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

By S. Lauritsen, Mads Kristensen, Mathias Vassard Olsen, Morten Skaarup Larsen, K. M. Lauritsen, Marianne Johansson Jørgensen, Jeppe Lange, Bo Thiesson. Nature Communications, 2019

375 citations recorded by the source corpus.

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. Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Here, the authors develop an explainable artificial intelligence early warning score system for its early detection.

Subfields: Artificial Intelligence, Health Information Management

View Paper: https://www.semanticscholar.org/paper/73117e13cbb14601644aad677ea0f523b6076b8e

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Citations in the corpus

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021, background): “In another work [106], the input time series is formed by EHRs sampled at a much lower rate, from which a DNN model can decide among a set of medical conditions.”
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, background): “Lauritsen et al. (2020) proposed an explainable AI EWS (xAI-EWS) system for the early detection of acute critical illness.”
Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, methodology): “…techniques, association rule mining (using biologically-based relationships between data elements), and functional validation of the results (117,119). o f In instances where the biological associations are not well-understood, e.g., in analyses of complex imaging and language data, the AI…”
Harnessing multimodal data integration to advance precision oncology (2021, methodology): “For example, post hoc explanation methods, which seek to interpret model predictions in terms of input feature values, have been applied to probe medical algorithms 137 .”