Personalized Adverse Drug Event Tolerance Prediction System
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Solution Overview
Problem
Current adverse drug reaction databases are limited in predicting unplanned reactions, lack personalized patient data, and fail to incorporate real-time feedback, leading to inefficiencies in medication dosing and treatment outcomes.
Innovation Solution
A computer-implemented method and system that receives real-time data on drug doses and adverse reactions to calculate personalized adverse drug reaction tolerance for a candidate drug or drug pair, using a processor to refine predictions based on known drug data and patient-specific information, including demographic and genomic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current adverse drug reaction databases are used for prediction, then general drug safety information is available, but personalized patient prediction accuracy is poor
Solution Approach 1:
The patent applies local quality by transitioning from general population-level drug reaction data to patient-specific personalized data. The system collects and analyzes individual patient responses to drugs, creating localized prediction models for each patient based on their unique characteristics, thereby improving prediction accuracy for personalized medicine.
Solution Approach 2:
The system performs preliminary action by collecting patient-specific drug reaction data before administering candidate drugs. Through pre-screening and baseline data collection during treatment phases, the system prepares personalized prediction models in advance, enabling more accurate tolerance predictions before critical dosing decisions are made.
2Measurement precision
If real-time data collection is implemented, then personalized prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies universality by creating a multi-functional prediction system that handles multiple data types (demographic, genomic, real-time reaction data), performs various analysis functions (tolerance prediction, dosing optimization), and serves different clinical purposes. This integrated approach manages complexity through unified architecture rather than separate systems.
Solution Approach 2:
The system uses an intermediary computational processing layer that mediates between raw real-time data collection and clinical decision-making. This intermediary layer aggregates, validates, and processes diverse data streams, transforming complex raw data into actionable prediction results, thereby managing system complexity through structured data flow management.
3Productivity
If known drug data is used for candidate drugs, then prediction speed is maintained, but prediction personalization is reduced
Solution Approach 1:
The patent applies merging by combining known population-level drug data with patient-specific real-time data. The system integrates general drug safety information from databases with individualized patient response data, creating a hybrid prediction model that maintains the speed advantages of established data while incorporating personalized elements for improved accuracy.
Solution Approach 2:
The system applies partial action by selectively applying personalization to specific prediction aspects while relying on known drug data for other components. Rather than requiring complete personalization for all predictions, the system uses known drug data for established safety profiles and supplements with personalized data where it provides marginal gains, optimizing the balance between speed and personalization.
Data Source
AI summary
Embodiments include method, systems and computer program products for predicting adverse drug events on a computational system. Aspects include receiving a personalized data set including a plurality of real-time drug doses for a first drug or drug combination and a plurality of corresponding real-time adverse drug reaction tolerance data for the first drug or drug combination for a patient. Aspects also include receiving known drug data for a candidate drug or drug pair. Aspects also include calculating, based upon the known drug data and the personalized data set, a predicted adverse drug reaction tolerance for the candidate drug or drug pair at a candidate dosage, wherein the predicted adverse drug reaction tolerance is personalized to the patient.