AI-Driven East Asian Medicine Patient Analytics
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Solution Overview
Problem
The dynamic and multivariate nature of factors in East Asian Medicine makes it challenging for practitioners to provide accurate and timely diagnostics and recommendations, relying heavily on manual calculations and experience.
Innovation Solution
A system and method utilizing Artificial Intelligence/Machine Learning to collect and process patient and environmental data, applying East Asian Medicine principles to provide analytics, diagnostics, and recommendations through convolutional neural networks and wearable sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If practitioners rely on manual calculations and experience-based diagnosis, then diagnostic accuracy can be maintained through expert knowledge, but diagnostic efficiency and timeliness deteriorate due to the complex and dynamic nature of multivariate factors
Solution Approach 1:
The patent replaces manual mechanical calculations and human expert analysis with an automated information handling system that uses machine learning algorithms and neural networks to process diagnostic data, thereby improving efficiency while managing complexity through computational automation
Solution Approach 2:
The system performs self-learning through machine learning algorithms that automatically analyze patterns in patient data and environmental factors, enabling the system to improve diagnostic accuracy over time without requiring manual reprogramming or expert intervention for each case
2Measurement precision
If practitioners consider all related factors and determine relative environment and time, then diagnostic accuracy improves, but time consumption increases due to the need to process multiple dynamic variables
Solution Approach 1:
The system collects and pre-processes environmental data and patient information in advance, preparing datasets before diagnostic needs arise. This preliminary data gathering and organization enables rapid analysis when diagnostic decisions are required, improving both accuracy and speed
Solution Approach 2:
The machine learning system continuously learns from diagnostic outcomes and adjusts its algorithms based on feedback from practitioner decisions and patient outcomes, improving diagnostic accuracy over time while maintaining efficient processing through optimized algorithms
3Productivity
If manual data collection and processing is used, then data accuracy can be maintained through practitioner expertise, but productivity deteriorates due to the labor-intensive nature of processing multivariate patient data
Solution Approach 1:
The information handling system is designed to handle multiple types of data simultaneously - patient symptoms, environmental factors, temporal variations, and diagnostic criteria - through a unified processing platform that can analyze all these diverse data types together, improving both efficiency and completeness
Data Source
AI summary
A system, method, and computer-readable medium are disclosed that provide patient analytics based East Asian Medicine (EAM) or Oriental Medicine (OM) or principles. Data specific to a patient along with environmental data related to the patient are collected. The collected data is processed using EAM principles through Artificial Intelligence/Machine Learning (AI/ML). The collected data is processed with EAM principles and output form AI/ML to provide patient health status over different time intervals. Diagnostics and recommendations are provided based on the AI/ML and symptoms checking.