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Intracerebral hemorrhage (ICH) is the second most common and deadliest formof stroke. Despite medical advances, predicting treat ment outcomes for ICHremains a challenge. This paper proposes a novel prognostic model that utilizesboth imaging and tabular data to predict treatment outcome for ICH. Our modelis trained on observational data collected from non-randomized controlledtrials, providing reliable predictions of treatment success. Specifically, wepropose to employ a variational autoencoder model to generate a low-dimensionalprognostic score, which can effectively address the selection bias resultingfrom the non-randomized controlled trials. Importantly, we develop avariational distributions combination module that combines the information fromimaging data, non-imaging clinical data, and treatment assignment to accuratelygenerate the prognostic score. We conducted extensive experiments on areal-world clinical dataset of intracerebral hemorrhage. Our proposed methoddemonstrates a substantial improvement in treatment outcome prediction comparedto existing state-of-the-art approaches. Code is available athttps://github.com/med-air/TOP-GPM

Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data