  Intracerebral hemorrhage (ICH) is the second most common and deadliest form
of stroke. Despite medical advances, predicting treat ment outcomes for ICH
remains a challenge. This paper proposes a novel prognostic model that utilizes
both imaging and tabular data to predict treatment outcome for ICH. Our model
is trained on observational data collected from non-randomized controlled
trials, providing reliable predictions of treatment success. Specifically, we
propose to employ a variational autoencoder model to generate a low-dimensional
prognostic score, which can effectively address the selection bias resulting
from the non-randomized controlled trials. Importantly, we develop a
variational distributions combination module that combines the information from
imaging data, non-imaging clinical data, and treatment assignment to accurately
generate the prognostic score. We conducted extensive experiments on a
real-world clinical dataset of intracerebral hemorrhage. Our proposed method
demonstrates a substantial improvement in treatment outcome prediction compared
to existing state-of-the-art approaches. Code is available at
https://github.com/med-air/TOP-GPM

Computer Vision and Pattern Recognition