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

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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:

You might also read

Related Articles

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

Sort by

Same author

Molecular genetic influences on sustained attention and executive processes and their links with psychopathology in the AFFECT study.

Molecular psychiatry·2026

Same author

Mapping the genetic architecture of human cortical expansion and its links to neuropsychiatric disorders.

bioRxiv : the preprint server for biology·2026

Same author

Association of Genetic Liability to Psychiatric Disorders with Peripheral Metabolic Dysregulation.

medRxiv : the preprint server for health sciences·2026

Same author

Why People Conceal Mental Health Problems: Qualitative Analysis of TikTok Posts.

Journal of medical Internet research·2026

Same author

Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods.

JMIR medical informatics·2026

Same author

Using smartphone surveys to predict next-week suicide attempts.

Journal of psychopathology and clinical science·2026

Search research articles

Related Experiment Video

Updated: Sep 30, 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

Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With

Kate H Bentley^1,2,3, Kelly L Zuromski², Rebecca G Fortgang²

¹Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.

JMIR Formative Research

|March 11, 2022

Summary

This summary is machine-generated.

Healthcare providers are dissatisfied with current suicide risk assessment tools but are open to machine learning models for predicting suicide risk. Key facilitators include specific risk factors and systematic workflows, while barriers involve liability and alert fatigue.

Keywords:

implementation machine learning mobile phone suicide

More Related Videos

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

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

Related Experiment Videos

Last Updated: Sep 30, 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 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

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

Area of Science:

Clinical Informatics
Artificial Intelligence in Healthcare
Mental Health Research

Background:

Machine learning models for suicide risk prediction using electronic health record data are proliferating.
Clinical implementation and utility of these models remain largely unexplored.
Stakeholder partnership, especially with frontline providers, is crucial for successful deployment.

Purpose of the Study:

To inform the deployment of suicide risk prediction models in clinical practice.
To understand current suicide risk assessment and management practices.
To identify provider perspectives, barriers, facilitators, and recommendations for automated suicide risk prediction tools.

Main Methods:

Conducted 10 focus groups with 40 healthcare providers across various departments.
Transcribed and coded audio recordings for recurrent themes.
Utilized consensus meetings for discrepancy resolution.

Main Results:

Providers expressed dissatisfaction with current suicide risk assessment tools.
General attitudes towards automated suicide risk prediction models were positive.
Key facilitators included presenting specific risk factors and developing systematic workflows; barriers included liability and alert fatigue.

Conclusions:

Providers are open to machine learning-based suicide risk prediction systems despite dissatisfaction with current methods.
Concerns regarding barriers and facilitators for implementation were raised.
Future efforts should incorporate systematic qualitative feedback from diverse stakeholders.