A computational pipeline for sepsis patients’ stratification and diagnosis

Campos D, Pinho R, Neugebauer U, Popp J, Oliveira JL (2018)


Publication Type: Conference contribution

Publication year: 2018

Publisher: SciTePress

Book Volume: 5

Pages Range: 408-413

Conference Proceedings Title: HEALTHINF 2018 - 11th International Conference on Health Informatics, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018

Event location: Funchal, Madeira, PRT

ISBN: 9789897582813

DOI: 10.5220/0006579104080413

Abstract

Sepsis is still a little acknowledged public health issue, despite its increasing incidence and the growing mortality rate. In addition, a clear diagnosis can be lengthy and complicated, due to highly variable symptoms and non-specific criteria, causing the disease to be diagnosed and treated too late. This paper presents the HemoSpec platform, a decision support system which, by collecting and automatically processing data from several acquisition devices, can help in the early diagnosis of sepsis.

Involved external institutions

How to cite

APA:

Campos, D., Pinho, R., Neugebauer, U., Popp, J., & Oliveira, J.L. (2018). A computational pipeline for sepsis patients’ stratification and diagnosis. In Reyer Zwiggelaar, Hugo Gamboa, Ana Fred, Sergi Bermudez i Badia (Eds.), HEALTHINF 2018 - 11th International Conference on Health Informatics, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 (pp. 408-413). Funchal, Madeira, PRT: SciTePress.

MLA:

Campos, David, et al. "A computational pipeline for sepsis patients’ stratification and diagnosis." Proceedings of the 11th International Conference on Health Informatics, HEALTHINF 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018, Funchal, Madeira, PRT Ed. Reyer Zwiggelaar, Hugo Gamboa, Ana Fred, Sergi Bermudez i Badia, SciTePress, 2018. 408-413.

BibTeX: Download