Researchers at the University of Copenhagen trained the algorithm on the medical data of more than 230,000 Danish ICU patients. When only long-term disease history was analyzed, the algorithm performed similarly to three widely used mortality risk prediction tools — the Multimorbidity Index, SAPS II and APACHE II — and became more precise with more extensive medical history data.
However, when a neural network of the patients’ acute physiology measures was also factored in, the algorithm consistently outperformed all three previous methods, all of which were based on static, linear representations rather than utilizing machine learning.
“Our study shows that more realistic interpretations of the risk of death can be achieved with neural network models than by studying the additive effects of disease history and ICU measures,” the researchers wrote. “Explainable machine learning models are key in clinical settings, and our results emphasize how to progress toward the transformation of advanced models into actionable, transparent and trustworthy clinical tools.”
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