Prognosis Prediction System Using Weighted Genomic Data Normalization
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Solution Overview
Problem
Current methods for predicting a patient's prognosis or response to therapy are inaccurate and lack a systematic approach, often relying on trial and error, as physicians have limited knowledge about how a patient will respond to a specific treatment before administration.
Innovation Solution
A method that determines a target patient's prognosis by comparing their data with a reference set of patients who share common features, normalizing and weighting the data, and applying classification methods to predict the best matches and potential treatment responses, utilizing genomic and clinical data to guide treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trial and error methods are used for treatment prediction, then physicians can identify effective treatments through experimentation, but the process is time-consuming and lacks accuracy in predicting patient response
Solution Approach 1:
The system performs preliminary analysis by comparing a target patient's genomic and clinical data against a reference set of patients before treatment is administered. This pre-assessment identifies the most likely treatment responses in advance, eliminating the need for time-consuming trial and error while maintaining high prediction accuracy through systematic data comparison and classification.
2Measurement precision
If comprehensive patient data analysis is performed to improve prediction accuracy, then more features can be considered, but the system complexity increases
Solution Approach 1:
The system segments the comprehensive patient data into distinct categories: genomic data, clinical data, and comparison features. Each segment is processed independently through normalization and weighting specific to its type, then integrated through classification. This segmentation allows the system to handle complex multi-type data systematically while maintaining manageable processing steps and reducing overall system complexity.
3Reliability
If a systematic approach with multiple data types is implemented, then prediction reliability improves, but the ease of operation decreases due to complex data processing requirements
Solution Approach 1:
The system implements self-service through automated normalization methods that adapt to different data types, automatic weighting calculations based on feature importance, and classification algorithms that require minimal manual configuration. Once the reference set is established, the system autonomously processes target patient data through the complete workflow, significantly improving ease of operation while maintaining high prediction reliability through consistent application of the systematic approach.
Data Source
AI summary
Methods and systems for predicting prognosis of a patient are provided. An example method can comprise determining a target set, wherein the target set comprises patient data from a target patient, determining a reference set, wherein the reference set comprises patient data from a plurality of patients, and the plurality of patients have one or more features in common with the target patient, determining common comparison features, normalizing common comparison feature data by a normalization method, weighting the normalized common comparison feature data, and determining one or more best matches by applying a classification method to the weighted normalized common comparison feature data. Finally, the prognosis of the patient in the target set is predicted.