蛛网膜下腔出血
估计
医学影像学
医学
放射科
计算机科学
蛛网膜下腔出血
神经影像学
人工智能
动脉瘤
外科
工程类
系统工程
精神科
作者
Wenao Ma,Cheng Chen,Yuqi Gong,Nga Yan Chan,Meirui Jiang,Calvin Hoi-Kwan Mak,Jill Abrigo,Qi Dou
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2024-04-18
卷期号:43 (8): 2778-2789
标识
DOI:10.1109/tmi.2024.3390812
摘要
Aneurysmal subarachnoid hemorrhage is a medical emergency of brain that has high mortality and poor prognosis. Causal effect estimation of treatment strategies on patient outcomes is crucial for aneurysmal subarachnoid hemorrhage treatment decision-making. However, most existing studies on treatment decision-making support of this disease are unable to simultaneously compare the potential outcomes of different treatments for a patient. Furthermore, these studies fail to harmoniously integrate the imaging data with non-imaging clinical data, both of which are useful in clinical scenarios. In this paper, we estimate the causal effect of various treatments on patients with aneurysmal subarachnoid hemorrhage by integrating plain CT with non-imaging clinical data, which is represented using structured tabular data. Specifically, we first propose a novel scheme that uses multi-modality confounders distillation architecture to predict the treatment outcome and treatment assignment simultaneously. With these distilled confounder features, we design an imaging and non-imaging interaction representation learning strategy to use the complementary information extracted from different modalities to balance the feature distribution of different treatment groups. We have conducted extensive experiments using a clinical dataset of 656 subarachnoid hemorrhage cases, which was collected from the Hospital Authority Data Collaboration Laboratory in Hong Kong. Our method shows consistent improvements on the evaluation metrics of treatment effect estimation, achieving state-of-the-art results over strong competitors. Code is released at https://github.com/med-air/TOP-aSAH.
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