| Autor: |
Ravi B. Parikh, Manqing Liu, Eric Li, Runze Li, Jinbo Chen |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
npj Digital Medicine, Vol 4, Iss 1, Pp 1-5 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2398-6352 |
| DOI: |
10.1038/s41746-021-00477-6 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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