Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care.

Journal: Computer methods and programs in biomedicine
PMID:

Abstract

BACKGROUND AND OBJECTIVE: Scalable, flexible and highly interpretable tools for predicting mortality in residential aged care facilities for the purpose of informing and optimizing palliative care decisions, do not exist. This study is the first and most comprehensive work applying machine learning to address this need while seeking to offer a transformative approach to integrating AI into palliative care decision-making. The objective is to predict survival in elderly individuals six months post-admission to residential aged care facilities with patient-level interpretability for transparency and support for clinical decision-making for palliative care options.

Authors

  • Teo Susnjak
    School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand.
  • Elise Griffin
    School of Mathematical and Computational Sciences, Massey University, Albany, Auckland, 0632, New Zealand.