599 citations · 1.6k across the 142 of their papers we have counts for
12 papers · 1 filter
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar
Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often const…
Risk-Stratify: Confident Stratification Of Patients Based On Risk
Kartik Ahuja, Mihaela van der Schaar
A clinician desires to use a risk-stratification method that achieves confident risk-stratification - the risk estimates of the different patients reflect the true risks with a hig…
Feature Selection for Survival Analysis with Competing Risks using Deep Learning
Carl Rietschel, Jinsung Yoon, Mihaela van der Schaar
Deep learning models for survival analysis have gained significant attention in the literature, but they suffer from severe performance deficits when the dataset contains many irre…
MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis
Trent Kyono, Fiona J. Gilbert, Mihaela van der Schaar
With an aging and growing population, the number of women requiring either screening or symptomatic mammograms is increasing. To reduce the number of mammograms that need to be rea…
Joint Concordance Index
Kartik Ahuja, Mihaela van der Schaar
Existing metrics in competing risks survival analysis such as concordance and accuracy do not evaluate a model's ability to jointly predict the event type and the event time. To ad…
Siamese Survival Analysis with Competing Risks
Anton Nemchenko, Trent Kyono, Mihaela Van Der Schaar
Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one…
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning
Bryan Lim, Mihaela van der Schaar
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. Despite the many advantages of joint mode…
Measuring the quality of Synthetic data for use in competitions
James Jordon, Jinsung Yoon, Mihaela van der Schaar
Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available. Unfortunately, much of that potential is not be…
GAIN: Missing Data Imputation using Generative Adversarial Nets
Jinsung Yoon, James Jordon, Mihaela van der Schaar
We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial I…
Optimal Piecewise Local-Linear Approximations
Kartik Ahuja, William Zame, Mihaela van der Schaar
Existing works on "black-box" model interpretation use local-linear approximations to explain the predictions made for each data instance in terms of the importance assigned to the…
Learning Optimal Policies from Observational Data
Onur Atan, William R. Zame, M van der Schaar
Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others. One approach to this problem is through con…
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Jinsung Yoon, James Jordon, Mihaela van der Schaar
Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related…