Treatment Effect Prediction System Using Pathological Data Matching
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Solution Overview
Problem
Conventional medical systems lack the ability to accurately predict treatment effects for patients, relying heavily on physician experience and limited measurable examination values, which can lead to inaccuracies in determining the best course of treatment for conditions like diabetes and cancer.
Innovation Solution
A treatment effect prediction system that uses pathological condition information obtained from diagnostic data to find similar conditions in a database, retrieving corresponding treatment effects, allowing for a more precise prediction of treatment outcomes by simulating patient conditions using biological models and accessing stored data on treatment methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional medical systems use only directly measurable examination values to predict treatment effects, then the system complexity remains low, but the prediction accuracy deteriorates
Solution Approach 1:
The patent introduces pathological condition information as an intermediary that bridges the gap between directly measurable examination values and treatment effect predictions. This intermediary layer processes and transforms raw examination data into meaningful pathological insights, enabling more accurate treatment predictions without requiring direct measurement of all relevant medical parameters.
Solution Approach 2:
The system segments the treatment prediction process into distinct functional modules: examination value acquisition, pathological condition information generation, similarity comparison, and treatment effect prediction. This segmentation allows each module to specialize in specific tasks, improving overall prediction accuracy while maintaining manageable system complexity through modular architecture.
2Reliability
If physicians rely on their own experience and perception to select treatment methods, then the need for complex prediction systems is reduced, but the objectivity and consistency of treatment decisions deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms by comparing patient pathological condition information against a database of similar cases and their treatment outcomes. This feedback loop provides objective data-driven insights that complement physician experience, enhancing decision consistency while maintaining the physician's role in final treatment selection.
Solution Approach 2:
The system performs preliminary analysis by generating pathological condition information and retrieving similar cases before the physician makes the final treatment decision. This preliminary action prepares structured, evidence-based recommendations that guide physician decision-making, improving consistency without replacing clinical judgment.
3Adaptability or versatility
If conventional systems only monitor examination results and provide drug dosages, then the system complexity remains low, but the ability to provide comprehensive treatment support information deteriorates
Solution Approach 1:
The system achieves multi-functionality by integrating multiple capabilities into a single platform: examination result monitoring, pathological condition analysis, similar case retrieval, treatment effect prediction, and dosage recommendation. This universal approach provides comprehensive treatment support across various clinical scenarios while managing system complexity through integrated architecture.
Data Source
AI summary
A treatment effect prediction system, comprising: a processor; and a memory, under control of the processor, including instructions enabling the processor to carry out operations comprising: determining a patient pathological condition information, which represents a feature of pathological condition of a patient, based on diagnostic data of the patient; accessing a database of stored pathological condition information and corresponding treatment effects occurring when predetermined treatment is provided; and retrieving, from the database, a specific treatment effect corresponding to the one of the stored pathological condition information that is similar to the patient pathological condition information, is disclosed. A treatment effect prediction method and a computer program product thereof are also disclosed.