Name
Title | ||
|---|---|---|
Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes |
Abstract | ||
|---|---|---|
•Dynamic Bayesian networks reveal trajectories to COVID-19 outcomes.•We obtain conditional probability maps over time and visualise trajectories.•Trajectories visualised via resampling, dynamic time warping, and prototyping.•Kidney dysfunction and cardiac damage are crucial links to outcomes.•Death was linked to elevated procalcitonin and D-dimer levels. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.cmpb.2022.106873 | Computer Methods and Programs in Biomedicine |
Keywords | DocType | Volume |
COVID-19,Mortality,Dynamic Bayesian network,Dynamic time warping,Graphical model | Journal | 221 |
ISSN | Citations | PageRank |
0169-2607 | 0 | 0.34 |
References | Authors | |
1 | 14 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrico Longato | 1 | 1 | 2.41 |
| Mario Luca Morieri | 2 | 0 | 0.34 |
| Giovanni Sparacino | 3 | 0 | 0.34 |
| Barbara Di Camillo | 4 | 0 | 0.34 |
| Annamaria Cattelan | 5 | 0 | 0.34 |
| Sara Lo Menzo | 6 | 0 | 0.34 |
| Marco Trevenzoli | 7 | 0 | 0.34 |
| Andrea Vianello | 8 | 0 | 0.34 |
| Gabriella Guarnieri | 9 | 0 | 0.34 |
| Federico Lionello | 10 | 0 | 0.34 |
| Angelo Avogaro | 11 | 0 | 0.34 |
| Paola Fioretto | 12 | 0 | 0.34 |
| Roberto Vettor | 13 | 0 | 0.34 |
| Gian Paolo Fadini | 14 | 0 | 0.34 |