Categorization of patients' health status in COPD disease using a wearable platform and random forests methodology
Abstract
Chronic diseases are diseases of long duration and generally slow progression. Chronic Obstructive Pulmonary Disease (COPD) as one of these requires frequent examinations and hospital visits for its long-term management. CHRONIOUS, an integrated platform for chronic disease patients, provides functional information about their health status continuously, from the patients' environment. The main component of the system is the intelligent core of the wearable device that aims at the real-time characterization of the patients' health level based on the fusion of multiple data that acquired by wearable sensors, through patients smart device interface or retrieved from the specific system's database. The Decision Support System (DSS) is activated whenever new data are entered, and is functioning using by two classification methodologies composed by highly effective algorithms. These parallel classification techniques are: a rule-based expert system and a supervised Radom Forest classifier. The classification conclusion after the above analysis is twofold. The supervised technique provides with high accuracy the severity of patients' health level and the rule-based part, extracts the critical parameters or their combination which had been triggered leading in the improvement or the worsening of the patient's condition.
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Recent works in diagnosis/decision making involve estimating severity of health episodes of patients with chronic disease, sleep issues such as polysomnography and apnea, estimation and classification of health conditions, and emotion recognition.
“Some examples of recent works in this area involve estimating the severity of health episodes of patients suffering chronic disease [44–46], sleep issues such as polysomnography and apnea [47,48], estimation and classification of health conditions [49,50], and emotion recognition [51].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [46]
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An intelligent system for classification of patients suffering from chronic diseases
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
- Authors
- Bellos, C., Papadopoulos, A., Rosso, R., & Fotiadis, D. I.
- Affiliations
- Foundation for Research and Technology Hellas (GR)
- Venue
- Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics
- Publisher
- IEEE
- Published
- 2012-01
- Pages
- 404-407
- ISSN
- 2641-3590 (ISSN-L)
- OpenAlex
- W1972860702
- Cited by
- 12
- References
- 8
- Citations by year
- 2024: 1 · 2023: 2 · 2022: 2 · 2021: 1 · 2020: 1 · 2019: 1 · 2016: 2 · 2013: 2
- FWCI
- 1.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- chronic obstructive pulmonary disease, wearable sensors, COPD severity assessment, decision support system, rule-based expert systems, random forest, sensor fusion, chronic disease management
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