Extraction of Clinical Data from Electronic Health Records using Regular Expression (original) (raw)

Abstract

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The paper presents an automated method for extracting clinical data from Electronic Health Records (EHR) using regular expressions in Python. The method achieved high precision and sensitivity for structured data extraction compared to traditional methods. It highlights the challenges faced in managing unstructured clinical data and advocates for the use of advanced techniques like regex for efficient data extraction to support patient care and research.

Key takeaways

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  1. The automated data extraction achieved 95% accuracy and 86% precision for structured data.
  2. Extracted data can support secondary applications like disease prediction and prognosis.
  3. The study processed data from 38,300,000 records from a prior big data extraction study.
  4. The sensitivity for unstructured data extraction was measured at 85%.
  5. The text mining applications provide accessible data formats for over 38,000 patients.

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