Machine-Learning Date Extraction from Unstructured Medical Records
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Solution Overview
Problem
Extracting key dates associated with patient diagnosis and treatment from large volumes of unstructured medical records is inefficient and impractical using conventional techniques, as it involves manual processing of vast amounts of unstructured data, making it difficult to identify precise dates for events like diagnosis or treatment initiation and completion.
Innovation Solution
A model-assisted system using a machine learning model to analyze unstructured medical data, identify dates, and determine their association with patient events, enabling automated extraction of key dates from electronic medical records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction of dates from unstructured medical records is performed, then accuracy in identifying key dates can be maintained through human judgment, but productivity becomes extremely low due to the vast volume of data requiring review
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing models and date extraction algorithms that act as a mediator between unstructured medical records and structured date data. This intermediary automatically processes documents, identifies relevant dates, and structures them for research use, thereby resolving the contradiction between maintaining accuracy and improving productivity.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated computational system using machine learning models and text processing algorithms. This substitution eliminates manual labor while maintaining date extraction accuracy through trained models that can process vast volumes of unstructured medical records efficiently.
2Productivity
If automated date extraction systems are implemented, then productivity increases through rapid processing of large datasets, but measurement precision may deteriorate due to difficulty in understanding ambiguous medical notes
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on annotated medical records and common date expression patterns before deployment. This preliminary training enables the automated system to understand ambiguous medical terminology and context, thereby maintaining high accuracy while achieving rapid processing speeds on large datasets.
Solution Approach 2:
The system incorporates feedback mechanisms where extraction results are validated against known patterns and can be refined through iterative processing. The model learns from feedback on ambiguous cases, improving its ability to accurately interpret medical notes while maintaining high productivity across the entire dataset.
3Reliability
If comprehensive review of all medical records is performed to ensure complete date capture, then reliability of extracted dates improves, but loss of time increases due to the sheer volume of documents to search
Solution Approach 1:
The patent segments the comprehensive review process into modular components: document ingestion, text preprocessing, date pattern identification, context validation, and result aggregation. This segmentation allows parallel processing of multiple documents simultaneously, maintaining complete date capture while dramatically reducing total processing time through efficient resource utilization.
Solution Approach 2:
The system implements continuous processing pipelines that handle medical records in streaming fashion rather than batch review. This continuity enables the system to process vast volumes of documents without interruption, maintaining reliability through complete coverage while minimizing time loss through optimized data flow and parallel computation.
Data Source
AI summary
A model-assisted system for processing data to extract a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of dates represented in at least one document included in the medical record; identify a plurality of snippets of information included in the at least one document, each snippet of the plurality of snippets being associated with a date of the plurality of dates; inputting the plurality of snippets into a machine learning model, the machine learning model having been trained to determine associations between dates and patient events based on a training set of snippet data; and determine whether each date of the plurality of dates is associated with a patient event based on an output of the machine learning model.