Machine Learning Model for Extracting Key Dates from Unstructured Medical Records
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Solution Overview
Problem
Current methods are inefficient in extracting key dates associated with patient diagnosis and treatment from unstructured medical data, making it difficult for researchers to analyze large populations of patient records, as the information is often ambiguous and scattered across numerous documents.
Innovation Solution
A processor-based system that analyzes unstructured medical records to identify snippets of information, determines associated dates, and generates probabilities for query periods, using machine learning models to automate the extraction of key dates such as diagnosis and treatment dates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction of dates from unstructured medical records is performed, then accuracy of date identification may be maintained, but productivity becomes extremely low and the task becomes infeasible for large populations
Solution Approach 1:
The patent replaces manual mechanical review of medical records with an automated computer-based system that uses natural language processing and machine learning algorithms to extract dates and events from unstructured text, thereby maintaining accuracy while dramatically increasing productivity
Solution Approach 2:
The patent introduces an intermediary processing layer that includes text normalization, entity recognition, and probability calculation components between the raw unstructured data and the final extracted dates, enabling automated systems to achieve human-level accuracy in date identification
2Device complexity
If conventional text search techniques are used to extract key dates, then device complexity remains low, but measurement precision deteriorates due to ambiguous notes scattered across multiple documents
Solution Approach 1:
The patent segments the complex task of date extraction into multiple independent processing stages including text preprocessing, snippet identification, date pattern recognition, and probability calculation, allowing each component to be optimized independently while improving overall accuracy
Solution Approach 2:
The patent performs preliminary text normalization and snippet identification before date extraction, preparing the data in advance to improve the accuracy of subsequent date recognition processes while managing system complexity
3Reliability
If comprehensive review of all medical records is conducted to ensure complete date extraction, then reliability of extracted data improves, but loss of time becomes prohibitive for large datasets
Solution Approach 1:
The patent applies partial action by focusing extraction efforts on the most relevant snippets and documents identified through preliminary analysis, rather than reviewing every document in detail, thereby maintaining high reliability while reducing processing time for large datasets
Data Source
AI summary
A model-assisted system for determining 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 snippets of information in the medical record associated with a patient event; determine a date associated with each of the plurality of snippets; identify a plurality of query periods associated with the patient event; and generate, for each of the query periods, a probability of whether the patient event occurred during the query period based on the plurality of snippets and the associated dates.