5 citations · 6 across the 3 of their papers we have counts for
5 papers · 1 filter
Learning predictive checklists from continuous medical data
Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan
Checklists, while being only recently introduced in the medical domain, have become highly popular in daily clinical practice due to their combined effectiveness and great interpre…
Predicting the impact of treatments over time with uncertainty aware neural differential equations
Edward De Brouwer, Javier González Hernández, Stephanie Hyland
Predicting the impact of treatments from observational data only still represents a majorchallenge despite recent significant advances in time series modeling. Treatment assignment…
Longitudinal modeling of MS patient trajectories improves predictions of disability progression
Edward De Brouwer, Thijs Becker, Yves Moreau +38
Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during…
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
Edward De Brouwer, Jaak Simm, Adam Arany +1
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimension…
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification
Edward De Brouwer, Jaak Simm, Adam Arany +1
We present a generative approach to classify scarcely observed longitudinal patient trajectories. The available time series are represented as tensors and factorized using generati…