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Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation.

Ilia RattsevVered StearnsAmanda L BlackfordDaniel L HertzKaren L SmithJames M RaeCasey Overby Taylor
Published in: JAMIA open (2024)
Incorporation of genomic and 3-month follow-up data improved the ability of the models to identify the individuals at risk of AI discontinuation. Genetic risk factors were particularly important for predicting early discontinuers. This study provides insight into the complex nature of AI discontinuation and highlights the importance of incorporating genetic risk factors and emergent symptoms into prediction models.
Keyphrases
  • risk factors
  • copy number
  • genome wide
  • artificial intelligence
  • big data
  • electronic health record
  • stem cells
  • dna methylation
  • machine learning
  • mesenchymal stem cells
  • bone marrow
  • depressive symptoms
  • data analysis