Machine Learning Models Predicting Illness Susceptibility

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional methods for analyzing the outcomes of illnesses on living organisms are qualitative and lack accuracy, necessitating a more precise approach to assess risk and predict illness outcomes.

Innovation Solution

The development of methods that utilize machine learning models to analyze genetic markers and other data, enabling the prediction of illness susceptibility and outcomes in living organisms, such as COVID-19.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional qualitative methods are used for analyzing illness outcomes, then the analysis process is simple, but the accuracy and precision of predictions are insufficient

Engineering Contradiction:

Improveprediction accuracyVSAvoidmethod complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces conventional qualitative analytical methods with machine learning models that process genetic markers and clinical data quantitatively. This substitution enables precise numerical predictions of illness susceptibility and outcomes, transforming the field from subjective qualitative assessment to objective quantitative analysis while maintaining computational efficiency through established ML algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If machine learning models are implemented to predict illness susceptibility, then prediction accuracy improves, but computational resources and data processing requirements increase

Engineering Contradiction:

Improverisk assessment accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent performs preliminary processing of genetic markers and clinical data before feeding them into machine learning models. By pre-processing and structuring the data in advance, the system reduces the computational burden during actual prediction operations, enabling high reliability risk assessments while optimizing resource utilization through efficient data preparation and feature selection.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250114044A1Predicting susceptibility of living organisms to medical conditions using machine learning models

Publication Date: 2025.04.10 GENERAL GENOMICS INC

AI summary

Embodiments of the present disclosure generally relate to methods for analyzing outcomes of illnesses, such as COVID-19, on living organisms. More particularly, embodiments of the present disclosure relate to methods for identifying risk of illness based on genetic markers and other available data, predicting results of mass exposure to an Illness based on a populations genomes and other available data, and providing indicators and methods of visualization for probability of illness in any living organism.