance to identified conditions, rather than storing all samples. The machine learning system identifies and flags specific samples warranting retention based on their correlation with medical observations and condition markers, thereby reducing the quantity of stored samples while maintaining sufficient precision for thorough analysis of relevant cases
Data Source
AI summary
An example computer-implemented method for identifying biological samples from non-human subjects for testing includes receiving medical information of a non-human subject, receiving test results including a detected characteristic of a component indicative of a condition for a biological sample from the non-human subject, extracting one or more keywords from the medical information by executing a machine-learning natural language processing logic, and determining whether the detected characteristic is outside of a configurable threshold. In response to determining that the detected level is outside of the configurable threshold and based on the keywords being present in the medical information, the method includes generating instructions to retain the biological sample for further testing.