End-to-End ML Framework for Reducing Computational Operations in Predictive Diagnosis
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Solution Overview
Problem
Existing predictive data analysis solutions face inefficiencies and reliability issues in generating recommendation scores, often requiring computationally expensive operations and lacking in computational efficiency.
Innovation Solution
An end-to-end machine learning framework that utilizes a diagnosis prediction model to generate a probabilistic diagnosis data object, which is then processed by a hybrid diagnosis-provider classification model to produce a variable-length classification, ultimately generating a consultation recommendation score using a recommendation scoring model, thereby reducing computational operations and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional predictive data analysis solutions are used to generate recommendation scores, then comprehensive analysis can be achieved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the recommendation scoring process into multiple specialized machine learning models, each handling specific aspects of the analysis. This division allows parallel processing and reduces the computational burden on any single model, thereby improving efficiency while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent implements preliminary action by pre-processing input data and pre-training specialized models on relevant datasets before actual recommendation scoring. This preparation work reduces the computational complexity during runtime, enabling faster generation of recommendation scores without sacrificing accuracy.
2Reliability
If complex machine learning frameworks are implemented to improve prediction accuracy, then reliability of diagnosis increases, but system complexity and computational resources required increase
Solution Approach 1:
The complex machine learning framework is segmented into multiple specialized models, each responsible for specific diagnostic tasks. This modular architecture maintains high prediction accuracy through specialized processing while reducing overall system complexity by separating concerns and enabling independent optimization of each component.
Solution Approach 2:
The patent introduces intermediary processing layers and data structures that facilitate communication between different model components. These intermediaries simplify the integration of complex models by providing standardized interfaces and abstractions, thereby managing system complexity while preserving diagnostic accuracy.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by an end-to-end machine learning framework that performs at least the following steps/operations: (i) a service request data object is processed by a diagnosis prediction machine learning model to generate a probabilistic diagnosis data object, (ii) the probabilistic diagnosis data object is processed by the hybrid diagnosis-provider classification machine learning model to generate a variable-length classification for the service request data object, and (iii) the variable-length classification is processed by a recommendation scoring machine learning model to generate a consultation recommendation score for the service request data object.