Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.3K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.3K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.7K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.7K
Outliers and Influential Points01:08

Outliers and Influential Points

4.1K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.1K
What Are Outliers?01:12

What Are Outliers?

4.0K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
4.0K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Unusual Results01:16

Unusual Results

3.2K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
3.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Characterizing Real-World Data by Care Setting to Support Clinical Research.

Studies in health technology and informatics·2026
Same author

Practical Implications for Using Laboratory Data: Research over Federated Networks.

Studies in health technology and informatics·2026
Same author

A Large-Scale Analysis of Autologous Stem Cell Transplantation for Multiple Myeloma Patients Older than 65 Years.

Clinical lymphoma, myeloma & leukemia·2025
Same author

Enhancing real world data interoperability in healthcare: A methodological approach to laboratory unit harmonization.

International journal of medical informatics·2024
Same author

A global federated real-world data and analytics platform for research.

JAMIA open·2023
Same author

Building an i2b2-Based Population Repository for COVID-19 Research.

Studies in health technology and informatics·2022
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Video

Updated: Jul 30, 2025

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
08:33

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.1K

Automatic Outlier Detection in Laboratory Result Distributions Within a Real World Data Network.

Aída Muñoz Monjas1, David Rubio Ruiz1,2, David Pérez-Rey1

  • 1Biomedical Informatics Group, Universidad Politécnica de Madrid, Spain.

Studies in Health Technology and Informatics
|May 19, 2023
PubMed
Summary
This summary is machine-generated.

Standardizing laboratory test results using LOINC codes is crucial for healthcare data interoperability. This study evaluated two methods for automatically setting histogram limits to exclude outliers in Real World Data (RWD).

Keywords:
LOINCOutlier detectioninteroperabilitylaboratory testreal world data

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions
08:23

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Published on: September 25, 2018

13.3K

Related Experiment Videos

Last Updated: Jul 30, 2025

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
08:33

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions
08:23

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Published on: September 25, 2018

13.3K

Area of Science:

  • Health Informatics
  • Data Science
  • Biostatistics

Background:

  • Accurate comparison of laboratory test results across healthcare organizations requires data interoperability.
  • Standardized terminologies, such as LOINC (Logical Observation Identifiers, Names and Codes), are essential for unique identification of laboratory tests.
  • Real World Data (RWD) often contains outliers and abnormal values that necessitate exclusion from analysis.

Purpose of the Study:

  • To analyze two automated methods for selecting histogram limits to sanitize lab test result distributions.
  • To compare Tukey's box-plot method and a 'Distance to Density' approach within the TriNetX Real World Data Network.
  • To evaluate the impact of these methods on data sanitization for RWD.

Main Methods:

  • Implementation of Tukey's box-plot method for outlier detection and exclusion.
  • Application of a 'Distance to Density' algorithm for identifying and excluding abnormal values.
  • Analysis of laboratory test results within the TriNetX Real World Data Network.

Main Results:

  • The study compared the generated histogram limits from both methods using clinical RWD.
  • Tukey's method generally produced wider limits, while the 'Distance to Density' approach yielded narrower limits.
  • Both methods' outcomes were significantly influenced by the chosen algorithm parameters.

Conclusions:

  • Automated methods can effectively sanitize lab test result distributions by excluding outliers.
  • The choice between Tukey's method and 'Distance to Density' depends on desired limit width and parameter sensitivity.
  • Further research is needed to optimize parameter selection for robust RWD analysis.