Healthc Inform Res.  2018 Apr;24(2):148-153. 10.4258/hir.2018.24.2.148.

HEDEA: A Python Tool for Extracting and Analysing Semi-structured Information from Medical Records

• Aggarwal A
• Garhwal S
• Kumar A

• KMID: 2418183
• DOI: http://doi.org/10.4258/hir.2018.24.2.148

Abstract

OBJECTIVES
One of the most important functions for a medical practitioner while treating a patient is to study the patient's complete medical history by going through all records, from test results to doctor's notes. With the increasing use of technology in medicine, these records are mostly digital, alleviating the problem of looking through a stack of papers, which are easily misplaced, but some of these are in an unstructured form. Large parts of clinical reports are in written text form and are tedious to use directly without appropriate pre-processing. In medical research, such health records may be a good, convenient source of medical data; however, lack of structure means that the data is unfit for statistical evaluation. In this paper, we introduce a system to extract, store, retrieve, and analyse information from health records, with a focus on the Indian healthcare scene.
METHODS
A Python-based tool, Healthcare Data Extraction and Analysis (HEDEA), has been designed to extract structured information from various medical records using a regular expression-based approach.
RESULTS
The HEDEA system is working, covering a large set of formats, to extract and analyse health information.
CONCLUSIONS
This tool can be used to generate analysis report and charts using the central database. This information is only provided after prior approval has been received from the patient for medical research purposes.

Keyword

Medical Records; Information Storage and Retrieval; Data Collection; Metadata; Medical Report; Regular Expression

MeSH Terms

Boidae*
Data Collection
Delivery of Health Care
Humans
Information Storage and Retrieval
Medical Records*

Figure

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  Figure 1 System architecture diagram.

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  Figure 2 Unstructured extraction using regular expressions and distance scoring.

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  Figure 3 Sample output.

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  Figure 4 Blood sugar levels of a patient.

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  Figure 5 Body mass index (BMI) of patients in the comprehensive analytics database.

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