Machine Learning Clinical Data Extraction Engine
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Solution Overview
Problem
Laboratory information systems face challenges in processing unstructured clinical data due to inconsistencies in content and format across different medical devices, clinical facilities, and automation platforms, leading to inefficiencies and delays in clinical workflows.
Innovation Solution
A machine learning-based extraction engine that identifies and structures clinical data by applying trained models to recognize entities and patterns within unstructured data, enabling the control of medical devices and improving data interoperability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used, then system complexity is low, but data processing speed and efficiency are insufficient
Solution Approach 1:
The patent replaces traditional mechanical data processing systems with machine learning-based automated extraction systems. The machine learning model automatically identifies and extracts clinically significant data from unstructured clinical data, eliminating the need for complex manual processing rules and significantly improving processing speed while managing system complexity through intelligent automation.
Solution Approach 2:
The machine learning model performs self-learning and automatic data extraction without requiring complex manual configuration or intervention. The system automatically adapts to different data formats and structures, processing data independently and efficiently, which improves productivity while keeping the operational complexity manageable through autonomous operation.
2Loss of time
If manual data extraction is used, then system complexity is low, but time consumption increases
Solution Approach 1:
The patent substitutes manual data extraction processes with automated machine learning-based extraction. The machine learning model automatically identifies and extracts clinically significant data from unstructured clinical data, dramatically reducing extraction time while implementing a high level of automation that eliminates manual intervention in the data processing workflow.
3Reliability
If unstructured data is processed directly, then data format flexibility is high, but data consistency and reliability are poor
Solution Approach 1:
The patent replaces direct processing of unstructured data with machine learning-based extraction and transformation. The machine learning model processes unstructured clinical data, identifies patterns, and extracts consistently formatted clinically significant data, improving data consistency and reliability while managing processing complexity through intelligent pattern recognition and automated standardization.
Data Source
AI summary
A method may include receiving, from one or more data systems, a message including unstructured clinical data. A machine learning model may be applied to identify a first entity and a second entity present in the unstructured clinical data. The first entity and the second entity may occupy a same row or successive rows in a same column of the unstructured clinical data. The machine learning model may be trained to determine, based at least on the unstructured clinical data including the first entity, that the second entity is a most likely entity occupying a next position in the same row or a next row in the same column of the unstructured clinical data. Clinically significant data may be extracted from the structured clinical data. At least one medical device may be controlled, based at least on the clinically significant data, to perform one or more tasks.