Personalized Treatment Model Using Physiological Data Analysis
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Solution Overview
Problem
Current treatments are often arbitrarily recommended and lack customization, leading to adverse effects due to uninformed selection and implementation, without measures to detect and recommend treatments tailored to individual user needs.
Innovation Solution
A system and method using a computing device to calculate condition state labels from user physiological data, generate a treatment model with a machine-learning algorithm, and output treatments based on user preferences, receiving and scoring treatment responses to refine the treatment approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If treatments are arbitrarily recommended based on current trends and literature, then treatment implementation is simplified, but treatment effectiveness deteriorates due to lack of customization
Solution Approach 1:
The system customizes treatment parameters specifically for each user based on their physiological data, rather than applying uniform treatment protocols. The treatment model generates individualized condition state labels and selects treatments tailored to each user's specific condition, achieving local optimization of treatment effectiveness while maintaining systematic implementation through automated processing.
Solution Approach 2:
The system dynamically adjusts treatment parameters based on user-specific physiological data and condition state labels. The treatment model modifies treatment characteristics according to calculated parameters such as user age, condition severity, and physiological measurements, enabling adaptive treatment optimization without requiring manual customization for each case.
2Device complexity
If uninformed treatment selection is used, then decision-making process is simplified, but adverse effects increase due to lack of personalized assessment
Solution Approach 1:
The system automatically performs treatment selection and customization without requiring manual expert assessment for each case. The treatment model autonomously processes user physiological data, calculates condition state labels, and generates personalized treatment recommendations, enabling informed decision-making through automated analysis rather than simplified but uninformed selection.
Solution Approach 2:
The system incorporates treatment response scoring to evaluate the effectiveness of recommended treatments and refine future recommendations. By analyzing user responses and outcomes, the system continuously improves treatment selection accuracy, reducing adverse effects through learned optimization rather than static, uninformed protocols.
3Productivity
If standardized treatment protocols are applied, then treatment delivery is streamlined, but individual user needs are not met
Solution Approach 1:
The treatment model serves multiple functions within a single system: it processes diverse physiological data types, generates condition state labels, selects appropriate treatments, and evaluates treatment responses. This multi-functional approach enables customized treatment delivery without requiring separate specialized systems for each treatment aspect, maintaining efficiency while achieving adaptability.
Solution Approach 2:
The system dynamically adapts treatment recommendations based on real-time physiological data and user responses. Rather than applying fixed standardized protocols, the treatment model continuously adjusts treatment parameters according to changing user conditions and treatment effectiveness, enabling personalized treatment delivery that responds to individual user needs while maintaining systematic processing.
Data Source
AI summary
A system for customizing treatments. The system includes a computing device configured to record a user biological extraction containing an element of user physiological data. The computing device is configured to receive condition state training data and generate a condition state model utilizing a first machine-learning algorithm. The computing device is configured to calculate a condition state label using the condition state model. The computing device is configured to select a treatment model utilizing the condition state label. The computing device is configured to generate a treatment model and output a plurality of treatments utilizing the treatment model.