90 citations · 428 across the 38 of their papers we have counts for
41 papers
Learning Matching Representations for Individualized Organ Transplantation Allocation
Can Xu, Ahmed M. Alaa, Ioana Bica +3
Organ transplantation is often the last resort for treating end-stage illness, but the probability of a successful transplantation depends greatly on compatibility between donors a…
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev +1
Devising domain- and model-agnostic evaluation metrics for generative models is an important and as yet unresolved problem. Most existing metrics, which were tailored solely to the…
Estimating Structural Target Functions using Machine Learning and Influence Functions
Alicia Curth, Ahmed M. Alaa, Mihaela van der Schaar
We aim to construct a class of learning algorithms that are of practical value to applied researchers in fields such as biostatistics, epidemiology and econometrics, where the need…
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19
Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar
The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these…
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Ahmed M. Alaa, Mihaela van der Schaar
Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging. Usable estimate…
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Ahmed M. Alaa, Mihaela van der Schaar
Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data. Yet, when using RNNs to inform decision-making, predictions by themselves are not su…
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian +1
Modern neural networks have proven to be powerful function approximators, providing state-of-the-art performance in a multitude of applications. They however fall short in their ab…
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar
The coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures in order to slow down the outbreak. Questions on whether go…
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
Ioana Bica, Ahmed M. Alaa, James Jordon +1
Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, w…
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot +2
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has,…
Lifelong Bayesian Optimization
Yao Zhang, James Jordon, Ahmed M. Alaa +1
Automatic Machine Learning (Auto-ML) systems tackle the problem of automating the design of prediction models or pipelines for data science. In this paper, we present Lifelong Baye…
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
Ioana Bica, Ahmed M. Alaa, Mihaela van der Schaar
The estimation of treatment effects is a pervasive problem in medicine. Existing methods for estimating treatment effects from longitudinal observational data assume that there are…
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
Ahmed M. Alaa, Mihaela van der Schaar
Disease progression models are instrumental in predicting individual-level health trajectories and understanding disease dynamics. Existing models are capable of providing either a…
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Ahmed M. Alaa, Mihaela van der Schaar
Clinical prognostic models derived from largescale healthcare data can inform critical diagnostic and therapeutic decisions. To enable off-theshelf usage of machine learning (ML) i…
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms
Ahmed M. Alaa, Mihaela van der Schaar
We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data, this is a central problem in various application domains,…
Deep Counterfactual Networks with Propensity-Dropout
Ahmed M. Alaa, Michael Weisz, Mihaela van der Schaar
We propose a novel approach for inferring the individualized causal effects of a treatment (intervention) from observational data. Our approach conceptualizes causal inference as a…
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Ahmed M. Alaa, Mihaela van der Schaar
Predicated on the increasing abundance of electronic health records, we investi- gate the problem of inferring individualized treatment effects using observational data. Stemming f…
Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model
Ahmed M. Alaa, Jinsung Yoon, Scott Hu +1
We report the development and validation of a data-driven real-time risk score that provides timely assessments for the clinical acuity of ward patients based on their temporal lab…
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis
Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar
Critically ill patients in regular wards are vulnerable to unanticipated adverse events which require prompt transfer to the intensive care unit (ICU). To allow for accurate progno…
Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors
J. Yoon, W. R. Zame, A. Banerjee +3
Given the limited pool of donor organs, accurate predictions of survival on the wait list and post transplantation are crucial for cardiac transplantation decisions and policy. How…
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
Ahmed M. Alaa, Mihaela van der Schaar
Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we deve…
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data
Ahmed M. Alaa, Jinsung Yoon, Scott Hu +1
Critically ill patients in regular wards are vulnerable to unanticipated clinical dete- rioration which requires timely transfer to the intensive care unit (ICU). To allow for risk…
Personalized Donor-Recipient Matching for Organ Transplantation
Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras +1
Organ transplants can improve the life expectancy and quality of life for the recipient but carries the risk of serious post-operative complications, such as septic shock and organ…
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes
Ahmed M. Alaa, Jinsung Yoon, Scott Hu +1
Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on…