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

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...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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 number is...
What Are Outliers?01:12

What Are Outliers?

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...
Outliers and Influential Points01:08

Outliers and Influential Points

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 vertical...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Pharmacovigilance01:19

Pharmacovigilance

Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...

You might also read

Related Articles

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

Sort by
Same author

The use of logic for machine learning models in sepsis.

Intensive care medicine experimental·2026
Same author

Exploring Sexual Health and Well-Being in Rural Areas: A Systematic Literature Review with Case Study from Indonesia.

International journal of sexual health : official journal of the World Association for Sexual Health·2026
Same author

A Decision-Theoretic Perspective on Fairness in Clinical Predictive Models.

Research square·2026
Same author

Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction.

Transactions on machine learning research·2026
Same author

Causal modeling reveals cell-cell communication dynamics in the tumor microenvironment during anti-PD-1 therapy in breast cancer patients.

Briefings in bioinformatics·2026
Same author

Cardiovascular and Autonomic Phenotypes Reveal Distinct Mechanisms of Sepsis Decompensation via Deep Learning.

Research square·2026
Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Automating Adjudication of Cardiovascular Events Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
See all related articles

Related Experiment Video

Updated: Jun 4, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Conditional outlier detection for clinical alerting.

Milos Hauskrecht1, Michal Valko, Iyad Batal

  • 1Computer Science Department.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 25, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an anomaly detection method for patient management in electronic health records (EHR). Unusual actions may indicate errors, and this approach helps identify them with low false alerts.

Related Experiment Videos

Last Updated: Jun 4, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Patient Safety

Background:

  • Electronic health record (EHR) systems contain vast patient data.
  • Identifying unusual patient management actions is crucial for error detection.
  • Existing methods may not effectively flag subtle deviations in care.

Purpose of the Study:

  • To develop and evaluate a data-driven approach for detecting anomalous patient-management actions.
  • To test the hypothesis that unusual actions correlate with potential errors.
  • To assess the feasibility of anomaly-based alerting in clinical settings.

Main Methods:

  • Utilized a dataset of 4,486 post-cardiac surgical patients from EHRs.
  • Developed a data-driven anomaly detection algorithm.
  • Evaluated the system's performance based on expert panel opinions.

Main Results:

  • The anomaly-based alerting system demonstrated reasonably low false alert rates.
  • Stronger detected anomalies correlated with higher probabilities of being clinically significant.
  • The approach showed potential for identifying unusual patient management strategies.

Conclusions:

  • Data-driven anomaly detection is a viable strategy for enhancing patient safety.
  • This method can flag potentially erroneous patient-management actions within EHR data.
  • Further research can refine anomaly detection for improved clinical decision support.