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Related Concept Videos

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Related Experiment Video

Updated: Jan 20, 2026

Standardization of Basket Use in Sialendoscopy: A Ten-Year Retrospective Study
09:36

Standardization of Basket Use in Sialendoscopy: A Ten-Year Retrospective Study

Published on: June 6, 2025

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Developing a Standardization Algorithm for Categorical Laboratory Tests for Clinical Big Data Research: Retrospective

Mina Kim1,2, Soo-Yong Shin1,2, Mira Kang1,2,3

  • 1Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.

JMIR Medical Informatics
|August 31, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces the Standardization Algorithm for Laboratory Test-Categorical Results (SALT-C), an automated method to clean and standardize electronic health record laboratory data. SALT-C achieves high accuracy, reducing manual efforts for clinical big data research.

Keywords:
data qualitydata scienceelectronic health recordsstandardization

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How Data are Classified: Categorical Data
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Related Experiment Videos

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How Data are Classified: Categorical Data
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Area of Science:

  • Medical Informatics
  • Health Data Science
  • Clinical Data Management

Background:

  • Electronic health records (EHRs) require data standardization for clinical practice and research.
  • Challenges in EHR data standardization include duplicates, errors, and inconsistencies.
  • Manual methods for standardizing laboratory data are labor-intensive and inefficient.

Purpose of the Study:

  • To develop an automated method for standardizing categorical laboratory data in EHRs.
  • To eliminate noise, group, and map cleaned data using standard terminology.
  • To improve the reliability and efficiency of clinical big data research.

Main Methods:

  • Developed the Standardization Algorithm for Laboratory Test-Categorical Results (SALT-C).
  • SALT-C involves data cleaning, categorization into 5 groups, vectorization, similarity calculation, and value assignment.
  • Processes diverse categorical laboratory data, including urinalysis results and color findings.

Main Results:

  • SALT-C was validated on over 59 million data points from 23 years of tertiary hospital records.
  • Achieved high accuracy in mapping unique raw data to reference values across categories: 97.6% (urine color), 97.5% (urine dipstick), 95% (blood type), 99.68% (presence-finding), and 99.61% (pathogenesis).
  • Successfully standardized most categorical laboratory test results, excluding uninterpretable data.

Conclusions:

  • The SALT-C algorithm effectively standardizes categorical laboratory test results with high reliability.
  • Automated standardization via SALT-C significantly reduces manual effort in clinical big data research.
  • SALT-C enhances the utility of EHR data for large-scale clinical research.