Cited by 87Healthcare Technology and Patient MonitoringData Stream Mining TechniquesArtificial Intelligence in Healthcare

Real-time analysis of physiological data to support medical applications

Apiletti, D., Baralis, E., Bruno, G., Cerquitelli, T.·2009·IEEE Transactions on Information Technology in Biomedicine

Abstract

This paper presents a flexible framework that performs real-time analysis of physiological data to monitor people's health conditions in any context (e.g., during daily activities, in hospital environments). Given historical physiological data, different behavioral models tailored to specific conditions (e.g., a particular disease, a specific patient) are automatically learnt. A suitable model for the currently monitored patient is exploited in the real-time stream classification phase. The framework has been designed to perform both instantaneous evaluation and stream analysis over a sliding time window. To allow ubiquitous monitoring, real-time analysis could also be executed on mobile devices. As a case study, the framework has been validated in the intensive care scenario. Experimental validation, performed on 64 patients affected by different critical illnesses, demonstrates the effectiveness and the flexibility of the proposed framework in detecting different severity levels of monitored people's clinical situations.

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Crossref · 10.1109/titb.2008.2010702

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References 26 · Cited by 87

Authors
Apiletti, D., Baralis, E., Bruno, G., & Cerquitelli, T.
Affiliations
Politecnico di Torino (IT)
Venue
IEEE Transactions on Information Technology in Biomedicine
Published
2009-05
Volume
13
Issue
3
Pages
313-321
ISSN
1089-7771 (ISSN-L)
OpenAlex
W2131747629
Cited by
87
References
26
Citations by year
2026: 2 · 2025: 1 · 2024: 2 · 2023: 5 · 2022: 7 · 2021: 5 · 2020: 4 · 2019: 8 · 2018: 5 · 2017: 4 · 2016: 7 · 2015: 8 · 2014: 4 · 2013: 8 · 2012: 12
FWCI
5.75
Type
journal article
Language
en
Open access
closed
Locations
  • IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
  • PubMed (publishedVersion)
  • PORTO@Iris (submittedVersion)
  • PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion)
Topics
  • Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
health monitoring, stream classification, sliding window, intensive care, ubiquitous health monitoring, mobile devices, clinical decision support
MeSH terms
Algorithms, Artificial Intelligence, Data Interpretation, Statistical, Humans, Models, Biological, Monitoring, Physiologic, Prognosis, Risk Factors, Signal Processing, Computer-Assisted, Reproducibility of Results, Cluster Analysis

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