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

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

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.

OriginalspracheEnglisch
Titel2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9798350324471
DOIs
PublikationsstatusVeröffentlicht - 2023
Veranstaltung45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, Australien
Dauer: 24 Juli 202327 Juli 2023

Publikationsreihe

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

Konferenz

Konferenz45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Land/GebietAustralien
OrtSydney
Zeitraum24/07/2327/07/23

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