Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

Abstract

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.

Keywords: AI; biomarkers; cardiovascular disease; coronary artery disease; explainable artificial intelligence; genomics; machine learning; molecular networks; multi-omics; proteomics.

Figure 1

Overview of how molecular data can be used for understanding and predicting the risk of recurrent cardiovascular events. Genome-wide association studies (GWAS) can be used to identify CVD risk loci. Weights obtained from GWAS can be used to calculate a polygenic risk score. Moreover, the GWAS loci can be combined with multi-omics data and prior knowledge to construct regulatory networks. From these networks, it is possible to extract physiological pathways and network modules, as well as associate the level of activity of distinct network regions with high or low risk. The network information and polygenic risk score can be integrated together to improve risk prediction of recurrent cardiovascular events.

Figure 2

(A) Illustration of a typical AI workflow. Each patient is first described by the same set of numerical and/or categorical attributes (features), such as risk factors or gene expression levels. The data (patients) are then divided into training, validation and test sets. AI models with different values of non-trainable parameters (hyperparameters) are trained on the training set. The model performance is evaluated on the validation set according to some metric. A final model with the hyperparameters yielding the best validation-set performance is then evaluated on the independent test set. (B) Illustration of a perceptron neural network with a sigmoid activation function. (C) Illustration of a multi-layer neural network with one hidden layer. The arrows indicate direction of feed-forward and back-propagation passes.

Figure 3

A typical clinical decision system with explainable AI. An AI model is first trained on a cohort containing for example clinical, imaging or multi-omics data. The trained model is then used to predict the risk for a patient to develop the disease or specific symptoms. Finally, the explainable AI provides information about the decision patterns, which helps the medical practitioner to assess faithfulness of the prediction and formulate a treatment strategy.