Name
Title | ||
|---|---|---|
A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity. |
Abstract | ||
|---|---|---|
•The conventional COPD exacerbation detection is reformulated in terms of symptom dynamics.•Two temporal Bayesian networks are used to model the dynamics of COPD symptoms from unevenly spaced clinical time series.•Hyperparameters and evidence type should be taken into consideration in continuous-time Bayesian models. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.artmed.2018.10.002 | Artificial Intelligence in Medicine |
Keywords | Field | DocType |
Dynamic Bayesian networks,Continuous-time Bayesian networks,Point evidence,Interval evidence,Irregular time-series data,COPD | Time series,Data mining,Computer science,Multivariate statistics,Strong prior,Bayesian network,Artificial intelligence,Discrete time and continuous time,Missing data,Snapshot (computer storage),Machine learning,Dynamic Bayesian network | Journal |
Volume | ISSN | Citations |
95 | 0933-3657 | 0 |
PageRank | References | Authors |
0.34 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manxia Liu | 1 | 0 | 1.69 |
| Fabio Stella | 2 | 160 | 19.72 |
| Arjen Hommersom | 3 | 121 | 19.62 |
| Peter J. F. Lucas | 4 | 6 | 2.16 |
| Lonneke Boer | 5 | 0 | 0.34 |
| Erik Bischoff | 6 | 0 | 0.34 |