Personal Health Record System for Clinical Data Extraction
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Solution Overview
Problem
Current medical big data systems face challenges in accurately recommending treatment plans due to reliance on knowledge bases without feedback from large patient data and the complexity of unstructured data, leading to potential misdiagnosis and inefficient decision-making.
Innovation Solution
A personal health record system with a decision support function that extracts clinically significant characteristic data from non-characteristic data using a cloud-based server and client platform, enabling effective data analysis and recommendation through a non-characteristic data processing interface and statistics query module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If computer-aided diagnosis systems use knowledge bases to assist diagnosis, then information integration ability is improved, but the ability to obtain feedback from large amount of patient data is lost
Solution Approach 1:
The system divides the large-scale patient data into structured data (organized in knowledge base) and unstructured data (clinical notes, reports). By segmenting the data processing tasks, the system can maintain the organized structure of the knowledge base while separately processing unstructured data through natural language understanding to extract relevant information and provide feedback.
Solution Approach 2:
The patent introduces an intermediary natural language understanding module that bridges the knowledge base and patient data. This intermediary extracts structured information from unstructured clinical data, enabling the system to utilize both the organized knowledge base and the feedback from actual patient cases without direct conflict between the two data sources.
2Loss of information
If natural language processing technology is used to understand medical data, then knowledge acquisition from massive data is improved, but technology complexity increases
Solution Approach 1:
The natural language processing system is divided into separate functional modules: text preprocessing, entity recognition, relationship extraction, and semantic analysis. Each module handles a specific aspect of language understanding, reducing the complexity of any single component while maintaining overall capability to process medical data effectively.
Solution Approach 2:
The system implements partial natural language processing by focusing on extracting specific structured information (diagnoses, treatments, symptoms) from clinical texts rather than attempting to fully understand all aspects of medical language. This selective approach reduces computational complexity while still achieving the goal of knowledge acquisition from unstructured data.
3Adaptability or versatility
If individual case treatment plans are recommended based on treatment effect score, then personalization is improved, but accuracy decreases due to unrepresentative individual cases
Solution Approach 1:
The system merges multiple data sources including structured knowledge base information, unstructured clinical data, and aggregated treatment outcome data. By combining these diverse sources, the system can provide personalized treatment recommendations that are both individually tailored and grounded in broader statistical evidence, improving accuracy while maintaining personalization.
Solution Approach 2:
The system implements feedback mechanisms where treatment outcomes from individual cases are continuously collected, analyzed, and fed back into the recommendation system. This feedback loop allows the system to learn from actual treatment results and adjust future recommendations, ensuring that personalization is based on representative and validated case data rather than isolated individual cases.
Data Source
AI summary
The present invention discloses a personal health record system with a process decision support function. By constructing a personal health record system with separated characteristic data extraction and characteristic data analysis, characteristic data that meets analysis requirements is expected to be rapidly obtained. A statistics query interface based on characteristic data and time logic is provided, and decision support for users is completed through steps such as characteristic matching, process classification and statistics evaluation, and process recommendation, so as to make medical big data better execute high-level information analysis and decision support functions.