Patrick Schwab | DeepAI

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∙ 06/15/2023

Multi-omics Prediction from High-content Cellular Imaging with Deep Learning

High-content cellular imaging, transcriptomics, and proteomics data prov...

0 Rahil Mehrizi, et al. ∙

∙ 11/07/2022

FED-CD: Federated Causal Discovery from Interventional and Observational Data

Causal discovery, the inference of causal relations from data, is a core...

0 Amin Abyaneh, et al. ∙

∙ 10/31/2022

CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data

Mapping biological mechanisms in cellular systems is a fundamental step ...

0 Mathieu Chevalley, et al. ∙

∙ 04/20/2022

Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database

Federated learning (FL) has been proposed as a method to train a model o...

0 Arash Mehrjou, et al. ∙

∙ 01/20/2022

Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations

The widespread adoption of electronic health records (EHRs) and subseque...

0 Simon Bing, et al. ∙

∙ 10/22/2021

GeneDisco: A Benchmark for Experimental Design in Drug Discovery

In vitro cellular experimentation with genetic interventions, using for ...

19 Arash Mehrjou, et al. ∙

∙ 09/06/2021

Learning Neural Causal Models with Active Interventions

Discovering causal structures from data is a challenging inference probl...

7 Nino Scherrer, et al. ∙

∙ 03/20/2021

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments

Estimating an individual's potential response to interventions from obse...

0 Sonali Parbhoo, et al. ∙

∙ 08/31/2020

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease c...

0 Patrick Schwab, et al. ∙

∙ 05/17/2020

predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019

Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory di...

0 Patrick Schwab, et al. ∙

∙ 01/02/2020

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data

Multiple sclerosis (MS) affects the central nervous system with a wide r...

0 Patrick Schwab, et al. ∙

∙ 10/27/2019

CXPlain: Causal Explanations for Model Interpretation under Uncertainty

Feature importance estimates that inform users about the degree to which...

18 Patrick Schwab, et al. ∙

∙ 02/03/2019

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

Estimating what would be an individual's potential response to varying l...

4 Patrick Schwab, et al. ∙

∙ 10/01/2018

Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks

Learning representations for counterfactual inference from observational...

0 Patrick Schwab, et al. ∙

∙ 10/01/2018

PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

Parkinson's disease is a neurodegenerative disease that can affect a per...

0 Patrick Schwab, et al. ∙

∙ 02/14/2018

Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care

Patients in the intensive care unit (ICU) require constant and close sup...

0 Patrick Schwab, et al. ∙

∙ 02/06/2018

Granger-causal Attentive Mixtures of Experts

Several methods have recently been proposed to detect salient input feat...

0 Patrick Schwab, et al. ∙

∙ 10/17/2017

Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks

With tens of thousands of electrocardiogram (ECG) records processed by m...

0 Patrick Schwab, et al. ∙

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