PREDICTING MORTALITY IN ADULTS USING TRANSFORMERS: INSIGHTS FROM THE HEALTH AND RETIREMENT STUDY
2024
Predicting Mortality in Adults Using Transformers
publication
Evidence: high
Author Information
Author(s): Weiss Jordan, Azhir Alaleh, Ram Nilam, Rehkopf David
Primary Institution: Stanford University
Hypothesis
Can a natural language processing model predict mortality in adults based on their demographic and health histories?
Conclusion
The NLP-based prediction model outperforms traditional statistical models in predicting mortality.
Supporting Evidence
- The model achieved an average precision score of 0.900, significantly higher than the 0.395 score of conventional statistical models.
- The study opens new avenues for personalized healthcare in observational settings.
Takeaway
This study uses a computer program to guess who might die next based on people's life stories and health information, and it does a better job than older methods.
Methodology
A transformer architecture was used to create embeddings from respondents' demographic, social, behavioral, and health histories to predict mortality.
Participant Demographics
Respondents from the US-based Health and Retirement Study.
Digital Object Identifier (DOI)
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