Multi-Dimensional Temporal Data Mining for ICU Monitoring
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Solution Overview
Problem
Current data mining environments struggle to effectively analyze and predict time-dependent behavior across multiple data streams, particularly in multi-dimensional environments with complex, temporal data, such as in intensive care units, where manual analysis is impractical due to the large volumes and complexity of physiological monitoring data.
Innovation Solution
A multi-dimensional temporal data mining system that collects and cleans data, applies temporal abstractions, and aligns it relative to specific time points of interest, enabling exploratory and explanatory data mining, and supporting null hypothesis testing through a framework that preserves temporal and contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used for physiological monitoring data, then analysis accuracy can be maintained, but productivity is severely limited due to the large volumes and complexity of data
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational systems. The data mining framework automatically processes multi-dimensional temporal data from multiple monitoring devices, substituting human manual analysis with algorithm-driven processing that maintains accuracy while dramatically improving productivity through automated pattern recognition and trend detection across large datasets
2Productivity
If existing data mining environments are used, then basic data processing can be performed, but they fail to preserve temporal and contextual information in multi-dimensional environments
Solution Approach 1:
The patent extends traditional data mining by adding temporal and contextual dimensions to the analysis framework. It processes data across multiple dimensions including time, patient characteristics, monitoring parameters, and environmental factors, preserving the multi-dimensional nature of physiological data that conventional single-dimension mining approaches cannot maintain
Solution Approach 2:
The patent implements nested data structures where temporal patterns are embedded within contextual frameworks. The system nests multiple levels of abstraction including raw data, temporal patterns, contextual relationships, and high-level insights, allowing preservation of detailed temporal information within broader contextual analyses
3Productivity
If data is aggregated and simplified for analysis, then processing speed improves, but temporal precision and contextual details are lost
Solution Approach 1:
The patent segments temporal data into meaningful intervals and events while preserving the temporal relationships between them. Instead of aggregating data into lossy summaries, it divides the continuous data stream into discrete temporal patterns and events that maintain precision while enabling efficient processing through structured segmentation of the temporal dimension
Data Source
AI summary
The present invention provides a system, method and computer program for multi-dimensional temporal abstraction and data mining. The invention comprises collecting and optionally cleaning multi-dimensional data, the multi-dimensional data including a plurality of data streams; temporally abstracting the multi-dimensional data; and relatively aligning the temporally abstracted multi-dimensional data based on a shared time point of interest.