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Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs
| Content Provider | SAGE Publishing |
|---|---|
| Author | Blanes-Selva, Vicent Doñate-Martínez, Ascensión Linklater, Gordon García-Gómez, Juan M. |
| Copyright Year | 2022 |
| Abstract | Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Bad survival prognosis and patients' decline are working criteria to guide PC decision-making for older patients. Still, there is not a clear consensus on when to initiate early PC. This work aims to propose machine learning approaches to predict frailty and mortality in older patients in supporting PC decision-making. Predictive models based on Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) were implemented for binary 1-year mortality classification, survival estimation and 1-year frailty classification. Besides, we tested the similarity between mortality and frailty distributions. The 1-year mortality classifier achieved an Area Under the Curve Receiver Operating Characteristic (AUC ROC) of 0.87 [0.86, 0.87], whereas the mortality regression model achieved an mean absolute error (MAE) of 333.13 [323.10, 342.49] days. Moreover, the 1-year frailty classifier obtained an AUC ROC of 0.89 [0.88, 0.90]. Mortality and frailty criteria were weakly correlated and had different distributions, which can be interpreted as these assessment measurements are complementary for PC decision-making. This study provides new models that can be part of decision-making systems for PC services in older patients after their external validation. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.1177/14604582221092592?download=true |
| ISSN | 14604582 |
| Issue Number | 2 |
| Volume Number | 28 |
| Journal | Health Informatics Journal (JHI) |
| e-ISSN | 17412811 |
| DOI | 10.1177/14604582221092592 |
| Language | English |
| Publisher | Sage Publications UK |
| Publisher Date | 2022-06-01 |
| Publisher Place | London |
| Access Restriction | Open |
| Rights Holder | © The Author(s) 2022 |
| Subject Keyword | machine learning older patients mortality needs assessment frailty palliative care deep learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics |
