Interpolation-prediction networks for irregularly sampled time series
In this paper, we present a new deep learning architecture for addressing the problem of
supervised learning with sparse and irregularly sampled multivariate time series. The …
supervised learning with sparse and irregularly sampled multivariate time series. The …
Health informatics via machine learning for the clinical management of patients
Objectives: To review how health informatics systems based on machine learning methods
have impacted the clinical management of patients, by affecting clinical practice. Methods …
have impacted the clinical management of patients, by affecting clinical practice. Methods …
Variational inference for Gaussian process modulated Poisson processes
We present the first fully variational Bayesian inference scheme for continuous Gaussian-
process-modulated Poisson processes. Such point processes are used in a variety of …
process-modulated Poisson processes. Such point processes are used in a variety of …
Finite-dimensional Gaussian approximation with linear inequality constraints
Introducing inequality constraints in Gaussian processes can lead to more realistic
uncertainties in learning a great variety of real-world problems. We consider the finite …
uncertainties in learning a great variety of real-world problems. We consider the finite …
Predicting medications from diagnostic codes with recurrent neural networks
JM Bajor, TA Lasko - International conference on learning …, 2022 - openreview.net
It is a surprising fact that electronic medical records are failing at one of their primary
purposes, that of tracking the set of medications that the patient is actively taking. Studies …
purposes, that of tracking the set of medications that the patient is actively taking. Studies …
A multitask point process predictive model
Point process data are commonly observed in fields like healthcare and social science.
Designing predictive models for such event streams is an under-explored problem, due to …
Designing predictive models for such event streams is an under-explored problem, due to …
Fast and flexible temporal point processes with triangular maps
Temporal point process (TPP) models combined with recurrent neural networks provide a
powerful framework for modeling continuous-time event data. While such models are …
powerful framework for modeling continuous-time event data. While such models are …
Predicting complications in critical care using heterogeneous clinical data
Patients in hospitals, particularly in critical care, are susceptible to many complications
affecting morbidity and mortality. Digitized clinical data in electronic medical records can be …
affecting morbidity and mortality. Digitized clinical data in electronic medical records can be …
Nonparametric regressive point processes based on conditional gaussian processes
S Liu, M Hauskrecht - Advances in neural information …, 2019 - proceedings.neurips.cc
Real-world event sequences consist of complex mixtures of different types of events
occurring in time. An event may depend on past events of the same type, as well as, the …
occurring in time. An event may depend on past events of the same type, as well as, the …
Detailed temporal structure of communication networks in groups of songbirds
Animals in groups often exchange calls, in patterns whose temporal structure may be
influenced by contextual factors such as physical location and the social network structure of …
influenced by contextual factors such as physical location and the social network structure of …