Adaptive Medical Decision Support System for Personalized Treatment
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Solution Overview
Problem
Current medical technologies lack an effective system to link genetic alterations to prognosis and therapy, especially for personalized medicine approaches, due to the large number of genetic variations and limited analytical reproducibility of genetic data.
Innovation Solution
An adaptive medical decision support system that utilizes a linked, learning database to rank treatment options based on expected efficacy and side effects, clinical experience, and genetic profiles, allowing for real-time sharing of clinical experience among users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a system links genetic alterations to prognosis and therapy for personalized medicine, then treatment efficacy is improved, but the complexity of managing large numbers of genetic variations increases
Solution Approach 1:
The system segments the complex task of personalized medicine into distinct functional modules: a database module for storing genetic alterations and clinical data, a processing module for analyzing genetic profiles, and a recommendation module for suggesting treatments. This segmentation manages system complexity by dividing the overall function into manageable, specialized components that can be developed and maintained independently.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw genetic data and treatment recommendations. This intermediary module standardizes and interprets genetic variations, converting complex genetic profiles into actionable clinical insights. The intermediary handles the complexity of genetic data interpretation, shielding the treatment recommendation system from direct exposure to raw genetic variation complexity.
2Loss of time
If clinical experience is shared across the medical community in real-time, then learning time is reduced, but the quantity of data to be processed increases
Solution Approach 1:
The system extracts only the essential and relevant clinical experience data from the vast amount of available information. The database selectively stores standardized fields such as genetic alterations, diagnoses, treatments administered, and outcomes. This extraction approach reduces the effective data quantity that needs to be processed while maintaining the critical information needed for clinical decision-making.
Solution Approach 2:
The patent transforms clinical experience data into standardized parameters and structured formats. By converting unstructured clinical narratives into standardized data fields (genetic alterations, diagnoses, treatments, outcomes), the system enables efficient processing and comparison. This parameter transformation reduces the computational burden of handling raw clinical data while preserving the essential learning value.
3Measurement precision
If treatment options are ranked based on expected efficacy and side effects, then treatment selection accuracy is improved, but the computational requirements increase
Solution Approach 1:
The system performs preliminary ranking and filtering of treatment options based on available genetic profile data before detailed analysis. The database pre-organizes treatment information by genetic alterations and disease types, so that when a patient's genetic profile is input, only relevant treatment options need to be considered. This preliminary organization reduces the computational scope while maintaining accurate treatment selection.
Solution Approach 2:
The patent implements a tiered approach to treatment ranking where the most critical factors (genetic alterations, disease type) are evaluated first with high precision, while less critical factors are evaluated with lower computational intensity. This partial action approach achieves sufficient treatment selection accuracy without requiring exhaustive analysis of all possible treatment parameters, thereby reducing overall computational energy requirements.
Data Source
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AI summary
A system, method and data sharing architecture are disclosed to be used by a group of people linked together in a network for treatment of human diseases, to assign preference rank to treatment option based on the similarity of the given patient's case to cases treated by the users of the same method previously.