Unsupervised Learning System for Anomalous Healthcare Pattern Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional pattern detection methods in healthcare struggle to efficiently identify and rank anomalous subsets in populations with complex and diverse outcomes, often requiring exhaustive searches and being biased by intuition or prior knowledge, and fail to uncover hidden patterns or identify model biases.
Innovation Solution
A computer-implemented method using unsupervised learning to detect, rank, and visualize anomalous subsets in populations, automating input data preparation, algorithm execution, and report generation, which includes feature selection, parameter choice, and conditional scanning to emphasize actionable features and values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional pattern detection methods are used to identify anomalous subsets in healthcare populations, then the analysis can be performed with simple methods, but the methods fail to efficiently identify and rank anomalous subsets and require exhaustive searches
Solution Approach 1:
The patent replaces conventional mechanical pattern detection methods with an unsupervised machine learning system that automatically detects, ranks, and visualizes anomalous subgroups. The system uses algorithms to process healthcare data without requiring exhaustive manual searches or human intuition, thereby improving efficiency while managing complexity through automation.
Solution Approach 2:
The system performs self-service by automatically detecting, ranking, and visualizing anomalous subsets without requiring human intervention for each analysis step. The unsupervised learning algorithm independently identifies patterns and ranks anomalies based on the data provided, eliminating the need for exhaustive manual searches.
2Loss of information
If conventional pattern detection methods are used, then the implementation is simpler, but the methods are biased by intuition or prior knowledge and fail to uncover hidden patterns
Solution Approach 1:
The patent replaces human intuition-based pattern detection with an unsupervised machine learning system that objectively analyzes healthcare data. This substitution eliminates biases from prior knowledge and intuition, allowing the system to uncover hidden patterns that conventional methods would miss, while managing complexity through automated algorithmic processing.
3Measurement precision
If exhaustive searches are performed to identify anomalous subsets, then more complete patterns can be found, but the analysis becomes less efficient and more time-consuming
Solution Approach 1:
The patent replaces exhaustive manual searches with an automated unsupervised learning system that efficiently processes healthcare data to identify and rank anomalous subsets. The system achieves complete pattern detection through algorithmic analysis without requiring time-consuming exhaustive searches, thereby maintaining measurement precision while significantly reducing analysis time.
Data Source
AI summary
A computer-implemented method for differentiating patterns of care (DPoC) to detect anomalous subsets in any given population with a defined set of outcomes and features. The computer-implemented method includes detecting the anomalous subsets, ranking the anomalous subsets based on a score of each anomalous subset that is reflective of an anomaly thereof, specifying whether each of the anomalous subsets overlaps with another one of the anomalous subsets, whether each of the anomalous subsets is unique and whether each of the anomalous subsets is conditional and specifying as to whether the detecting of each of the anomalous subsets has a higher or lower outcome than expected.