Hypertension Panel Detection Using Multi-Model Time-Series Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for detecting hypertension attributes in patient time-series data require extensive human interaction, are inefficient, and prone to inaccuracies when receiving singular inputs.
Innovation Solution
An apparatus and method utilizing a processor and memory to input patient time-series data into a hypertension panel comprising multiple hypertension models, generating hypertension attributes and confidence scores through a combination of first and second hypertension models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current systems use singular input for hypertension detection, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system segments the hypertension detection task into multiple specialized models (first hypertension model for initial assessment, second hypertension model for verification) that each process the same time-series data independently, then combines their outputs to achieve higher precision than any single model could provide
Solution Approach 2:
The patent merges the outputs of multiple hypertension models through a confidence score mechanism, combining the first hypertension attribute and second hypertension attribute to generate a final hypertension attribute with enhanced measurement precision that overcomes the limitations of singular input systems
2Reliability
If current systems require extensive human interaction, then reliability may improve through supervision, but productivity deteriorates
Solution Approach 1:
The system implements self-service by automatically processing patient time-series data through multiple hypertension models and generating confidence scores without requiring human interaction for each detection, thereby maintaining reliability through automated verification while dramatically improving productivity
Solution Approach 2:
The system uses feedback mechanisms where the confidence score is generated based on the comparison and combination of results from multiple hypertension models, providing automated self-verification that maintains reliability without human supervision while enabling high-throughput processing
Data Source
AI summary
An apparatus and method for detecting hypertension attributes in a patient time-series data includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a patient time-series data associated with a patient, input the patient time-series data into a hypertension panel wherein the hypertension panel comprises of a plurality of hypertension models, generate the hypertension attribute from the hypertension panel as a function of the patient time-series data and a hypertension model, and generate a confidence score from the hypertension panel as a function of the patient time-series data and the hypertension model.