Diagnoses-Based Disease Prediction Module
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Solution Overview
Problem
Current disease prediction methods rely on subjective clinician interpretation and fail to accurately forecast future diseases in individuals, as they lack objective data-driven approaches to analyze biomarkers and interrelationships between diseases.
Innovation Solution
A system utilizing a population information set of disease diagnoses and a subject-specific information set to apply prediction models, selecting members with common diseases to generate future disease predictions, incorporating various models like collaborative filtering and neural networks for accurate forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinician interpretation is used for disease diagnosis, then medical expertise can be applied, but subjective variability leads to inaccurate diagnosis
Solution Approach 1:
The patent replaces the mechanical system of human clinician interpretation with an automated prediction system that uses machine learning algorithms and statistical models to analyze patient data, thereby eliminating subjective variability while maintaining diagnostic capability
Solution Approach 2:
The patent introduces an intermediary prediction system that acts as a mediator between raw patient data and clinical decision-making, using algorithmic processing to transform unstructured medical records into standardized risk assessments
2Measurement precision
If traditional disease prediction methods are used, then future disease forecasting can be attempted, but lack of objective data-driven approaches reduces prediction accuracy
Solution Approach 1:
The patent performs preliminary action by systematically collecting and standardizing patient data, biomarkers, and disease outcomes in advance, creating a structured foundation that enables accurate retrospective analysis and prospective prediction
Solution Approach 2:
The patent applies parameter changes by transforming qualitative clinical observations into quantitative metrics, and by normalizing diverse biomarker data into standardized parameters that can be processed algorithmically for consistent prediction results
3Reliability
If clinician expertise is used for disease prediction, then medical knowledge can be applied, but subjective variability causes inconsistent predictions
Solution Approach 1:
The patent creates a universal prediction system that can handle multiple disease types, patient populations, and data formats through a single standardized algorithmic framework, ensuring consistent application of medical knowledge across diverse clinical scenarios
Solution Approach 2:
The patent segments the complex prediction task into distinct modular components including data preprocessing, feature extraction, risk calculation, and outcome prediction, allowing each component to be optimized independently while maintaining overall system consistency
Data Source
AI summary
A system for predicting future disease for a subject comprising: a population information set comprising population disease diagnoses for members of a population; a subject-specific information set comprising at least one subject-specific disease diagnosis; and a diagnoses-based prediction module configured to predict one or more future diseases for the subject based on said subject-specific disease diagnosis and said population disease diagnoses for population members having at least one disease in common with the subject.