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

Author

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  • Simon Meyer Lauritsen

    (Enversion A/S, Fiskerivej 12
    Aarhus University)

  • Mads Kristensen

    (Enversion A/S, Fiskerivej 12)

  • Mathias Vassard Olsen

    (Aalborg University)

  • Morten Skaarup Larsen

    (Aalborg University)

  • Katrine Meyer Lauritsen

    (Aarhus University
    Aarhus University Hospital)

  • Marianne Johansson Jørgensen

    (Horsens Regional Hospital)

  • Jeppe Lange

    (Aarhus University
    Horsens Regional Hospital)

  • Bo Thiesson

    (Enversion A/S, Fiskerivej 12
    Aarhus University)

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.

Suggested Citation

  • Simon Meyer Lauritsen & Mads Kristensen & Mathias Vassard Olsen & Morten Skaarup Larsen & Katrine Meyer Lauritsen & Marianne Johansson Jørgensen & Jeppe Lange & Bo Thiesson, 2020. "Explainable artificial intelligence model to predict acute critical illness from electronic health records," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17431-x
    DOI: 10.1038/s41467-020-17431-x
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    Cited by:

    1. Mohammad Ordikhani & Mohammad Saniee Abadeh & Christof Prugger & Razieh Hassannejad & Noushin Mohammadifard & Nizal Sarrafzadegan, 2022. "An evolutionary machine learning algorithm for cardiovascular disease risk prediction," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-16, July.
    2. Wasif Khan & Nazar Zaki & Nadirah Ghenimi & Amir Ahmad & Jiang Bian & Mohammad M Masud & Nasloon Ali & Romona Govender & Luai A Ahmed, 2023. "Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-17, December.
    3. Haque, AKM Bahalul & Islam, A.K.M. Najmul & Mikalef, Patrick, 2023. "Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    4. Gabriel Ferrettini & Elodie Escriva & Julien Aligon & Jean-Baptiste Excoffier & Chantal Soulé-Dupuy, 2022. "Coalitional Strategies for Efficient Individual Prediction Explanation," Information Systems Frontiers, Springer, vol. 24(1), pages 49-75, February.
    5. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    6. Nohyeong Jeong & Shinyun Park & Subhamoy Mahajan & Ji Zhou & Jens Blotevogel & Ying Li & Tiezheng Tong & Yongsheng Chen, 2024. "Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    7. Minoru Sakuragi & Eiichiro Uchino & Noriaki Sato & Takeshi Matsubara & Akihiko Ueda & Yohei Mineharu & Ryosuke Kojima & Motoko Yanagita & Yasushi Okuno, 2024. "Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-14, March.

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