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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.
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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.
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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.
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Hazard Rate01:11

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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Related Experiment Video

Updated: May 12, 2025

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Algorithm for investigating risk analysis and factors affecting suicidal attempts under uncertainty.

RuiHua Liang1, Ni Duan1, XueJing Liu1

  • 1Department of Psychiatry, Qingdao Mental Health Center, Qingdao, 266000, Shandong, China.

Scientific Reports
|May 7, 2025
PubMed
Summary

This study introduces a novel risk assessment model to predict suicide attempts using interval-valued q-rung orthopair fuzzy (ivq-ROF) sets and the EDAS method. The approach enhances accuracy for mental health interventions and resource allocation.

Keywords:
Aggregation operatorsEDAS methodInterval-valued q-rung orthopair fuzzy setMulticriteria decision makingRisk analysisSugeno–Weber operationsSuicidal attempts

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Area of Science:

  • Public Health
  • Data Science
  • Fuzzy Logic

Background:

  • Suicide presents a significant global challenge, necessitating improved risk assessment tools.
  • Current methods struggle with uncertainty in risk factor evaluation.

Purpose of the Study:

  • To develop and validate a novel risk assessment model for suicidal attempts under conditions of risk uncertainty.
  • To enhance the accuracy of suicide risk prediction for mental health practitioners and researchers.

Main Methods:

  • Utilized interval-valued q-rung orthopair fuzzy (ivq-ROF) set information with Sugeno-Weber aggregation operators and the EDAS method.
  • Applied Positive Distance from the Average (PDA) and Negative Distance from the Average (NDA) for criteria normalization and ranking.
  • Introduced ivq-ROF Sugeno-Weber weighted averaging (ivq-ROFSWWA) and ivq-ROFS weighted geometric (ivq-ROFSWG) operators for fuzzy information aggregation.

Main Results:

  • The proposed model accurately identifies critical risk factors by combining PDA and NDA scores.
  • The approach demonstrates increased accuracy in predicting suicide risk compared to existing methods.
  • The algorithm effectively handles compound data interfaces for decision-making.

Conclusions:

  • The developed analytical framework offers a robust approach to understanding and mitigating suicide risk.
  • Findings can inform mental health policies, resource allocation, and intervention strategies.
  • The study aims to contribute to the reduction of future suicides through enhanced analytical capabilities.