Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients

Kai Wu; Ee Heng Chen; Felix Wirth; Keti Vitanova; Rudiger Lange; Darius Burschka

doi:10.1109/EMBC40787.2023.10340113

Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients

Kai Wu, Ee Heng Chen, Felix Wirth, Keti Vitanova, Rudiger Lange, Darius Burschka

Technical University of Munich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Acute kidney failure is a dangerous complication for ICU patients, and it is difficult to identify at early stage with conventional medical analysis. In recent years, machine learning approaches have been applied to tackle medical diagnosis tasks with great performance. In this work, we deploy machine learning models for early detection of acute kidney failure that can handle static, temporal, sparse and dense data of ICU patients. We investigate different pre-processing methods for patient data to achieve higher prediction performance and how they influence the contribution of different physiological signals in the prediction process.

Original language	English
Title of host publication	2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350324471
DOIs	https://doi.org/10.1109/EMBC40787.2023.10340113
State	Published - 2023
Event	45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, Australia Duration: 24 Jul 2023 → 27 Jul 2023

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Country/Territory	Australia
City	Sydney
Period	24/07/23 → 27/07/23

Access to Document

10.1109/EMBC40787.2023.10340113

Cite this

Wu, K., Chen, E. H., Wirth, F., Vitanova, K., Lange, R., & Burschka, D. (2023). Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC40787.2023.10340113

Wu, Kai ; Chen, Ee Heng ; Wirth, Felix et al. / Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients. 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients",

abstract = "Acute kidney failure is a dangerous complication for ICU patients, and it is difficult to identify at early stage with conventional medical analysis. In recent years, machine learning approaches have been applied to tackle medical diagnosis tasks with great performance. In this work, we deploy machine learning models for early detection of acute kidney failure that can handle static, temporal, sparse and dense data of ICU patients. We investigate different pre-processing methods for patient data to achieve higher prediction performance and how they influence the contribution of different physiological signals in the prediction process.",

author = "Kai Wu and Chen, {Ee Heng} and Felix Wirth and Keti Vitanova and Rudiger Lange and Darius Burschka",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 ; Conference date: 24-07-2023 Through 27-07-2023",

year = "2023",

doi = "10.1109/EMBC40787.2023.10340113",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings",

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Wu, K, Chen, EH, Wirth, F, Vitanova, K, Lange, R & Burschka, D 2023, Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients. in 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023, Sydney, Australia, 24/07/23. https://doi.org/10.1109/EMBC40787.2023.10340113

Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients. / Wu, Kai; Chen, Ee Heng; Wirth, Felix et al.
2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients

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AU - Chen, Ee Heng

AU - Wirth, Felix

AU - Vitanova, Keti

AU - Lange, Rudiger

AU - Burschka, Darius

PY - 2023

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N2 - Acute kidney failure is a dangerous complication for ICU patients, and it is difficult to identify at early stage with conventional medical analysis. In recent years, machine learning approaches have been applied to tackle medical diagnosis tasks with great performance. In this work, we deploy machine learning models for early detection of acute kidney failure that can handle static, temporal, sparse and dense data of ICU patients. We investigate different pre-processing methods for patient data to achieve higher prediction performance and how they influence the contribution of different physiological signals in the prediction process.

AB - Acute kidney failure is a dangerous complication for ICU patients, and it is difficult to identify at early stage with conventional medical analysis. In recent years, machine learning approaches have been applied to tackle medical diagnosis tasks with great performance. In this work, we deploy machine learning models for early detection of acute kidney failure that can handle static, temporal, sparse and dense data of ICU patients. We investigate different pre-processing methods for patient data to achieve higher prediction performance and how they influence the contribution of different physiological signals in the prediction process.

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DO - 10.1109/EMBC40787.2023.10340113

M3 - Conference contribution

C2 - 38083688

AN - SCOPUS:85179644651

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

BT - 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023

Y2 - 24 July 2023 through 27 July 2023

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Wu K, Chen EH, Wirth F, Vitanova K, Lange R , Burschka D. Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC40787.2023.10340113

Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients

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