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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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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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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...
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Introduction To Survival Analysis01:18

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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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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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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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Related Experiment Video

Updated: Jan 13, 2026

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Published on: June 10, 2025

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Classification of Clinical Outcomes in Hospitalized Asian Elephants Using Machine Learning and Survival Analysis: A

Worapong Kosaruk1,2, Veerasak Punyapornwithaya1,3, Pichamon Ueangpaiboon4

  • 1Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand.

Veterinary Sciences
|October 28, 2025
PubMed
Summary

Veterinarians can now predict Asian elephant (Elephas maximus) health outcomes using a new machine learning model. This tool analyzes clinical data to improve elephant care and treatment planning.

Keywords:
Asian elephantclassification modelclinical outcomemachine learningsurvival analysistreatment

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Published on: June 10, 2025

522

Area of Science:

  • Veterinary Medicine
  • Wildlife Health
  • Data Science in Animal Health

Background:

  • Captive Asian elephants (Elephas maximus) often face complex diseases, lacking objective tools for outcome prediction.
  • Current clinical decision-making relies heavily on experience, with limited data-driven approaches in wildlife medicine.

Purpose of the Study:

  • To develop and validate a machine learning model for classifying clinical outcomes in hospitalized Asian elephants.
  • To identify key clinical variables influencing elephant health prognoses.

Main Methods:

  • Retrospective analysis of 467 medical records from Thailand's National Elephant Institute (2019-2024).
  • Development of classification models using Random Forest, eXtreme Gradient Boosting, Naïve Bayes, and multinomial logistic regression.
  • Utilized variables: age, sex, disease group, and length of stay (LOS).

Main Results:

  • The Random Forest model demonstrated high performance (accuracy = 86.3%, log-loss = 0.374).
  • Key predictors identified were disease group, length of stay (LOS), and age.
  • Survival analysis showed distinct hospitalization patterns for different disease categories.

Conclusions:

  • Machine learning offers a feasible approach for outcome classification in elephant medicine.
  • Clinical data science can enhance in-hospital prognostication, monitoring, and treatment strategies for elephants.