Machine Learning Patient Data Extraction
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Solution Overview
Problem
Current healthcare systems face challenges in processing large volumes of unstructured data from electronic medical records to identify patient conditions and associated dates, making it difficult to analyze statistically significant cohorts, especially for rare conditions, and limiting the efficacy of data extraction.
Innovation Solution
A deep learning model-based system that processes unstructured medical records to determine patient conditions and their association with specific dates, using two machine learning models to identify conditions and date correlations, enabling the generation of large cohorts for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data extraction by human reviewers is used, then data accuracy may be maintained, but the data set size becomes limited and processing capacity is insufficient for large populations
Solution Approach 1:
The patent replaces manual human review with an automated machine learning system that uses natural language processing to extract data from unstructured medical records. This substitution enables processing of large populations (millions of patients) while maintaining consistent extraction standards, resolving the contradiction between data accuracy and data set size.
2Productivity
If computer-based extraction is applied to large datasets, then processing capacity increases, but difficulty in extracting relevant information from unstructured notes increases
Solution Approach 1:
The patent employs machine learning models with natural language processing capabilities to automatically interpret and extract relevant information from unstructured medical notes. This intelligent system can understand clinical context, identify relevant entities, and extract meaningful data points, thereby maintaining high processing capacity while managing the complexity of unstructured data extraction.
3Measurement precision
If manual review is used for rare conditions, then data quality may be maintained, but the sample size becomes too small for statistical analysis
Solution Approach 1:
The automated machine learning system enables processing of extremely large patient populations, including those with rare conditions. By automatically analyzing millions of records, the system can identify sufficient sample sizes for rare conditions while maintaining consistent extraction quality, enabling statistically meaningful analysis that would be impossible with manual review.
4Speed
If automated extraction is implemented, then processing speed increases, but reliability of extracting accurate information from unstructured data decreases
Solution Approach 1:
The patent uses trained machine learning models that have been optimized for medical data extraction. These models incorporate domain-specific knowledge and can be trained on labeled data to improve accuracy. The system maintains reliability through consistent application of extraction rules, validation mechanisms, and the ability to handle unstructured data in a standardized manner, while achieving high processing speeds through automation.
Data Source
AI summary
A model-assisted system for extracting patient information. A processor may be programmed to access a database storing one or more medical records associated with a patient and determine, using a first machine learning model and based on unstructured information included in the one or more medical records, whether the patient is associated with a condition. The processor may further be programmed to identify a date associated with the patient and determine, using a second machine learning model and based on the unstructured information, whether the patient is associated with the condition relative to the date. The processor may generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.