Correction: Anastopoulos et al. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600.

In the original publication [...].

Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events.

While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interactions that can happen in real world diverse patient populations. With a growing abundance of real-world evidence databases containing hundreds of thousands of patient records, it is now feasible to build machine learning models that incorporate individual patient information to provide personalized adverse event predictions. In this study, we build models that integrate patient specific demographic, clinical, and genetic features (when available) with drug structure to predict adverse drug reactions. We develop an extensible graph convolutional approach to be able to integrate molecular effects from the variable number of medications a typical patient may be taking. Our model outperforms standard machine learning methods at the tasks of predicting hospitalization and death in the UK Biobank dataset yielding an R2 of 0.37 and an AUC of 0.90, respectively. We believe our model has potential for evaluating new therapeutic compounds for individualized toxicities in real world diverse populations. It can also be used to prioritize medications when there are multiple options being considered for treatment.

The dynamics of interacting folds under biaxial compressive stresses.

The gradual in-plane compression of a solid film bonded to a soft substrate can lead to surface wrinkling and even to the formation of a network of folds for sufficiently high strain. An understanding of how these folds initiate, propagate, and interact with each other is still lacking. In a previous study, we developed an experimental system to observe the wrinkle-to-fold transition of layered elastic materials under biaxial compressive stresses. Here we focus on the dynamic interaction of a pair of propagating folds under biaxial compression. We find experimentally that their behavior is mediated through their tips and depends on the separation of the tips and their angle of interception. When the angle is lower than 45°, the two folds either form a unique fold by the coalescence of their tips when close enough, or bend their trajectories to intersect each other and form a lenticular region in analogy with cracks. When the angle is higher then 45°, the folds simply intersect and form a T-like junction. We rationalize this behavior by conducting numerical simulations to visualize the stress field around the two tips and find that the initial geometric position of the tips primarily determines the final state of the folds.