Trajectories of mortality risk among patients with cancer and associated end-of-life utilization

Ravi B. Parikh, Manqing Liu, Eric Li, Runze Li, Jinbo Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Machine learning algorithms may address prognostic inaccuracy among clinicians by identifying patients at risk of short-term mortality and facilitating earlier discussions about hospice enrollment, discontinuation of therapy, or other management decisions. In the present study, we used prospective predictions from a real-time machine learning prognostic algorithm to identify two trajectories of all-cause mortality risk for decedents with cancer. We show that patients with an unpredictable trajectory, where mortality risk rises only close to death, are significantly less likely to receive guideline-based end-of-life care and may not benefit from the integration of prognostic algorithms in practice.

Original languageEnglish (US)
Article number104
Journalnpj Digital Medicine
Volume4
Issue number1
DOIs
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Computer Science Applications
  • Health Informatics
  • Health Information Management

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