Cognitive Inference System for Healthcare Dark Data Extraction
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and analyzing large volumes of big data, particularly 'dark data' from diverse sources, which is often neglected or underutilized, hindering organizations and individuals from extracting actionable insights.
Innovation Solution
A cognitive inference and learning system (CILS) that processes data from various sources, including healthcare data, to provide optimized insights through semantic analysis, goal optimization, collaborative filtering, common sense reasoning, natural language processing, and entity resolution, generating cognitive graphs for intelligent data retrieval and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then data processing can be performed with simple tools, but processing efficiency is insufficient and cannot handle large volumes of data within tolerable time intervals
Solution Approach 1:
The patent segments the data processing task into multiple distributed worker nodes that process data in parallel. Each worker handles a portion of the big data, allowing the system to scale processing capacity by adding more nodes without increasing the complexity of individual processing units.
Solution Approach 2:
The patent introduces a MapReduce framework as an intermediary layer between data storage and analysis. The Map phase transforms and filters data, while the Reduce phase aggregates results, enabling efficient processing of large datasets without requiring complex custom processing logic.
2Loss of information
If dark data from diverse sources is collected and processed, then more actionable insights can be extracted, but data curation, storage, search, sharing, and analysis become increasingly difficult
Solution Approach 1:
The patent implements a universal data lake architecture that can store and process diverse data types (structured, unstructured, semi-structured) from multiple sources using a single platform. This multi-functional system handles curation, storage, search, sharing, and analysis through integrated tools, reducing the complexity that would arise from managing separate systems for each function.
Solution Approach 2:
The patent creates standardized data copies and representations (such as data schemas, metadata models, and processed views) that simplify access to complex dark data. These copies enable efficient searching and analysis without requiring direct manipulation of the raw diverse data sources.
3Measurement precision
If cognitive inference and learning operations are implemented to process healthcare data, then optimized cognitive insights can be provided, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data processing, filtering, and transformation operations before applying complex cognitive inference and learning algorithms. By pre-processing the healthcare data to extract relevant features and reduce dimensionality, the system prepares the data in advance, which reduces the computational burden and processing time of subsequent cognitive operations while maintaining insight quality.
Data Source
AI summary
A method for providing healthcare optimized cognitive insights comprising: receiving data from a plurality of data sources, at least some of the plurality of data sources comprising healthcare relevant data sources; processing the data from the plurality of data sources to provide cognitively processed insights; performing a learning operation to iteratively improve the cognitively processed insights over time; and, providing the cognitively processed healthcare relevant insights to a destination.