Publications by authors named "Matthew Barker-Hewitt"
Cancers (Basel)
August 2021
Article Synopsis
- Acute kidney injury (AKI) is a frequent issue in cancer patients that leads to worse treatment outcomes and higher death rates, prompting the need for earlier detection.
- Researchers developed a random forest model using over 597,000 blood test results from nearly 49,000 cancer patients to predict AKI events within the following 30 days, achieving an accuracy of 88.1%.
- The model successfully identified AKI risk levels in 73.8% of patients prior to an event, indicating that routine blood tests could be instrumental in reducing the incidence of AKI and minimizing interruptions in cancer treatment.
