28 citations · 28 across the 3 of their papers we have counts for
3 papers
Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients
Thesath Nanayakkara
There has been considerable interest in leveraging RL and stochastic control methods to learn optimal treatment strategies for critically ill patients, directly from observational…
Deep Normed Embeddings for Patient Representation
Thesath Nanayakkara, Gilles Clermont, Christopher James Langmead +1
We introduce a novel contrastive representation learning objective and a training scheme for clinical time series. Specifically, we project high dimensional EHR. data to a closed u…
Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment
Thesath Nanayakkara, Gilles Clermont, Christopher James Langmead +1
Sepsis is a potentially life threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the…