AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units

Ozyurt Y, Kraus M, Hatt T, Feuerriegel S (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Association for Computing Machinery

Pages Range: 3452-3462

Conference Proceedings Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Event location: Virtual SG

ISBN: 9781450383325

DOI: 10.1145/3447548.3467143

Abstract

Clinical practice in intensive care units (ICUs) requires early warnings when a patient's condition is about to deteriorate so that preventive measures can be undertaken. To this end, prediction algorithms have been developed that estimate the risk of mortality in ICUs. In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs. Specifically, we develop an attentive deep Markov model called AttDMM. To the best of our knowledge, AttDMM is the first ICU prediction model that jointly learns both long-term disease dynamics (via attention) and different disease states in health trajectory (via a latent variable model). Our evaluations were based on an established baseline dataset (MIMIC-III) with 53,423 ICU stays. The results confirm that compared to state-of-the-art baselines, our AttDMM was superior: AttDMM achieved an area under the receiver operating characteristic curve (AUROC) of 0.876, which yielded an improvement over the state-of-the-art method by 2.2%. In addition, the risk score from the AttDMM provided warnings several hours earlier. Thereby, our model shows a path towards identifying patients at risk so that health practitioners can intervene early and save patient lives.

Authors with CRIS profile

Mathias Kraus Juniorprofessur für Data Analytics

Involved external institutions

Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich CH Switzerland (CH)

How to cite

APA:

Ozyurt, Y., Kraus, M., Hatt, T., & Feuerriegel, S. (2021). AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 3452-3462). Virtual, SG: Association for Computing Machinery.

MLA:

Ozyurt, Yilmazcan, et al. "AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units." Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021, Virtual Association for Computing Machinery, 2021. 3452-3462.

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