Machine Learning Query Construction for ALS Diagnosis
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Solution Overview
Problem
Current methods are inefficient in identifying rare medical conditions like ALS due to limitations in processing large volumes of data and lack of knowledge about relevant parameters, leading to delayed diagnosis and ineffective treatment.
Innovation Solution
A machine-learning based method that continually obtains and processes data sets from distributed environments to identify optimal features for classifying patients, generating models in real-time to predict the presence or progression of medical conditions, and applying these models to undiagnosed populations for early detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If passive surveillance relying on existing medical records is used, then cost is reduced, but diagnostic accuracy and reliability deteriorate due to incomplete and unreliable data
Solution Approach 1:
The patent introduces an intermediary layer of machine learning algorithms and predictive analytics that process and enhance existing medical records. This intermediary system bridges the gap between passive surveillance cost-effectiveness and active surveillance diagnostic accuracy by automatically identifying patterns and filling data gaps in existing records.
Solution Approach 2:
The patent replaces manual active surveillance methods (questionnaires and tests) with automated machine learning-based passive surveillance. This substitution maintains cost-effectiveness while improving diagnostic accuracy through advanced computational analysis of existing medical records.
2Reliability
If active surveillance with questionnaires and tests is used, then diagnostic accuracy is improved, but cost and practicality deteriorate
Solution Approach 1:
The patent replaces expensive and impractical active surveillance methods with automated machine learning algorithms that analyze existing medical records. This substitution dramatically reduces cost and resource requirements while maintaining or improving diagnostic accuracy through advanced pattern recognition.
Solution Approach 2:
The system enables self-service diagnostic identification by automatically processing and analyzing medical records without requiring manual intervention, questionnaires, or additional testing. The machine learning models autonomously identify potential ALS cases from existing data.
3Measurement precision
If machine learning algorithms are applied to large volumes of data, then pattern identification capability is improved, but computational complexity and processing time worsen
Solution Approach 1:
The patent segments the computational process into distinct stages: data preprocessing, feature extraction, model training, and prediction. This segmentation reduces computational complexity by breaking down the large-scale problem into manageable sub-tasks that can be processed efficiently.
Solution Approach 2:
The patent performs preliminary actions by pre-processing medical records and extracting relevant features before applying machine learning algorithms. This preliminary processing reduces the dimensionality and complexity of the data, making subsequent pattern identification more efficient and less computationally intensive.
4Measurement precision
If comprehensive parameter searching is performed in large data volumes, then diagnostic completeness is improved, but processing efficiency and time worsen
Solution Approach 1:
The patent extracts only the most relevant features and parameters from comprehensive medical records using feature extraction techniques. This extraction process maintains diagnostic completeness by identifying the most predictive variables while eliminating redundant information that would slow processing.
Solution Approach 2:
The patent performs preliminary feature extraction and data filtering before applying diagnostic algorithms. This preliminary action prepares the data in advance, enabling faster and more efficient processing while maintaining comprehensive diagnostic coverage of relevant parameters.
Data Source
AI summary
A method, computer program product, and system continually obtain machine-readable data sets related to a patient population diagnosed with a medical condition from one or more databases (different computing nodes in the distributed environment). The processor(s) continually applies a recurrent neural network to the plurality of data sets to machine learn optimal features for classifying patients into multiple categories related to presence or progression of the medical condition. The processor(s) continually generates, based on the machine learned optimal set of features, intermediate features, based on the weightings of a portion of the machine learned optimal set of features, i.e., a model. The processor(s) evaluates the records and classifies records into the categories, based on a current model (model generated in real-time).