Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative
  Prognostic Model with Imaging and Tabular Data
Wenao Ma Cheng Chen Jill Abrigo Calvin Hoi-Kwan Mak Yuqi Gong Nga Yan Chan Chu Han Zaiyi Liu Qi Dou
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