EHR System with Gaussian Mixture Model for Confidence-Based Inference
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Solution Overview
Problem
Current Electronic Health Record (EHR) systems lack integration with Natural Language Processing (NLP) systems, limiting their ability to provide confidence-based inferences from both text and multimedia data, and fail to seamlessly retrieve missing data through conversational interfaces.
Innovation Solution
A system that combines EHR data with NLP capabilities using a Gaussian mixture model to calculate conditional probability tables, enabling a conversational interface to query users for additional information and provide confidence-based inferences through a probabilistic inference reasoner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If EHR systems store standardized patient data for future recovery, then data accessibility and instantaneous retrieval are improved, but the system cannot provide confidence-based inferences or integrate with NLP systems
Solution Approach 1:
The patent combines EHR data storage capabilities with NLP processing and probabilistic inference systems into an integrated architecture. The EHR system is merged with a reasoning engine that processes natural language queries and generates confidence-based inferences, allowing the system to simultaneously maintain data accessibility and provide advanced analytical capabilities.
Solution Approach 2:
The system is designed to perform multiple functions: storing standardized EHR data, processing natural language queries through NLP, calculating probability density functions, and generating confidence-based inferences. This multi-functional architecture allows a single system to handle both data retrieval and complex medical reasoning tasks.
2Ease of operation
If NLP systems are used to communicate with humans and answer natural-language questions, then user interaction is improved, but the system lacks integration with EHRs and cannot access medical records
Solution Approach 1:
The patent introduces an intermediary layer that connects NLP systems with EHR databases. This intermediary component translates natural language queries into structured data requests, retrieves relevant information from EHRs, and feeds it back to the NLP system for generating appropriate responses, thereby enabling reliable integration between conversational interfaces and medical record systems.
3Measurement precision
If probability density functions are calculated and combined using Gaussian mixture models, then inference accuracy is improved, but computational complexity increases
Solution Approach 1:
The system pre-calculates and stores probability density functions for various medical parameters and conditions during system initialization or data update cycles. This preliminary computation allows the inference engine to quickly retrieve and combine pre-computed probability distributions using Gaussian mixture models during clinical queries, reducing real-time computational complexity while maintaining high inference accuracy.
Data Source
AI summary
Various embodiments provide systems, computer program products and computer implemented methods. In some embodiments, a system includes a method of providing a confidence-estimation-based inference, the method includes receiving a query concerning a patient from a user, accessing an electronic health record (EHR) for the patient, the EHR including a first component regarding the patient, querying the user, using a conversational interface, for a second component regarding the patient, receiving the second component regarding the patient in response to the query, calculating a first probability density function using the first component, and a second probability density function using the second component, combining the first and second probability density functions using a Gaussian mixture model, calculating at least one conditional probability table using the Gaussian mixture model and providing the confidence-estimation-based inference based on the at least one conditional probability table.