Diagnostic Report Generation Using Contextual Data Extraction
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Solution Overview
Problem
Current machine-learning models face limitations in data management, as they have constraints on the amount of input data they can process effectively, leading to inefficiencies in their use.
Innovation Solution
An apparatus and method for generating a diagnostic report using a processor and memory, which involves receiving a user profile, extracting contextual data, generating inquiries using an inquiry machine learning model, receiving inquiry responses, and producing a diagnostic report based on these inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If machine-learning models receive large amounts of input data, then the completeness of diagnostic information is improved, but the efficiency of model usage deteriorates due to processing constraints
Solution Approach 1:
The patent extracts only the most relevant contextual data from user profiles using NLP techniques and predefined contextual categories. Instead of feeding all available user data into the machine-learning model, the system selectively extracts pertinent information such as demographics, medical history, and current symptoms, thereby reducing input data volume while maintaining diagnostic quality.
Solution Approach 2:
The patent segments user profile data into distinct contextual categories (demographics, medical history, symptoms, etc.) and processes each segment separately. This segmentation allows the system to manage large amounts of data efficiently by organizing them into structured formats that the machine-learning model can process more effectively, rather than handling raw unstructured data as a single mass.
2Measurement precision
If contextual data is extracted and processed before generating inquiries, then the relevance of diagnostic questions is improved, but the complexity of the system increases
Solution Approach 1:
The patent performs preliminary extraction and organization of contextual data from user profiles before generating diagnostic inquiries. By pre-processing the user data to identify relevant contextual categories and relationships, the system enables the machine-learning model to generate more targeted and relevant questions without requiring complex real-time processing during the inquiry generation phase.
Solution Approach 2:
The patent introduces an intermediary processing layer that uses NLP techniques to extract and structure contextual data from unstructured user profiles. This intermediary layer translates raw user information into a structured format with identified contextual relationships, which then serves as refined input for the inquiry generation model, simplifying the overall system architecture.
Data Source
AI summary
An apparatus for generating a diagnostic report is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a user profile from a user. The memory instructs the processor to generate a first set of inquiries as a function of the user profile using an inquiry machine learning model. The memory instructs the processor to receive a first set of inquiry responses from the user as a function of the first set of inquiries. The memory instructs the processor to generate a diagnostic report as a function of the first set of inquiries and the first set of inquiry responses. The memory instructs the processor to display the diagnostic report using a display device.