Adaptive GUI for Time-Windowed ML Predictions
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Solution Overview
Problem
Conventional machine learning models and user interfaces in healthcare applications are limited in predicting outcomes for multiple conditions across multiple time windows, failing to account for complex interactions between these factors.
Innovation Solution
A computerized method that transforms an interactive graphical user interface using machine learning, generating selectable user interface elements corresponding to personas, extracting data structures, transforming them into input variables, and using multiple machine learning models to predict outcomes across various time windows, synthesizing these predictions into a single output variable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning models predict outcomes for a single problem within a single time window, then the model complexity is low and ease of operation is high, but the ability to account for complex interactions between multiple problems across multiple time windows is limited
Solution Approach 1:
The patent segments the prediction task into multiple independent machine learning models, each responsible for predicting a specific condition within a specific time window. This segmentation allows the system to handle multiple conditions and time windows while keeping individual model complexity manageable. The ensemble architecture divides the overall prediction problem into smaller, specialized sub-problems that can be solved independently and then combined.
Solution Approach 2:
The patent creates a universal ensemble framework that can handle multiple types of predictions across multiple conditions and time windows through a single integrated system. The ensemble model serves multiple functions by combining various specialized models, allowing the system to predict different conditions (e.g., hospitalization, emergency department visits, urgent care visits) across different time windows without requiring separate systems for each prediction task.
2Measurement precision
If multiple machine learning models are used to predict multiple conditions across multiple time windows, then prediction accuracy improves, but computational resources required increase
Solution Approach 1:
By segmenting the prediction task into specialized models for different conditions and time windows, the system can optimize each model independently for its specific task. This segmentation allows for more efficient computation compared to a single large model, as each smaller model can be trained and executed more quickly while maintaining high accuracy for its specific prediction domain.
Solution Approach 2:
The system uses partial action by deploying only the necessary subset of models for each specific prediction request. Rather than running all possible models for every prediction, the system selectively activates the relevant models based on the specific condition and time window being predicted, reducing unnecessary computational overhead while maintaining prediction accuracy.
3Loss of information
If conventional user interfaces display predictions for single problems, then the interface complexity is low, but the ability to present complex multi-condition interactions is insufficient
Solution Approach 1:
The user interface segments the display of prediction results by organizing them according to different conditions and time windows. Each prediction is presented in a structured format that shows the specific condition, the predicted outcome, and the relevant time window. This segmentation helps users understand complex multi-condition interactions by breaking down the information into manageable, condition-specific sections rather than presenting all predictions in a single undifferentiated view.
Solution Approach 2:
The patent adds temporal dimensionality to the user interface by organizing predictions across multiple time windows (e.g., 30-day, 90-day, 180-day windows). This dimensional organization allows users to view how predictions change over time and understand the temporal relationships between different conditions. The interface transforms complex multi-dimensional data into a visually organized structure that preserves information about condition interactions while remaining user-friendly.
Data Source
AI summary
A computerized method of transforming an interactive graphical user interface according to machine learning includes generating a user interface element corresponding to a persona. In response to user interaction with the user interface element, data structures related to the persona are extracted from a first data store. The data structures are transformed into a set of input variables. The method includes generating a first output variable based on the set of input variables. A second output variable based on the first input variable is generated by: generating a set of intermediate output variables, determining a first intermediate output variable of the set of intermediate output variables, and determining a second intermediate output variable based on a machine learning model corresponding to the first intermediate output variable. In response to the second output variable exceeding a first threshold, the graphical user interface displays a first message.