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
this https URL

该研究论文提出了一种利用成像和表格数据预测脑出血治疗结果的新型预测模型。作者通过观察性数据训练模型，提供了可靠的治疗成功预测，使用变分自编码器模型生成低维预测得分，以有效解决非随机对照试验产生的选择偏倚。实验结果表明，该方法在治疗结果预测方面相较现有方法有显著改进。