Explainable artificial intelligence model to predict acute critical illness from electronic health records

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DOI

Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as early warning scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on electronic health records (EHR) trained artificial intelligence (AI) systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. Here, we present an explainable AI early warning score (xAI-EWS) system for early detection of acute critical illness. xAI-EWS potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it.
OriginalsprogEngelsk
Artikelnummer3852
TidsskriftNature Communications
Vol/bind11
ISSN2041-1723
DOI
StatusUdgivet - jul. 2020

    Forskningsområder

  • Kritisk sygdom, Sepsis, Akut nyresygdom, kunstig intelligens, AI, Artificial intelligence, Acute Kidney Injury, Acute Lung Injury/diagnosis, Computer learning, Computer Science, Maskinlæringsalgoritme, Maskinlæring, Machine Learning

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