NLP Pipeline for Secure Medical Dashboard Data Structuring
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Solution Overview
Problem
Current systems fail to accurately, securely, and efficiently generate structured data from unstructured medical documents, such as patient records and reports, for use in secure medical dashboards, particularly in identifying relevant patient data without violating confidentiality and optimizing data aggregation for various medical needs.
Innovation Solution
A system utilizing natural language processing (NLP) pipelines to parse unstructured medical data, identify medical relevance attributes, and generate structured documents, which are then used to create a secure medical dashboard that dynamically adjusts based on user requirements, reducing manual input and computational resources while ensuring data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reading and tagging of medical documents is performed, then data accuracy is improved, but productivity deteriorates due to the time-consuming nature of manual processing
Solution Approach 1:
The NLP pipeline automatically parses, tags, and structures medical documents without requiring manual human intervention for each document, enabling the system to self-process large volumes of unstructured data while maintaining consistent accuracy standards
Solution Approach 2:
The patent replaces the mechanical manual tagging process with an automated NLP-based system that uses natural language processing algorithms to extract, tag, and structure medical information from unstructured documents, thereby increasing processing speed while maintaining data accuracy
2Adaptability or versatility
If comprehensive patient data is aggregated for various medical needs, then adaptability is improved, but data security deteriorates due to potential confidentiality violations
Solution Approach 1:
The system applies different security and access control properties to different portions of patient data based on user roles and specific medical needs, allowing comprehensive data aggregation while protecting sensitive information through localized security measures tailored to each data element's sensitivity level
Solution Approach 2:
The NLP pipeline acts as an intermediary that processes and structures patient data before making it available to users, enabling adaptability for various medical needs while maintaining security by controlling what information is extracted, how it is formatted, and who can access it through the structured dashboard interface
3Measurement precision
If NLP pipelines are trained with pre-labeled data, then measurement precision is improved, but loss of time increases due to the training process
Solution Approach 1:
The system performs preliminary action by training the NLP pipeline with pre-labeled medical data in advance, creating a pre-trained model that can then rapidly and accurately process new unstructured medical documents without requiring re-training, thus achieving high tagging accuracy while minimizing time loss during actual operation
Data Source
AI summary
Systems, apparatuses, and methods are described herein for generating structured data from unstructured data using natural language processing to generate a secure medical dashboard. The present invention is configured to identify at least one data input, wherein the at least one data input comprises unstructured data; apply at least one NLP pipeline to the at least one data input; parse the unstructured data to generate a parsed unstructured dataset; identify a medical relevance attribute; generate a structured document comprising the at least one term and associated medical relevance attribute; correlate the at least one term comprising the positive medical attribute to a medical entity title; generate a medical dashboard interface component comprising the at least one term comprising the positive medical attribute and the medical entity title; transmit the medical dashboard interface component to a user device; and generate a secure medical dashboard on the user device.