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

Simon Meyer Lauritsen (), Mads Kristensen, Mathias Vassard Olsen, Morten Skaarup Larsen, Katrine Meyer Lauritsen, Marianne Johansson Jørgensen, Jeppe Lange and Bo Thiesson
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Simon Meyer Lauritsen: Enversion A/S, Fiskerivej 12
Mads Kristensen: Enversion A/S, Fiskerivej 12
Mathias Vassard Olsen: Aalborg University
Morten Skaarup Larsen: Aalborg University
Katrine Meyer Lauritsen: Aarhus University
Marianne Johansson Jørgensen: Horsens Regional Hospital
Jeppe Lange: Aarhus University
Bo Thiesson: Enversion A/S, Fiskerivej 12

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-17431-x

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