Name
Abstract | ||
|---|---|---|
•We improve an Expectation-Maximisation method to stratify drug responses of patients. With this we aim to provide different drug doses for each stratum and so, maximise therapy efficacy while minimising its toxicity.•Two novel model selection criteria, based on the Minimum Description Length and the Normalized Maximum Likelihood, were derived and developed for clustering pharmacokinetic (PK) responses.•The method was evaluated over synthetic and real data and showed the ability to unveil the correct number of clusters underlying the mixture of PK curves.•A cost-efficient parallel implementation in Java is publicly and freely available in a GitHub repository, along with a user manual and data used in the experiments. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.cmpb.2018.05.002 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Clustering,Model selection,Minimum description length,Normalised maximum likelihood,Pharmacokinetics | Heuristic,Drug doses,Computer science,Pharmacokinetics,Minimum description length,Maximum likelihood,Model selection,Artificial intelligence,Statistics,Cluster analysis,Machine learning | Journal |
Volume | ISSN | Citations |
162 | 0169-2607 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Guerra | 1 | 1 | 1.37 |
| Alexandra M. Carvalho | 2 | 223 | 16.39 |
| Paulo Mateus | 3 | 33 | 4.55 |