Search Results for author: Patrick Schwab

Found 16 papers, 8 papers with code

Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations

1 code implementation20 Jan 2022 Simon Bing, Andrea Dittadi, Stefan Bauer, Patrick Schwab

We demonstrate experimentally that HealthGen generates synthetic cohorts that are significantly more faithful to real patient EHRs than the current state-of-the-art, and that augmenting real data sets with conditionally generated cohorts of underrepresented subpopulations of patients can significantly enhance the generalisability of models derived from these data sets to different patient populations.

Time Series

GeneDisco: A Benchmark for Experimental Design in Drug Discovery

no code implementations ICLR 2022 Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab

GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.

Active Learning Drug Discovery +1

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments

no code implementations20 Mar 2021 Sonali Parbhoo, Stefan Bauer, Patrick Schwab

Estimating an individual's potential response to interventions from observational data is of high practical relevance for many domains, such as healthcare, public policy or economics.

Counterfactual Inference Representation Learning

Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation

1 code implementation29 Nov 2020 August DuMont Schütte, Jürgen Hetzel, Sergios Gatidis, Tobias Hepp, Benedikt Dietz, Stefan Bauer, Patrick Schwab

Our study offers valuable guidelines and outlines practical conditions under which insights derived from synthetic medical images are similar to those that would have been derived from real imaging data.

14 Computed Tomography (CT) +3

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

no code implementations31 Aug 2020 Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients.

Clinical Predictive Models for COVID-19: Systematic Study

no code implementations17 May 2020 Patrick Schwab, August DuMont Schütte, Benedikt Dietz, Stefan Bauer

Here, we study clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care.

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data

no code implementations2 Jan 2020 Patrick Schwab, Walter Karlen

Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms.

CXPlain: Causal Explanations for Model Interpretation under Uncertainty

2 code implementations NeurIPS 2019 Patrick Schwab, Walter Karlen

Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machine-learning models.

Feature Importance Frame

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

1 code implementation3 Feb 2019 Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy.

Model Selection

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

1 code implementation ICLR 2019 Patrick Schwab, Lorenz Linhardt, Walter Karlen

However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both.

Counterfactual Inference

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

no code implementations1 Oct 2018 Patrick Schwab, Walter Karlen

One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed.

Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

1 code implementation6 Feb 2018 Patrick Schwab, Djordje Miladinovic, Walter Karlen

Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data.

Feature Importance

Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks

no code implementations17 Oct 2017 Patrick Schwab, Gaetano Scebba, Jia Zhang, Marco Delai, Walter Karlen

With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for the continuous monitoring of patients at risk.

General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.