Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Steps in Outbreak Investigation

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:

Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

You might also read

Related Articles

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

Sort by

Same author

The effect of reduced-step finishing and polishing protocols on roughness, gloss, and surface quality of bulk-fill resin-based composites.

BMC oral health·2026

Same author

Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.

Frontiers in oncology·2026

Same author

PRAME Immunohistochemical Expression as a Diagnostic Tool to Distinguish Between Endocervical and Endometrial Adenocarcinomas.

International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists·2026

Same author

Circulating Orexin-A Levels in Patients With Schizophrenia, Bipolar Disorder, and Major Depressive Disorder: A Systematic Review and Meta-Analysis.

Cureus·2026

Same author

Consensus-based expert recommendations on the management of heavily treatment-experienced people living with HIV in the Middle East.

Journal of infection and public health·2025

Same author

End of Apnea Event Prediction Leveraging EEG Signals and Interpretable Machine Learning.

Biosensors·2025

Search research articles

Related Experiment Video

Updated: Sep 30, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients.

Abdulrahman A Alrajhi¹, Osama A Alswailem², Ghassan Wali³

¹Department of Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia.

International Journal of Environmental Research and Public Health

|March 10, 2022

Summary

This study developed a real-time COVID-19 severity prediction tool using machine learning for hospitalized patients. The random forest model demonstrated excellent performance, aiding resource allocation during surges.

Keywords:

COVID-19 applied artificial intelligence decision support systems hospital operations severity prediction

More Related Videos

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

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

Published on: May 15, 2020

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis

Published on: June 30, 2023

Related Experiment Videos

Last Updated: Sep 30, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

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

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

Published on: May 15, 2020

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis

Published on: June 30, 2023

Area of Science:

* Medical Informatics
* Machine Learning in Healthcare
* Infectious Disease Epidemiology

Background:

* Clinicians require stable tools for predicting COVID-19 severity to optimize hospital resource allocation.
* Evolving COVID-19 guidelines and limitations in early prediction models hinder effective clinical decision-making.
* Existing models often lack generalizability and clinical validation in real-world hospital settings.

Purpose of the Study:

* To develop and validate a real-time COVID-19 severity prediction tool for hospitalized patients.
* To assist clinicians in resource management and patient care during COVID-19 surges.
* To provide a data-driven framework for predicting COVID-19 severity at admission using comprehensive clinical data.

Main Methods:

* Evaluation of four machine learning models using a large dataset (1386 patients, March 2020-April 2021).
* Utilization of comprehensive patient-level clinical data from electronic medical records, vital sign monitors, and PCR tests.
* Development of a multi-class, data-driven framework by clinical and data experts.

Main Results:

* The random forest model achieved high discrimination in concurrent validation (AUC 0.83-0.87).
* Prospective validation demonstrated promising performance with recall ranging from 78.4-90.0% and precision from 75.0-97.8% across severity classes.
* The developed framework effectively predicts COVID-19 severity at the time of hospital admission.

Conclusions:

* The proposed machine learning framework offers a reliable tool for predicting COVID-19 severity in hospitalized patients.
* This tool can significantly improve the management of healthcare resources during pandemic surges.
* The study highlights the potential of leveraging comprehensive EMR data for real-time clinical decision support.