Symptom Complaint Prioritization via Machine Learning
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Solution Overview
Problem
Accurate prioritization of medical attention and treatment for user symptom complaints is often inaccurate, leading to untreated conditions and frustration for both patients and medical professionals.
Innovation Solution
A system and method that utilize a computing device to receive symptom complaint data, determine a frequency element, produce a disease criticality score through expert input, and generate a suspected disease state, incorporating a k-nearest neighbor algorithm and supervised machine-learning model to prioritize medical attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used for prioritizing medical attention, then the process is simple, but the accuracy of prioritization is poor
Solution Approach 1:
The patent introduces a computing device as an intermediary between symptom data collection and medical decision-making. This device processes symptom complaint datums through machine learning models and algorithms to generate prioritization recommendations, acting as a mediator that enhances accuracy without requiring direct complex human analysis of all symptoms
Solution Approach 2:
The patent replaces traditional manual medical assessment mechanisms with automated computing systems. Machine learning models and algorithms substitute for human clinicians' direct analysis of symptom patterns, transforming the mechanical process of symptom evaluation into an automated computational system that improves prioritization accuracy
2Measurement precision
If accurate prioritization is achieved through complex analysis, then the prioritization accuracy improves, but the time required for analysis increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on extensive medical data before actual use. The system performs preliminary analysis by continuously learning from symptom complaint datums, so that when actual prioritization is needed, the pre-trained models can quickly generate accurate recommendations without requiring time-consuming real-time complex analysis
Solution Approach 2:
The patent uses copying by creating virtual representations of medical knowledge through machine learning models. These models copy and internalize medical prioritization patterns from training data, allowing the system to rapidly reproduce accurate prioritization decisions without repeatedly performing the full complexity of original medical analysis
Data Source
AI summary
A system for prioritizing user symptom complaint inputs includes a computing device, wherein the computing device is configured to receive a plurality of symptom complaint datums generated by a user, determine a frequency element as a function of the plurality of symptom complaint datums, produce a disease criticality score as a function of the plurality of the frequency element, wherein producing further comprises obtaining an expert input, and determining the criticality score as a function of the expert input and the frequency element, and generate a suspected disease state as a function of the disease criticality score.