Yes / We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients’ first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital (n=24696) and compared the performance of these models in data from another hospital (n=13477). We used two performance measures – the calibration slope and area under the curve (AUC). The logistic model performed reasonably well – calibration slope 0.90, AUC 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting. / Health Foundation; National Institute for Health Research (NIHR) Yorkshire and Humberside Patient Safety Translational Research Centre (NIHR YHPSTRC)
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/16623 |
| Date | 29 November 2018 |
| Creators | Faisal, Muhammad, Scally, Andy J., Howes, R., Beatson, K., Richardson, D., Mohammed, Mohammed A. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted manuscript |
| Rights | © The Author(s) 2018. The final, definitive version of this paper has been published in Health Informatics Journal, vol 26/issue 1 by SAGE Publications Ltd, All rights reserved. |
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