Correlated gamma‐based hidden Markov model for the smart asthma management based on rescue inhaler usage
| Autor: | Shiyu Zhou, Junbo Son, Patricia Flatley Brennan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Epidemiology Remote patient monitoring Population 0211 other engineering and technologies 02 engineering and technology Biostatistics 01 natural sciences 010104 statistics & probability Administration Inhalation Health care Humans Medicine Computer Simulation Anti-Asthmatic Agents 0101 mathematics education Hidden Markov model Simulation Monitoring Physiologic Asthma Likelihood Functions education.field_of_study Models Statistical 021103 operations research business.industry Nebulizers and Vaporizers Disease Management medicine.disease Health indicator Data science Markov Chains 3. Good health Remote Sensing Technology Key (cryptography) Regression Analysis Smartphone Mobile telephony business |
| Zdroj: | Statistics in Medicine. 36:1619-1637 |
| ISSN: | 1097-0258 0277-6715 |
| DOI: | 10.1002/sim.7214 |
| Popis: | Asthma is a very common chronic disease that affects a large portion of population in many nations. Driven by the fast development in sensor and mobile communication technology, a smart asthma management system has become available to continuously monitor the key health indicators of asthma patients. Such data provides opportunities for healthcare practitioners to examine patients not only in the clinic (on-site) but also outside of the clinic (off-site) in their daily life. In this paper, taking advantage from this data availability, we propose a correlated gamma-based hidden Markov model framework, which can reveal and highlight useful information from the rescue inhaler-usage profiles of individual patients for practitioners. The proposed method can provide diagnostic information about the asthma control status of individual patients and can help practitioners to make more informed therapeutic decisions accordingly. The proposed method is validated through both numerical study and case study based on real world data. Copyright © 2017 John Wiley & Sons, Ltd. |
| Databáze: | OpenAIRE |
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