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

TL;DR

This paper introduces a generative prognostic model combining imaging and clinical data to predict treatment outcomes for intracerebral hemorrhage, addressing bias from non-randomized trials and outperforming existing methods.

Contribution

A novel variational autoencoder-based model that integrates multimodal data to improve ICH treatment outcome prediction and corrects for selection bias in observational data.

Findings

Significant improvement over state-of-the-art prediction methods.

Effective generation of prognostic scores from multimodal data.

Robustness in real-world clinical dataset evaluations.

Abstract

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