Personalized Sensory Treatment Apparatus Using Data Mining Recovery Prediction
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Solution Overview
Problem
Conventional treatment apparatuses for sensory organs do not optimize treatment parameters based on individual sensory data, leading to ineffective treatment outcomes for patients with sensory defects, as they treat all patients similarly without considering personalized sensory data or feedback.
Innovation Solution
An apparatus that uses a data mining algorithm, such as a Self-Organizing Map, to calculate recovery prediction data from both the target patient's sensory data and a reference group's data, allowing for the adjustment of treatment parameters like stimulation signal intensity and frequency to optimize treatment outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional treatment apparatuses treat all patients with the same treatment parameters, then the treatment process is simple and easy to operate, but the treatment effectiveness is reduced because individual sensory defects are not considered
Solution Approach 1:
The patent applies local quality by customizing treatment parameters according to each patient's specific sensory defect characteristics. The system analyzes individual sensory data and adjusts stimulation parameters (intensity, frequency, duration) specifically for each patient's defect profile, rather than using uniform treatment for all patients. This ensures treatment effectiveness is optimized for each individual's unique sensory impairment.
Solution Approach 2:
The patent implements preliminary action by collecting and analyzing sensory data from reference groups before treating individual patients. The system pre-processes sensory data, identifies patterns and correlations, and prepares optimized treatment parameter sets in advance. This preliminary analysis of reference group data enables faster and more accurate treatment customization for each patient without requiring complex real-time adjustments during treatment.
2Reliability
If treatment parameters are adjusted based on individual sensory data and reference group data, then treatment outcomes are optimized, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent applies copying by creating a virtual model or representation of the patient's sensory defect based on their sensory data and comparing it with patterns from reference groups. The system copies successful treatment parameter configurations from reference cases that match the current patient's defect profile, rather than developing entirely new treatment parameters from scratch. This reduces computational complexity while maintaining high prediction accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where treatment outcomes are continuously monitored and fed back into the data mining algorithm. The system uses the actual treatment responses to refine and update the treatment parameter recommendations for future sessions. This feedback loop enables the system to learn from each patient's response and improve prediction accuracy over time without requiring increasingly complex algorithms.
3Adaptability or versatility
If sensory data from both target person and reference group are analyzed, then personalized treatment optimization is achieved, but the amount of data to be processed increases
Solution Approach 1:
The patent applies extraction by selectively extracting only the most relevant features and parameters from the large volumes of sensory data from both target patients and reference groups. Rather than processing all raw sensory data, the system identifies and extracts key discriminative features that are most predictive of treatment outcomes. This dimensionality reduction maintains personalization capability while significantly reducing the computational burden of data processing.
Solution Approach 2:
The patent implements segmentation by dividing the reference group data into distinct clusters or categories based on similarity of sensory defect profiles. Instead of treating all reference group data uniformly, the system segments the data into relevant subgroups and only compares the target patient with the most similar segments. This segmentation strategy reduces the effective data volume that needs to be processed while preserving the ability to find relevant personalized treatment patterns.
Data Source
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Figure 3A~3B
AI summary
The present invention relates to an apparatus for automatic adjustment of a treatment of a target person to be treated based on calculated recovery prediction data for predicting a change of a sensory function of said target person in response to said treatment.