Cloud Health Warning Model for Incomplete Physiological Data
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Solution Overview
Problem
Conventional medical care systems rely heavily on human interpretation of limited physiological data from sensors, lacking comprehensive determination of physiological status and effective warning mechanisms, especially for home care, which leads to inefficiencies and high caregiver burden.
Innovation Solution
A health care prediction and warning system utilizing a central server that integrates continuous and non-continuous physiological and environmental data through machine learning to establish a care-taking warning prediction model, enabling accurate prediction and warning of physiological events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If physiological sensors are worn on the care recipient to continuously monitor physiological information, then the care center can obtain physiological data at all times, but the sensors can only provide limited physiological information that requires independent interpretation by physicians
Solution Approach 1:
The patent segments the health care system into multiple components: terminal physiological sensors for data collection, a cloud-based server for comprehensive analysis integrating multiple data sources (medical records, environmental factors, physiological data), and a warning system for specific condition alerts. This segmentation allows each component to have specialized functions, improving overall information completeness without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces an intermediary AI-based analysis system that acts as a mediator between the simple physiological sensors and the complex medical decision-making process. This intermediary automatically interprets physiological data, integrates it with other data sources, and generates meaningful health assessments, reducing the burden on physicians while maintaining information completeness.
2Reliability
If physicians make decisions based on their experiences with limited physiological data, then medical judgment can be applied, but it cannot account for all possibilities and effective warnings for specific situations cannot be generated
Solution Approach 1:
The patent implements preliminary action by pre-programming the system with multiple prediction models for various health conditions (sleep quality assessment, fall risk prediction, disease progression warnings). These models are prepared in advance based on medical knowledge and statistical analysis, enabling the system to automatically generate appropriate warnings for specific situations without requiring real-time physician intervention for every scenario.
Solution Approach 2:
The patent utilizes parameter changes by analyzing trends and patterns in physiological data over time rather than relying on single-point measurements. The system transforms raw physiological parameters into meaningful health indicators through multiple analysis models, enabling more reliable and versatile medical decisions that account for various possibilities.
3Ease of manufacture
If conventional physiological sensors are used for home care, then they are simpler than medical institution equipment, but the physiological data obtained is limited and cannot be used for accurate interpretation of specific conditions
Solution Approach 1:
The patent merges multiple data sources including physiological sensor data, electronic medical records, environmental monitoring data, and patient history into a comprehensive analysis framework. This combination compensates for the limitations of simple home-care sensors by integrating them with other information sources, achieving accurate interpretation of specific conditions without requiring complex medical institution-grade equipment.
Data Source
AI summary
A health care prediction and warning system and an operating method thereof are provided. The system is implemented by a cloud system which operates a care-taking warning prediction model. The cloud system receives physiological data from various terminal devices. After analyzing the physiological data, analyzed result is compared with criteria set by the cloud system for determining whether the physiological data is sufficient to predict physiological status of a care recipient. Under first criterion, a result of predicted physiological status of the care recipient is generated based on the received physiological data; under second criterion, the cloud system requires the terminal device to transmit more data in different time period for effectively predicting the physiological status; and, under third criterion, the cloud system requires the terminal device to provide other sensing data generated by other sensors so as to effectively predict the physiological status.