Dosage Evaluation Model for Patient-Specific Drug Safety
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Solution Overview
Problem
Existing methods for determining drug dosages in patients are often based on population pharmacokinetics and medical professional experience, leading to variability in appropriateness due to individual differences in physiological conditions, which can result in overdose or underdose issues.
Innovation Solution
A method utilizing a computing device to evaluate the appropriateness of drug dosages by feeding physiological and medication parameters into a dosage evaluation model, which is established using machine learning algorithms and training data sets containing characteristic parameters of subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If population pharmacokinetics and medical professional experience are used to determine drug dosage, then the method is simple and easy to implement, but the accuracy of dosage determination deteriorates due to individual differences in physiological conditions
Solution Approach 1:
The patent introduces a dosage evaluation model as an intermediary between the simple population pharmacokinetics method and the need for accurate individualized dosage determination. This model acts as a mediator that processes multiple individual physiological parameters (age, weight, renal function, liver function, etc.) to generate accurate dosage recommendations, thereby maintaining ease of operation while improving measurement precision through the mediating computational model.
Solution Approach 2:
The patent transforms the dosage determination process by changing from using single population-average parameters to utilizing multiple individual physiological parameters (age, weight, height, renal function indicators like creatinine clearance, liver function indicators, etc.). This parameter transformation enables the system to account for individual differences while maintaining a standardized evaluation framework that is easy to implement clinically.
2Measurement precision
If individualized dosage determination is implemented to account for physiological differences, then the accuracy of dosage determination is improved, but the complexity of the evaluation process increases
Solution Approach 1:
The patent creates a universal dosage evaluation model that can handle multiple different physiological parameters and drug types through a single integrated system. This multi-functional model accepts various input parameters (demographic, physiological, medication history) and provides dosage evaluation for different drug categories, thereby reducing the apparent complexity by providing a unified approach rather than separate evaluation methods for each scenario.
Solution Approach 2:
The patent replaces complex manual clinical judgment and multiple separate evaluation methods with a computational model that automatically processes physiological parameters. This substitution of mechanical/computational processing for human expert analysis simplifies the evaluation process while maintaining or improving accuracy, as the computational model systematically integrates all relevant parameters without human cognitive limitations.
3Measurement precision
If conventional AI techniques are used to determine drug dosage, then the accuracy is improved, but the reliability and safety are worsened due to potential overdose or underdose
Solution Approach 1:
The patent incorporates feedback mechanisms where the dosage evaluation model continuously refines its recommendations based on patient response and actual outcomes. The system allows for iterative adjustment of dosage recommendations, comparing predicted outcomes with actual patient responses, and using this feedback to improve future dosage determinations. This feedback loop enhances both reliability and safety by enabling continuous validation and correction of dosage recommendations.
Solution Approach 2:
The patent implements beforehand cushioning by incorporating safety margins and boundary conditions in the dosage evaluation model. The system pre-establishes safe dosage ranges, contraindications, and warning thresholds based on the input physiological parameters. This preventive approach cushions against potential overdose or underdose by built-in safety mechanisms that alert clinicians to potential risks before dosage administration, thereby enhancing reliability without sacrificing accuracy.
Data Source
AI summary
A method for evaluating appropriateness of dosage of a target drug administered to a patient is adapted to be implemented by a computing device that stores a dosage evaluation model. The method comprises steps of: obtaining at least one physiological parameter that is related to a physiological condition of the patient; obtaining at least one medication parameter that is related to a usage condition of the target drug by the patient; and feeding said at least one physiological parameter and said at least one medication parameter into the dosage evaluation model to obtain an evaluation result that indicates the appropriateness of the dosage of the target drug administered to the patient.