Researchers from the University of California San Diego developed a machine-learning model by training a boosted decision tree algorithm on de-identified electronic health records data of 5,822 hospitalized or ambulatory patients with heart failure from the University of California San Diego.
This machine learning model is based on eight readily available variables. These include, diastolic blood pressure, creatinine, blood urea nitrogen, haemoglobin, white blood cell count, platelets, albumin, and red blood cell distribution width. This model was able to predict life expectancy in 88% of the patients.
This study is published in the European Journal of Heart Failure.
The tool was additionally tested using data from the University of California, San Francisco, and a database derived from 11 European medical centers.
With the advance of machine learning and artificial intelligence tools, large amounts of health data thanks to electronic health records, and computing power, we able to work on creating more and more accurate risk prediction tools. These tools will be commonplace in clinical practice to help with data-based decisions.
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