ClinIAN: Clinically Informed Attention Network

Artificial Neural Networks (ANNs) hold promise in predicting disease severity from viral protein sequences. To gain clinical insights, the models must adhere to two key factors: first, they need to be interpretable, as using black-box models within a healthcare setting poses risks, and second, they should integrate viral and clinical features to correct for biases within the data. We propose ClinIAN (Clinically Informed Attention Network), an inherently interpretable end-to-end learning model that effectively combines clinical and sequence parameters. We evaluate ClinIAN within the context of the challenging task of predicting the severity of SARS-CoV-2 infection, but it could be applied to other medical and biological contexts, with sequence and tabular features, such as bacterial infections or cancer research.

We evaluate the performance capabilities of ClinIAN in predicting SARS-CoV-2 severity using a subset of the EuCARE hospitalized cohort. We show ClinIAN's ability to capture biologically relevant features across multiple layers of resolution while retaining stable state-of-the-art performance.

The code is available on Zenodo and on GitHub.