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Found 20 papers, 12 papers with code
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
In drug discovery, mapping interactions between genes within cellular systems is a crucial early step.
Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function.
FED-CD: Federated Causal Discovery from Interventional and Observational Data
Existing causal discovery methods typically require the data to be available in a centralized location.
CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data
Traditional evaluations conducted on synthetic datasets do not reflect the performance in real-world systems.
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 on different units without exchanging data.
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
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.
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
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.
Learning Neural Causal Models with Active Interventions
Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science.
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
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.
Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation
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.
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
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
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
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms.
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
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.
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
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.
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
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.
PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data
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.
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Patients in the intensive care unit (ICU) require constant and close supervision.
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
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.
Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
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.
