Personalized Disease Progression Model for Treatment Prediction
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Solution Overview
Problem
Current technologies for predicting treatment outcomes are limited in their ability to effectively select the most suitable treatment protocol for individual patients with specific diseases, as they rely on statistical and historical data from patients with comparable characteristics, failing to accurately predict clinical outcomes for patients with unique conditions or endpoints not evaluated in past clinical trials.
Innovation Solution
A novel system and method that utilize medical data and disease progression models, modified using machine learning algorithms trained on large datasets, to create personalized disease progression models for individual patients, allowing for the evaluation and ranking of treatment protocols based on predefined endpoints such as survival or tumor size, enabling the selection of the most effective treatment protocol.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If statistical and historical data from patients with comparable characteristics are used for prediction, then the prediction method is simple and easy to implement, but the prediction accuracy for individual patients with unique conditions deteriorates
Solution Approach 1:
The patent segments the prediction approach into two levels: population-level statistical analysis for general trends and individual-level personalized modeling for specific patients. This segmentation allows the system to maintain simplicity for population studies while achieving high accuracy for individual predictions through customized disease progression models trained on each patient's specific medical data.
2Reliability
If statistical data from past clinical trials are used, then the method relies on established historical evidence, but it cannot predict outcomes for endpoints not evaluated in past trials
Solution Approach 1:
The patent implements dynamic disease progression models that can adapt to predict various endpoints beyond those in historical trials. The models are designed to simulate different treatment scenarios and predict outcomes for multiple endpoints including survival, progression-free survival, and tumor response, allowing the system to provide reliable predictions for both established and novel endpoints by dynamically adjusting to different prediction targets.
3Measurement precision
If personalized disease progression models are created using machine learning, then prediction accuracy for individual patients improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training disease progression models on comprehensive medical data before actual prediction is needed. The system pre-processes and structures patient data, pre-trains base models on population data, and prepares the computational framework in advance. This preliminary preparation reduces the complexity burden during actual prediction by having the heavy computational lifting done beforehand, making the personalized prediction process more manageable despite the inherent complexity of machine learning models.
Data Source
AI summary
A computerized system and method for planning a medical treatment for an individual under specific medical condition comprises a data input utility configured for receiving input data, and a data processor. The input data includes first input data comprising medical data of a specific individual, and second input data comprising data indicative of at least one endpoint of treatment. The data processor is configured for utilizing the medical data of the specific individual and the data indicative of the at least one endpoint and processing data indicative of disease progression models, each disease progression model corresponding to a treatment plan comprising one or more predetermined treatment protocols for treating the specific medical condition. The data processor generates output data indicative of a treatment effect on the individual with respect to each of the treatment plans and at least one endpoint to evaluate the treatment plans.