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

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:

Psychosis: Goals of Pharmacotherapy

Psychosis: Goals of Pharmacotherapy

Antipsychotic drugs are a crucial treatment method for acute and chronic psychoses, bipolar illness, and behavioral disorders. The selection of these drugs depends on several factors, including the state of the disease, clinical judgment, possible drug interactions, and the patient's sensitivity to adverse effects. In immediate scenarios, such as delirium and dementia, short-term treatment with low doses of high-potency typical or atypical agents can effectively manage symptom exacerbation.

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...

You might also read

Related Articles

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

Sort by

Same author

User Perspectives on Conversational Agents for Preventive Alcohol Self-Help: Qualitative Exploratory Study.

JMIR human factors·2026

Same author

Avoidant Personality Traits and Avoidant Coping in Cognitive-Behavioral Therapy vs. Short-Term Psychodynamic Psychotherapy for Adult Depression.

Personality and mental health·2026

Same author

App-based skills training to reduce problem drinking among adult internet help-seekers: a double-blinded randomized controlled trial.

Addiction science & clinical practice·2026

Same author

Are some therapists more effective when they deliver one type of therapy versus another?

Psychotherapy (Chicago, Ill.)·2026

Same author

Examining the factor structure of the 40-item Defense Style Questionnaire among adults with depression: an individual participant data meta-analysis.

Research in psychotherapy (Milano)·2026

Same author

Cognitive reactivity and dysfunctional attitudes in cognitive behavioral and psychodynamic therapy for adult depression.

Psychotherapy research : journal of the Society for Psychotherapy Research·2026

Search research articles

Related Experiment Video

Updated: Nov 26, 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

Predicting hospitalization following psychiatric crisis care using machine learning.

Matthijs Blankers^1,2,3, Louk F M van der Post⁴, Jack J M Dekker^4,5

¹Department of Research, Arkin Mental Health Care, Klaprozenweg 111, 1033NN, Amsterdam, The Netherlands. matthijs.blankers@arkin.nl.

BMC Medical Informatics and Decision Making

|December 11, 2020

Summary

Machine learning models can predict psychiatric hospitalization risk, with Gradient Boosting showing the highest accuracy. Previous mental health care use is a key predictor for psychiatric hospitalization.

Keywords:

Acute psychiatry Machine learning Prognostic modeling Psychiatric hospitalization

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Nov 26, 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

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Psychiatry
Machine Learning
Health Informatics

Background:

Accurate prediction of psychiatric hospitalization is lacking.
Machine learning may improve psychiatric hospitalization prediction models.
This study evaluates machine learning algorithms for predicting psychiatric hospitalization.

Purpose of the Study:

To evaluate the accuracy of ten machine learning algorithms for predicting psychiatric hospitalization within 12 months of psychiatric crisis care contact.
To compare the performance of machine learning algorithms against generalized linear models (GLM/logistic regression).
To identify key predictors of psychiatric hospitalization.

Main Methods:

Utilized data from 2084 patients in the Amsterdam Study of Acute Psychiatry.
Evaluated 39 variables including socio-demographics, clinical characteristics, and prior mental health care contacts.
Compared accuracy and AUC of ten ML algorithms, including an ensemble model, and GLM/logistic regression.

Main Results:

Gradient Boosting achieved the highest accuracy (AUC = 0.774), outperforming GLM/logistic regression.
K-Nearest Neighbors performed the least accurately (AUC = 0.702).
Nine of the top ten predictors were related to previous mental health care use.

Conclusions:

Gradient Boosting demonstrated the highest predictive accuracy for psychiatric hospitalization.
While differences between algorithms were modest, ensemble models can achieve high predictive accuracy.
Previous mental health care utilization is a significant predictor of future hospitalization.