Complete Blood Count Anomaly Detection With Autoencoder Encoding
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Solution Overview
Problem
Current methods for analyzing complete blood count (CBC) data are inefficient, as they primarily utilize summary level measurements, discarding rich and raw data, which limits the ability to provide a comprehensive picture of an individual's health status and fails to leverage machine learning for automated anomaly detection.
Innovation Solution
A system and method utilizing machine learning to preprocess and encode CBC data using autoencoders, followed by classification with algorithms like XGBoost or Random Forest, to detect anomalies and identify biological traits and signatures, enabling efficient utilization of both raw and rich data for health and ill-health assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If summary level measurements are used for CBC data analysis, then the analysis process is simple and quick, but the ability to provide a comprehensive picture of health status is limited and rich data is discarded
Solution Approach 1:
The patent applies preliminary action by pre-processing and encoding the rich CBC data using autoencoders before the actual anomaly detection. The autoencoder learns to compress the high-dimensional raw data into a lower-dimensional representation that captures essential health information, preparing the data in advance for efficient analysis while preserving valuable information that would otherwise be discarded
Solution Approach 2:
The patent extracts the essential information from the rich CBC data through the autoencoder compression process. By taking out the critical patterns and features from the high-dimensional data and representing them in a condensed form, the system maintains the comprehensive health picture while reducing data complexity for subsequent analysis
2Measurement precision
If machine learning models are applied to detect anomalies in CBC data, then diagnostic accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training the autoencoder model offline to compress CBC data into a lower-dimensional space. This pre-processing step simplifies the subsequent anomaly detection process, as the complex high-dimensional data is transformed into a more manageable representation that requires less computational resources for classification
Solution Approach 2:
The patent segments the anomaly detection system into two distinct components: an autoencoder for data compression and representation learning, and a classifier for anomaly detection. This segmentation allows each component to specialize in specific tasks, with the autoencoder handling the complex data transformation and the classifier focusing on pattern recognition, thereby reducing overall computational complexity
3Loss of information
If raw and rich CBC data are utilized for analysis, then a comprehensive health picture is achieved, but data processing time and storage requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-compressing the rich CBC data using the autoencoder before it is needed for analysis. This pre-processing transforms the large volume of raw data into a compact representation that maintains comprehensive health information while significantly reducing storage requirements and processing time for subsequent anomaly detection tasks
Data Source
AI summary
Herein disclosed is a method of preparing a model to detect health and ill-health related characteristics in complete blood counts (CBC) data. The method comprises receiving CBC data from one or more data sources, where the CBC data comprise raw and rich data; encoding CBC data using one or more machine-learning algorithms; training classifier for biological traits based on the encoded CBC data, where the biological traits comprise disease phenotypes; and outputting the model comprising the trained classifier.