Paper Info
Title
Generation Of Virtual Patient Data For In-Silico Cardiomyopathies Drug Development Using Tree Ensembles: A Comparative Study
Abstract
In-silico clinical platforms have been recently used as a new revolutionary path for virtual patients (VP) generation and further analysis, such as, drug development. Advanced individualized models have been developed to enhance flexibility and reliability of the virtual patient cohorts. This study focuses on the implementation and comparison of three different methodologies for generating virtual data for in-silico clinical trials. Towards this direction, three computational methods, namely: (i) the multivariate log-normal distribution (log-MVND), (ii) the supervised tree ensembles, and (iii) the unsupervised tree ensembles are deployed and evaluated against their performance towards the generation of high-quality virtual data using the goodness of fit (gof) and the dataset correlation matrix as performance evaluation measures. Our results reveal the dominance of the tree ensembles towards the generation of virtual data with similar distributions (gof values less than 0.2) and correlation patterns (average difference less than 0.03).
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9176567
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
Keywords
DocType
Volume
virtual population generation, cardiomyopathy, in-silico clinical trials, log-MVND, tree ensembles
Conference
2020
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Vasileios C Pezoulas102.03
Grigorios I Grigoriadis200.68
Nikolaos S Tachos301.35
Fausto Barlocco401.01
Iacopo Olivotto501.35
Dimitrios I. Fotiadis6941121.32