Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model
Open accessAbstract
Rapid advances in ICT and collection of large amount of mobile health data are giving room to new ways of treating patients. Studies suggest that telemonitoring systems and predictive models for clinical support and patient empowerment may improve several pathologies, such as heart failure, which admissions rate is high. In the current medical practice, clinicians make use of simple rules that generate large number of false alerts. In order to reduce the false alerts, in this study, the predictive models to prevent decompensations that may lead into admissions are presented. They are based on mobile clinical data of 242 heart failure (HF) patients collected for a period of 44 months in the public health service of Basque Country (Osakidetza). The best predictive model obtained is a combination of alerts based on monitoring data and a questionnaire with a Naive Bayes classifier using Bernoulli distribution. This predictive model performs with an AUC = 67% and reduces the false alerts per patient per year from 28.64 to 7.8. This way, the system predicts the risk of admission of ambulatory patients with higher reliability than current alerts.
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Larburu et al.'s predictive models were based on clinical data from 242 heart failure patients' mobile accumulated over 44 months.
“These predictive models are based on clinical data taken from 242 heart failure patients' mobile accumulated in 44 months.”
From Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective (2020) · cites this paper as [96]
Larburu et al. proposed an m-health application based on AI for avoiding heart failures in patients.
“Larburu et al. in [96] proposed an m-health application based on artificial intelligence for avoiding heart failures in patients.”
From Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective (2020) · cites this paper as [96]
Larburu et al.'s proposed model lowered false alerts for a patient for a year from 28.64 to 7.8 gradually.
“This proposed model lowered the false alerts for a patient for a year from 28.64 to 7.8 gradually.”
From Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective (2020) · cites this paper as [96]
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Cited by
Sorted by citations
khan, Z. F., & Alotaibi, S. R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8894694 · Cited by 235 · see citing works
- Authors
- Larburu, N., Artetxe, A., Escolar, V., Lozano, A., & Kerexeta, J.
- Affiliations
- Vicomtech (ES)
- Hospital de Basurto (ES)
- Hospital Universitario de Basurto (Osakidetza Health Care System), Avda. Montevideo 18, 48013 Bilbao, Spain
- Corresponding author
- Larburu, Nekane
- Venue
- Mobile Information Systems
- Publisher
- Wiley
- Published
- 2018-11-05
- Volume
- 2018
- Pages
- 1-11
- ISSN
- 1574-017X (ISSN-L)
- 1875-905X
- OpenAlex
- W2899937116
- Cited by
- 35
- References
- 29
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 2 · 2023: 6 · 2022: 4 · 2021: 6 · 2020: 5 · 2019: 1
- FWCI
- 3.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Mobile Information Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Basque Government (2017222015)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- heart failure, remote patient monitoring, mobile health, predictive modeling, false alarms, hospitalization risk, Naive Bayes classifier, Bernoulli distribution, outpatients, real-time monitoring
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