TY - GEN
T1 - Risk Estimation for ICU Patients with Personalized Anomaly-Encoded Bedside Patient Data
AU - Wu, Kai
AU - Chen, Ee Heng
AU - Wirth, Felix
AU - Vitanova, Keti
AU - Lange, Rudiger
AU - Burschka, Darius
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We propose a novel framework to estimate intensive care unit patients' health risk continuously with anomaly-encoded patient data. This framework consists of two modules. In the first module, we use Gaussian process models to learn change trend and day-night circulation in temporal patient data and annotate abnormal data. Such models provide dynamically adaptable bedside patient monitoring instead of conventional threshold-based monitoring. In the second module, we use the abnormal data together with the learned Gaussian models to estimate patients' risk level by predicting their in-hospital mortality and remaining length of stay in ICU ward. We show that prediction models with anomaly-encoded data have better performance than those with raw patient measurements, and they are comparable with state-of-art prediction models.
AB - We propose a novel framework to estimate intensive care unit patients' health risk continuously with anomaly-encoded patient data. This framework consists of two modules. In the first module, we use Gaussian process models to learn change trend and day-night circulation in temporal patient data and annotate abnormal data. Such models provide dynamically adaptable bedside patient monitoring instead of conventional threshold-based monitoring. In the second module, we use the abnormal data together with the learned Gaussian models to estimate patients' risk level by predicting their in-hospital mortality and remaining length of stay in ICU ward. We show that prediction models with anomaly-encoded data have better performance than those with raw patient measurements, and they are comparable with state-of-art prediction models.
UR - http://www.scopus.com/inward/record.url?scp=85179641722&partnerID=8YFLogxK
U2 - 10.1109/EMBC40787.2023.10340317
DO - 10.1109/EMBC40787.2023.10340317
M3 - Conference contribution
C2 - 38083790
AN - SCOPUS:85179641722
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
ER -
