Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths
| Autor: | Ardavan Khoshnood, Ellen Tolestam Heyman, Ulf Ekelund, Lina Holmqvist, Mattias Ohlsson, Markus Lingman, Awais Ashfaq |
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| Rok vydání: | 2021 |
| Předmět: |
Palliative care
Receiver operating characteristic business.industry Registry study Advanced stage Emergency department Logistic regression Machine learning computer.software_genre Machine Learning Logistic Models ROC Curve 30 day mortality Emergency Medicine Humans Medicine In patient Artificial intelligence Emergency Service Hospital business computer Retrospective Studies |
| Zdroj: | The Journal of Emergency Medicine. 61:763-773 |
| ISSN: | 0736-4679 |
| Popis: | BACKGROUND Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit. OBJECTIVES To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge. METHODS In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). RESULTS Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models. CONCLUSION In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly. |
| Databáze: | OpenAIRE |
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