Sökning: id:"swepub:oai:DiVA.org:su-195000" >
Cognitive Internet ...
Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning
-
- Alam, Mahbub Ul (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
-
- Rahmani, Rahim (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
-
(creator_code:org_t)
- 2021-03-30
- 2021
- Engelska.
-
Ingår i: Biomedical Engineering Systems and Technologies. - Cham : Springer. - 9783030723781 - 9783030723798 ; , s. 366-384
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- The internet of medical things (IoMT) is a relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits with the combination of cognitive computing. Effective utilization of the healthcare data is the critical factor in achieving such potential, which can be a significant challenge as the medical data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority. To address this issue, in this paper, we introduce a cognitive internet of medical things architecture with a use case of early sepsis detection using electronic health records. We discuss the various aspects of IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications. The use of an RNN-LSTM network for early prediction of sepsis according to Sepsis-3 criteria is evaluated with the empirical investigation using six different time window sizes. The best result is obtained from a model using a four-hour window with the assumption that data is missing-not-at-random. It is observed that when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis, the size of the time window has a considerable impact on predictive performance.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- IoT
- Cognitive computing
- Internet of Medical Things
- Edge computing
- Early prediction
- Machine learning
- Deep learning
- Health informatics
- Healthcare analytics
- datalogi
- Computer Science
Publikations- och innehållstyp
- ref (ämneskategori)
- kap (ämneskategori)
Hitta via bibliotek
Till lärosätets databas