Neural Network Predictive Computing System for Clinical Trial Patient Screening
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Solution Overview
Problem
Current predictive modeling in clinical trials is time-consuming and prone to inaccuracies due to the manual feature engineering process, often resulting in high screening failure rates and missed enrollment targets, as data scientists struggle to create optimal features for predicting patient conditions.
Innovation Solution
A predictive computing system utilizing neural networks to process structured healthcare data, automatically learning computing rules to identify undiagnosed medical conditions and predict future health issues, thereby streamlining feature engineering and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature engineering is used for predictive modeling in clinical trials, then the model can be built with existing tools, but the process becomes time-consuming and prone to inaccuracies
Solution Approach 1:
The system performs self-service by automatically learning optimal feature representations from raw healthcare data through neural network training. The model autonomously identifies relevant patterns and features without requiring manual feature engineering, thereby eliminating time-consuming manual processes while improving prediction accuracy through data-driven feature selection.
Solution Approach 2:
The patent replaces the mechanical manual feature engineering process with an automated neural network-based system. Instead of manually creating and selecting features, the system uses deep learning models to automatically learn optimal feature representations from raw data, substituting human manual work with automated computational processes that are both faster and more accurate.
2Reliability
If manual feature engineering is used, then existing tools can be utilized, but screening failure rates are high and enrollment targets are missed
Solution Approach 1:
The system autonomously optimizes prediction models by automatically training neural networks on historical clinical trial data. This self-service capability enables the system to continuously improve screening accuracy and reliability without manual intervention, thereby reducing screening failure rates and improving patient enrollment efficiency through automated model refinement.
Solution Approach 2:
The system implements feedback mechanisms by training neural networks on historical clinical trial data and using the learned models to improve future patient screening. The feedback loop continuously refines prediction accuracy by incorporating outcomes from previous screenings, thereby increasing screening success rates and enrollment efficiency through iterative model improvement.
3Productivity
If data scientists manually create features, then control over feature selection is maintained, but the process becomes complex and inefficient
Solution Approach 1:
The system performs self-service by automatically learning optimal feature representations from raw healthcare data through neural network training. This eliminates the need for complex manual feature engineering processes while maintaining control over feature selection through automated model training, thereby improving productivity and reducing process complexity simultaneously.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining data for a set of patients that each have a certain condition. A first and second sequence of data is determined based on the obtained data. A scoring model is generated by processing the first and second sequence of data to train a neural network. The scoring model determines a confidence that an individual has the particular healthcare condition. Patient scoring data is provided to the scoring model to determine the confidence that the individual has the healthcare condition. A confidence score is received as an output of the scoring model in response to providing the patient scoring data. The confidence score represents a determined confidence that the individual has the healthcare condition. An indication that represents the confidence that the individual has the healthcare condition is provided based on the received confidence score.