4 papers
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group
Cédric Beaulac, Jeffrey S. Rosenthal, Qinglin Pei +3
In this manuscript we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were co…
A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis
Cédric Beaulac, Jeffrey S. Rosenthal, David Hodgson
In the following short article we adapt a new and popular machine learning model for inference on medical data sets. Our method is based on the Variational AutoEncoder (VAE) framew…
BEST : A decision tree algorithm that handles missing values
Cédric Beaulac, Jeffrey S. Rosenthal
The main contribution of this paper is the development of a new decision tree algorithm. The proposed approach allows users to guide the algorithm through the data partitioning pro…
Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agents Location Using Hidden Markov Models
Cédric Beaulac, Fabrice Larribe
We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that…