Patient-Specific Physiological Monitoring and Prediction Using Structured Gaussian Processes
| Autor: | Yang Yang, David A. Clifton, Tingting Zhu, Clare MacEwen, Glen Wright Colopy, Katherine E. Niehaus, Christopher W. Pugh |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Physiology Computer science patient monitoring 0206 medical engineering Bayesian probability 02 engineering and technology Novelty detection symbols.namesake pattern analysis 0202 electrical engineering electronic engineering information engineering General Materials Science Divergence (statistics) Gaussian process Warning system business.industry General Engineering 020206 networking & telecommunications Pattern recognition 020601 biomedical engineering Bayes methods Population model symbols lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 Sign (mathematics) |
| Zdroj: | IEEE Access, Vol 7, Pp 58094-58103 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2912079 |
| Popis: | The management of patient well-being can be performed by monitoring continuous time-series vital-sign data via low-cost wearable devices. Automated algorithms may then be used with the resulting data to provide early warning of deterioration of the health of an individual. Such algorithms are typically trained for a large population without considering the time-variability and inter-subject variability of the data being collected. In the case where limited numbers of subjects are available, it is difficult to create a generalized population model from a small sample size. Furthermore, some “normal” patients may exhibit different physiological patterns when compared to other “normal” patients, forming multiple “normal” clusters/subgroups. This also makes inferring a population model difficult. It is, therefore, preferable to develop patient/subgroup-specific time-series models to overcome these challenges. We propose using Bayesian hierarchical Gaussian processes to infer the hidden latent structure of the vital sign's trajectory for each individual patient or group of patients who share similar patterns. We further demonstrate the feasibility of such a model in novelty detection, using the symmetric Kullback-Leibler divergence. This allows us to identify any patterns that correspond to “normal” or “abnormal” physiology, and further classifying “abnormal” patterns from a model of “normal” latent trajectories. We tested our approach using two real datasets for different monitoring scenarios. Our model was compared to the performance of the state-of-the-art unsupervised clustering algorithms, demonstrating at least 10% improvement in accuracy. We further benchmarked against two one-class classifiers and showed at least 5% accuracy improvement when using the proposed metrics in identifying abnormal physiological episodes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|