ML Screening Model Training Using Condition-Removed Medical Data
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Solution Overview
Problem
Current cancer screening methods are invasive, costly, and not frequently accessible, leading to late diagnoses and increased mortality rates, with a need for non-invasive, cost-effective, and proactive approaches.
Innovation Solution
A processor-implemented method using machine learning models trained on pre-processed medical data from diverse sources, including text, audio, and image data, to screen for health conditions like cancer, employing AI and ML techniques to analyze routine medical encounters for early detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive procedures like colonoscopies and specialized tests like PET scans are used for cancer screening, then detection accuracy is improved, but patient comfort and accessibility deteriorate
Solution Approach 1:
The patent replaces invasive mechanical procedures (colonoscopies, biopsies) and complex specialized tests (PET scans requiring radiopharmaceutical tracers) with an AI-based analysis system that processes routine medical data. The machine learning model analyzes patterns in existing medical records, lab results, and routine test data to detect cancer indicators, eliminating the need for uncomfortable invasive procedures while maintaining high detection accuracy.
Solution Approach 2:
The patent creates a universal screening system that can identify multiple types of cancer (colorectal, lung, pancreatic, breast, prostate) using a single AI model that analyzes routine medical data. This multi-functional approach allows one system to perform what previously required multiple specialized tests, improving accessibility without sacrificing detection capability.
2Measurement precision
If specialized cancer screening tests like PET scans are used, then detection capability is improved, but cost increases
Solution Approach 1:
The patent utilizes inexpensive, routinely collected medical data (electronic health records, lab results, routine test data) instead of expensive specialized tests. The AI model processes these low-cost data sources to achieve cancer detection capability previously requiring costly PET scans, making screening economically viable for widespread use.
Solution Approach 2:
The patent creates a virtual copy of the diagnostic process by training an AI model on extensive medical data to replicate the detection capabilities of specialized tests. The machine learning system learns to identify cancer patterns from routine data, effectively copying the diagnostic function of expensive specialized tests at a fraction of the cost.
3Productivity
If cancer screening is performed only based on age and risk factors, then resource allocation is optimized, but early detection capability deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the AI model continuously analyzes patient data and updates risk assessments. The system processes routine medical encounters and lab results over time, learning from patterns in the data to identify patients who may have cancer regardless of traditional risk factors. This feedback loop enables early detection in atypical cases while maintaining efficient resource allocation by prioritizing high-risk individuals identified through AI analysis.
Data Source
AI summary
A processor-implemented method for training a machine learning model to screen for a health condition may include obtaining medical data from a population of patients, pre-processing the medical data to remove indicia of a health condition, labeling encounters of the medical data according to whether the health condition is present, and training a machine learning model on the pre-processed, labeled medical data to screen for the health condition.