Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency

Abstract

To illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case‐control study of vitamin D deficiency predicting subsequent suicide.

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 05, 2021
Source ID
10.1002/mpr.1897

Entities

People

  • Andrew J King
  • Boris Tizenberg
  • Elizabeth A. Streeten
  • Jill A. Rachbeisel
  • John C. Umhau
  • Jose R. Zubizarreta
  • Lisa A Brenner
  • Maria V. Petukhova
  • Nancy A. Sampson
  • Patricia A. Deuster
  • Ronald C Kessler
  • Sanjaya K. Upadhyaya
  • Teodor T Postolache

Organizations

  • Defense Advanced Research Projects Agency
  • Division of Intramural Research, National Institute of Allergy and Infectious Diseases
  • F. Edward Hébert School of Medicine
  • Harvard Medical School
  • Harvard University
  • Patient-Centered Outcomes Research Institute
  • University of Colorado
  • University of Maryland School of Medicine

Tags

Readers

  • Neural Network Machine Learning.
  • Psychological Intervention/Treatment for Stress, Anxiety, PTSD, and Related Emotional and Cognitive Health Symptoms.
  • Women's Health and Cancer Risk Research: African American Women and Pregnancy Outcomes.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms