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I am a PhD student in the MRC BSU at the University of Cambridge. I am working on the project Clinical prediction under informative presence: Exploring what patterns and timing of repeated observations in routinely collected data can tell us about disease risk under the supervision of Dr Jessica Barrett and Dr Brian Tom.

My research interest lies in developing machine learning models for temporal data with a particular focus on inequalities in medical care access and delivery.


Publications

Papers

Constrained clustering and multiple kernel learning without pairwise constraint relaxation
June 2022
Springer - Advances in Data Analysis and Classification
B. Boecking, V. Jeanselme, A. Dubrawski
Code: Github
Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering
April 2022
Conference on Health, Inference, and Learning (CHIL)
V. Jeanselme, B. Tom, J. Barrett
Code: Github
Sex differences in post cardiac arrest discharge locations
December 2021
Resuscitation Plus
V. Jeanselme, M. De-Arteaga, J. Elmer, S. M.Perman, A. Dubrawski
Deep Parametric Time-to-Event Regression with Time-Varying Covariates
April 2021
AAAI Spring Symposium on Survival Analysis
C. Nagpal*, V. Jeanselme*, A. Dubrawski
Prediction of Hypotension Events with Physiologic Vital Sign Signatures in The Intensive Care Unit
August 2020
Critical Care
J. H. Yoon*, V. Jeanselme*, A. Dubrawski, M. Hravnak, M.R. Pinsky, G. Clermont

Preprint

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness
August 2022
V. Jeanselme, M. De-Arteaga, Z. Zhang, J. Barrett, B. Tom
Code: Github
Leveraging Expert Consistency to Improve Algorithmic Decision Support
July 2022
M. De-Arteaga, V. Jeanselme, A. Dubrawski, A. Chouldechova
DeepJoint: Robust Survival Modelling Under Clinical Presence Shift
May 2022
V. Jeanselme, G. Martin, M. Sperrin, N. Peek, B. Tom, J. Barrett
Code: Github

Extended Abstracts

Using observation processes to predict survival: A deep learning ap- proach to joint modelling
IBC - July 2022
V. Jeanselme, G. Martin, M. Sperrin, N. Peek, B. Tom, J. Barrett
Dynamic Phenotypes Preceding Hypotension in Intensive Care
ESICM - October 2021
V. Jeanselme, A. Dubrawski, M. R. Pinsky, G. Clermont, J. H. Yoon
A Case For Federated Learning: Enabling And Leveraging Inter-hospital Collaboration
ATS - Philadelphia, PA, USA - May 2020
S. Caldas, V. Jeanselme, G. Clermont, M. R. Pinsky and A. Dubrawski
Robustness Of Machine Learning Models For Hemorrhage Detection
ATS - Philadelphia, PA, USA - May 2020
V. Jeanselme, A. Wertz, G. Clermont, M. R. Pinsky and A. Dubrawsk
Cross-correlation Features of Vital Signs Enable Robust Detection of Hemorrhage
ISICEM - Brussels, Belgium - Mar. 2020
V. Jeanselme, A. Wertz, G. Clermont, M. R. Pinsky and A. Dubrawsk
Predicting Hypotension Episode With Numerical Vital Sign Signals in the Intensive Care Unit
ISICEM - Brussels, Belgium - Mar. 2019
J. H. Yoon, V. Jeanselme, A. Dubrawski, M. Hravnak, M. R. Pinsky and G. Clermont
A Real-time Photoplethysmography Signal Artifacts Removal System in ICU
ESICM - Paris, France - Oct. 2018
Y. Chen, J. H. Yoon, V. Jeanselme, M. Hravnak, A. Dubrawski, M. R. Pinsky and G. Clermont
Prediction for Hypotension Episode with Multigranular Data in the Intensive Care Unit
ATS - San Diego, CA, USA - May 2018
Received ATS Abstract Scholarship Award
J. H. Yoon, V. Jeanselme, Y. Chen, A. Dubrawski, M. Hravnak, M. R. Pinsky and G. Clermont

Talks

Using observation processes to predict survival: A deep learning approach to joint modelling
IBC - July 2022
V. Jeanselme, G. Martin, M. Sperrin, N. Peek, B. Tom, J. Barrett
Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering
CHIL - April 2022
V. Jeanselme, B. Tom, J. Barrett
Deep Parametric Time-to-Event Regression with Time-Varying Covariates
AAAI Spring Symposium on Survival Analysis - April 2021
C. Nagpal*, V. Jeanselme*, A. Dubrawski

Teaching

AI4ALL

I mentored highschool students during AI4ALL summer programms. In order to learn the basics of machine learning and programming in python, I created the House Pricing Project using data from Kaggle.

Teacher Assistant

I have been teacher assistant for Applied Data Science at Carnegie Mellon University (Spring 2020) and for Principles of Biostatistics at Cambridge (Fall 2021).

Visualization

Visalization is essential to understand the data at hand. I created a simple tool to visualize transformation from orignal space, to TSNE and PCA.

Clustering for labeled data can also be visualized with this tool which allows to display the hierachy given the class distribution in subbranches.

I also had some interest in visualizing sound


Open Source

Reproducibility is key in science, I publish the majority of my code in order to allow other researchers to make further analyses and help me to improve my code and findings.

A few tools that I use:

Some projects that I wanted to work on:

I have also implemented code for a few papers that I find particularly interesting:

And also some analysis of Kaggle datasets:


Contact

If you are interested in working with me, send me an email at vincent.jeanselme@gmail.com or contact me @JeanselmeV