Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units

Yun Chen, Hui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

The rapid advancements of biomedical instrumentation and healthcare technology have resulted in data-rich environments in hospitals. However, the meaningful information extracted from rich datasets is limited. There is a dire need to go beyond current medical practices, and develop data-driven methods and tools that will enable and help (i) the handling of big data, (ii) the extraction of data-driven knowledge, (iii) the exploitation of acquired knowledge for optimizing clinical decisions. This present study focuses on the prediction of mortality rates in Intensive Care Units (ICU) using patient-specific healthcare recordings. It is worth mentioning that postsurgical monitoring in ICU leads to massive datasets with unique properties, e.g., variable heterogeneity, patient heterogeneity, and time asyncronization. To cope with the challenges in ICU datasets, we developed the postsurgical decision support system with a series of analytical tools, including data categorization, data pre-processing, feature extraction, feature selection, and predictive modeling. Experimental results show that the proposed data-driven methodology outperforms traditional approaches and yields better results based on the evaluation of real-world ICU data from 4000 subjects in the database. This research shows great potentials for the use of data-driven analytics to improve the quality of healthcare services.

Original languageEnglish (US)
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4310-4314
Number of pages5
ISBN (Electronic)9781424479290
DOIs
StatePublished - Nov 2 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: Aug 26 2014Aug 30 2014

Publication series

Name2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

Other

Other2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
CountryUnited States
CityChicago
Period8/26/148/30/14

Fingerprint

Intensive care units
Intensive Care Units
Mortality
Feature extraction
Delivery of Health Care
Quality of Health Care
Decision support systems
Databases
Technology
Monitoring
Processing
Research
Datasets

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering
  • Medicine(all)

Cite this

Chen, Y., & Yang, H. (2014). Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (pp. 4310-4314). [6944578] (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2014.6944578
Chen, Yun ; Yang, Hui. / Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 4310-4314 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014).
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Chen, Y & Yang, H 2014, Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014., 6944578, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 4310-4314, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 8/26/14. https://doi.org/10.1109/EMBC.2014.6944578

Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units. / Chen, Yun; Yang, Hui.

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 4310-4314 6944578 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Chen Y, Yang H. Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 4310-4314. 6944578. (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014). https://doi.org/10.1109/EMBC.2014.6944578