How artificial intelligence and machine learning can help healthcare systems respond to COVID-19
The COVID-19 global pandemic is a threat not only to the health of millions of individuals,
but also to the stability of infrastructure and economies around the world. The disease will …
but also to the stability of infrastructure and economies around the world. The disease will …
Hyperimpute: Generalized iterative imputation with automatic model selection
Consider the problem of imputing missing values in a dataset. One the one hand,
conventional approaches using iterative imputation benefit from the simplicity and …
conventional approaches using iterative imputation benefit from the simplicity and …
Natural product-based studies for the management of castration-resistant prostate cancer: Computational to clinical studies
Background: With prostate cancer being the fifth-greatest cause of cancer mortality in 2020,
there is a dire need to expand the available treatment options. Castration-resistant prostate …
there is a dire need to expand the available treatment options. Castration-resistant prostate …
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks
We describe a new approach to estimating relative risks in time-to-event prediction problems
with censored data in a fully parametric manner. Our approach does not require making …
with censored data in a fully parametric manner. Our approach does not require making …
Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and …
Background Accurate prognostication is crucial in treatment decisions made for men
diagnosed with non-metastatic prostate cancer. Current models rely on prespecified …
diagnosed with non-metastatic prostate cancer. Current models rely on prespecified …
Deep cox mixtures for survival regression
C Nagpal, S Yadlowsky… - Machine Learning …, 2021 - proceedings.mlr.press
Survival analysis is a challenging variation of regression modeling because of the presence
of censoring, where the outcome measurement is only partially known, due to, for example …
of censoring, where the outcome measurement is only partially known, due to, for example …
Survival cluster analysis
Conventional survival analysis approaches estimate risk scores or individualized time-to-
event distributions conditioned on covariates. In practice, there is often great population …
event distributions conditioned on covariates. In practice, there is often great population …
Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering
Survival analysis involves the modelling of the times to event. Proposed neural network
approaches maximise the predictive performance of traditional survival models at the cost of …
approaches maximise the predictive performance of traditional survival models at the cost of …
CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19
Abstract The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of
overwhelming healthcare systems with unprecedented demands for intensive care …
overwhelming healthcare systems with unprecedented demands for intensive care …
Enabling counterfactual survival analysis with balanced representations
Balanced representation learning methods have been applied successfully to counterfactual
inference from observational data. However, approaches that account for survival outcomes …
inference from observational data. However, approaches that account for survival outcomes …