Diagnostic investigation has an important role in risk stratification and
clinical decision making of patients with suspected and documented Coronary
Artery Disease (CAD). However, the majority of existing tools are primarily
focused on the selection of gatekeeper tests, whereas only a handful of systems
contain information regarding the downstream testing or treatment. We propose a
multi-task deep learning model to support risk stratification and down-stream
test selection for patients undergoing Coronary Computed Tomography Angiography
(CCTA). The analysis included 14,021 patients who underwent CCTA between 2006
and 2017. Our novel multitask deep learning framework extends the state-of-the
art Perceiver model to deal with real-world CCTA report data. Our model
achieved an Area Under the receiver operating characteristic Curve (AUC) of
0.76 in CAD risk stratification, and 0.72 AUC in predicting downstream tests.
Our proposed deep learning model can accurately estimate the likelihood of CAD
and provide recommended downstream tests based on prior CCTA data. In clinical
practice, the utilization of such an approach could bring a paradigm shift in
risk stratification and downstream management. Despite significant progress
using deep learning models for tabular data, they do not outperform gradient
boosting decision trees, and further research is required in this area.
However, neural networks appear to benefit more readily from multi-task
learning than tree-based models. This could offset the shortcomings of using
single task learning approach when working with tabular data.

多任务深度学习模型用于冠状动脉计算机断层扫描（CCTA）患者风险分层和下游测试选择。模型在冠状动脉疾病风险分层和预测下游测试方面取得了良好的准确性，并能在临床实践中对风险分层和治疗带来新的变革。