Transforming Mortality Prediction: A Transformer-Based Mortality Prediction Model

Transformer-based models, such as BEHRT, significantly enhance mortality prediction compared with traditional approaches. These findings highlight the potential of transformer neural network models in social science-focused population health research on aging.

Authors

Alaleh Azhir (ORCID: https://orcid.org/0000-0002-0330-8823)
Nilàm Ram (ORCID: https://orcid.org/0000-0003-1671-5257)
Jordan Weiss (ORCID: https://orcid.org/0000-0002-0392-3535)
David H. Rehkopf (ORCID: https://orcid.org/0000-0002-7597-6513)

Institutions

Brigham and Women's Hospital (US)
Palo Alto University (US)
Stanford Health Care (US)
Stanford Medicine (US)
Optimal Solutions (United States) (US)
Stanford University (US)

Publication Details

Journal: The Journals of Gerontology Series B
Published: 2025-05-17
DOI: https://doi.org/10.1093/geronb/gbaf089
Citations: 1
Primary Topic: Insurance, Mortality, Demography, Risk Management
Type: article
Field-Weighted Citation Impact: 5.59

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article

Transforming Mortality Prediction: A Transformer-Based Mortality Prediction Model

Alaleh Azhir, Nilàm Ram, Jordan Weiss, David H. Rehkopf

May 17, 2025

1 citations

The Journals of Gerontology Series B

Insurance, Mortality, Demography, Risk Management

5.59

article

Transforming Mortality Prediction: A Transformer-Based Mortality Prediction Model

Alaleh Azhir, Nilàm Ram, Jordan Weiss, David H. Rehkopf

May 17, 2025article en

1 citations

Abstract

Transformer-based models, such as BEHRT, significantly enhance mortality prediction compared with traditional approaches. These findings highlight the potential of transformer neural network models in social science-focused population health research on aging.

The Journals of Gerontology Series BVol. 80(7)

Brigham and Women's Hospital (US), Palo Alto University (US), Stanford Health Care (US), Stanford Medicine (US), Optimal Solutions (United States) (US), Stanford University (US)

Openalex Percentile: Top 6%

Insurance, Mortality, Demography, Risk Management

5.59

DOI Source OpenAlex

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