AI Treatment Plan Engine for Privacy-Preserving Rehabilitation
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Solution Overview
Problem
Challenges exist in efficiently determining and adapting treatment plans for patients undergoing rehabilitation or exercise, particularly in telemedicine settings, due to the complexity of personal, performance, and measurement data, as well as the need to protect personal healthcare information and remotely monitor patient progress while ensuring privacy.
Innovation Solution
An artificial intelligence engine utilizing machine learning to analyze patient data, generate personalized treatment plans, and control exercise apparatuses remotely, while ensuring data privacy through anonymization and pseudonymization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If patient data is collected and analyzed in real-time to personalize treatment plans, then treatment effectiveness and adaptability are improved, but data privacy and security risks increase
Solution Approach 1:
The patent introduces de-identification intermediaries that act as mediators between raw patient data and the machine learning model. These intermediaries strip identifying information while preserving clinically relevant data, allowing the system to analyze patient responses and personalize treatment without exposing sensitive personal information. This resolves the contradiction by enabling adaptability through data analysis while protecting privacy through the intermediary de-identification process.
2Reliability
If machine learning models are trained on diverse patient cohorts to improve generalization, then model robustness increases, but data processing complexity and computational resources increase
Solution Approach 1:
The patent segments the patient population into distinct cohorts based on de-identified characteristics and treatment responses. By dividing the diverse patient population into manageable segments, the system can train more robust models on each cohort while reducing the overall computational complexity compared to processing all diverse data uniformly. This segmentation approach maintains model robustness through cohort-specific learning while managing processing complexity.
3Productivity
If treatment plans are dynamically adjusted based on real-time patient feedback, then patient outcomes improve, but system response time and computational load increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on de-identified historical patient data before deployment. This preliminary training phase allows the models to learn from diverse cohorts in advance, so that during real-time treatment, the models can quickly process new patient feedback and generate personalized treatment adjustments without requiring extensive computational time. This resolves the contradiction by improving recovery efficiency through real-time adaptation while minimizing system response time through advance model preparation.
Data Source
AI summary
The present disclosure provides a method for performing a treatment plan, wherein the method comprises: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan; receiving second patient data, wherein the second patient data includes a second patient identifier associated with the second patient and a second treatment plan; receiving first measurement data associated with a first performance level of the first treatment plan by the first patient; receiving second measurement data associated with a second performance level of the second treatment plan by the second patient; determining differential data, wherein the determining is based on a contrast of the first or the second measurement data or first or second patient data; and generating, based on the differential data, an instruction to modify an operating state of the treatment apparatus.