Estimating latent degradation states of mechanical systems from observation data provide
the basis for their prognostic and health management (PHM). Recently, deep learning …

Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a
labeled source domain that performs well on a similar yet different, unlabeled target domain …

In digital medicine, patient data typically record health events over time (eg, through
electronic health records, wearables, or other sensing technologies) and thus form unique …

Electronic health records (EHRs) contain diverse patient information, including medical
notes, clinical events, and laboratory test results. Integrating this multimodal data can …

Estimating heterogeneous treatment effects is an important problem across many domains.
In order to accurately estimate such treatment effects, one typically relies on data from …

Treatment effect estimation in continuous time is crucial for personalized medicine.
However, existing methods for this task are limited to point estimates of the potential …

Learning personalized decision policies that generalize to the target population is of great
relevance. Since training data is often not representative of the target population, standard …

Clinical risk prediction based on Electronic Health Records (EHR) can assist doctors in
better judgment and can make sense of early diagnosis. However, the prediction …

The COVID-19 pandemic has highlighted the critical need for advanced technology in
healthcare. Clinical Decision Support Systems (CDSS) utilizing Artificial Intelligence (AI) …

Estimating individualized treatment effects (ITEs) from observational data is crucial for
decision-making. In order to obtain unbiased ITE estimates, a common assumption is that all …