Root Cause Identification System Using Machine Learning
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Solution Overview
Problem
Accurate identification of root causes is challenging due to the vast amount of medical literature and limited time for practitioners to analyze it, making it difficult to determine the underlying cause of symptoms.
Innovation Solution
A system and method using a computing device that receives user input, extracts symptom data, generates queries, trains a machine learning process with expert input training sets correlated to causal link data, assigns weights, and identifies root causes for display to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If practitioners manually analyze medical literature to identify root causes, then accuracy of root cause identification can be improved, but time consumption and workload increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing modules, machine learning models, and knowledge graphs that act as a mediator between medical literature and practitioners. This intermediary automatically processes and analyzes medical literature, extracting relevant information and presenting synthesized results to practitioners, thereby maintaining high accuracy while significantly reducing time consumption.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with automated computational systems including NLP algorithms, machine learning models, and information extraction systems. These computational mechanisms automatically process medical literature, perform causal reasoning, and generate root cause identifications, eliminating the need for manual reading and analysis while preserving or enhancing accuracy.
2Reliability
If practitioners review comprehensive medical literature to ensure thorough analysis, then completeness of root cause identification improves, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically acquiring, processing, and analyzing medical literature without requiring practitioner intervention. The system independently executes information extraction, causal reasoning, and root cause identification tasks, then presents results to practitioners who simply need to review and validate findings, thereby maintaining completeness while dramatically improving ease of operation.
Solution Approach 2:
The system performs preliminary actions by pre-processing and pre-analyzing medical literature before practitioner involvement. The system proactively gathers relevant data, performs initial causal analysis, and prepares synthesized results, so that practitioners receive ready-to-review comprehensive analyses rather than raw literature requiring manual processing.
3Measurement precision
If the system processes extensive medical literature and expert data, then accuracy of root cause identification improves, but device complexity increases
Solution Approach 1:
The patent segments the complex system into distinct functional modules including data acquisition modules, natural language processing modules, machine learning model modules, knowledge graph modules, and result presentation modules. Each module handles specific tasks independently, making the overall complex system manageable through modular architecture while maintaining high accuracy through specialized processing in each segment.
Solution Approach 2:
The system employs universal multi-functional components that can handle various types of medical literature and data formats through standardized processing pipelines. The NLP modules, machine learning models, and knowledge graphs are designed to process diverse inputs (text, structured data, expert knowledge) through unified mechanisms, reducing complexity by avoiding the need for separate specialized systems for each data type.
Data Source
AI summary
A system for identifying root causes, the system including a computing device designed and configured to receive a user input from a user client device, extract at least a symptom datum form the user input, extracting the at least a symptom datum includes being configured to generate at least a query using the user input, and generate the at least a symptom datum as a function of the at least a query, train a machine learning process with an expert input training set from an expert knowledge database wherein the expert input training set further includes prognostic data correlated to causal link data, configured to assign weights to the correlated data as a function of the at least a symptom datum, identify root causes as a function of the assigned weights and display the root causes to the user.