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Related Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Psychological and Sociocultural Causes of Schizophrenia01:29

Psychological and Sociocultural Causes of Schizophrenia

Schizophrenia, a complex psychiatric disorder, has been historically misunderstood. Early psychological theories attributed its origins to childhood trauma and unresponsive parenting. However, contemporary research largely rejects these notions, favoring the vulnerability-stress hypothesis. This model proposes that individuals with a genetic predisposition to schizophrenia may develop the disorder following exposure to significant environmental stressors. Notably, studies on high-risk...
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
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Related Experiment Video

Updated: Jul 11, 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

A predictor model for suicide attempt: evidence from a population-based study.

Marzieh Nojomi1, Seyed-Kazem Malakouti, Jafar Bolhari

  • 1Department of Community Medicine, School of Medicine, Iran University of Medical Sciences, Crossroads of Hemmat and Chamran Expressways, Tehran 15875-6171, Iran. drnojomi@iums.ac.ir

Archives of Iranian Medicine
|October 2, 2007
PubMed
Summary

Understanding suicide risk factors is crucial for prevention. This study identified younger age, female sex, mental disorders, substance use, and unemployment as key predictors of suicide attempts in Karaj City.

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Last Updated: Jul 11, 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:

  • Public Health
  • Epidemiology
  • Psychiatry

Background:

  • Developing countries face a critical need for suicide prevention strategies.
  • Identifying and managing suicide risk factors is essential for effective intervention planning.
  • This study addresses the need for a predictive model for suicide attempts in Karaj City.

Purpose of the Study:

  • To determine a predictive model for suicide attempts based on identified risk factors.
  • To provide data for planning therapeutic, preventive, and educational interventions.
  • To understand the epidemiology of suicide attempts in Karaj City.

Main Methods:

  • A cross-sectional study was conducted in Karaj City, Iran.
  • Data were collected using the World Health Organization (WHO) SUPRE-MISS questionnaire.
  • A total of 2300 individuals were interviewed, covering demographics, personal/family history, substance use, mental/physical disorders, and community stress.

Main Results:

  • The study included 65% females, with a mean age of 26 for attempters versus 32 for non-attempters.
  • Significant predictors of suicide attempt included younger age, female sex, history of mental disorders, lifelong tobacco/alcohol use, and unemployment.
  • Demographic factors like high-school education and marriage status were also noted.

Conclusions:

  • Suicide prevention is achievable through a thorough understanding of its risk factors.
  • The identified predictors can inform targeted interventions for suicide prevention.
  • This research provides a foundation for developing localized suicide prevention programs.