The avalanche of new data generated by these gadgets will enable carriers to understand their customers better, leading to new product categories, more tailored pricing, and increasingly real-time service delivery. FREMONT, CA: The disruption caused by COVID-19 shifted the timetables for AI adoption by considerably speeding up insurers' digitalization. The underlying AI technologies are already in use in the workplaces, homes, vehicles, and bodies. Organizations must react almost immediately to accommodate remote workers, extend their digital capabilities to facilitate distribution, and modernize their web channels. While most firms did not engage extensively in AI during the epidemic, the increased emphasis on digital technology and a more substantial openness to embracing change will enable them to integrate AI into their operations.

T-cell immunomodulatory protein homolog, from the AlphaFold Protein Structure Database, reproduced under a CC-BY-4.0 license. DeepMind and the European Molecular Biology Laboratory (EMBL) have partnered to produce a database of predicted protein structure models. The first release covers all 20,000 proteins expressed in the human proteome, and the proteomes of 20 other biologically significant organisms, totalling over 350k structures. In the coming months they plan to expand the database to cover a large proportion of all catalogued proteins (the over 100 million in UniRef90). The data is freely and openly available to the scientific community. You can access the AlphaFold Protein Structure Database here.

Activation functions are a critical part of the design of a neural network. The choice of activation function in the hidden layer will control how well the network model learns the training dataset. The choice of activation function in the output layer will define the type of predictions the model can make. As such, a careful choice of activation function must be made for each deep learning neural network project. In this tutorial, you will discover how to choose activation functions for neural network models.

Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG signal directly. Since intelligent multimodal fusion can perform at the stateof-the-art level with an efficient deep network, therefore, in this paper, we propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification called Multimodal Image Fusion (MIF) and Multimodal Feature Fusion (MFF). At the input of these frameworks, we convert the raw ECG data into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF). In MIF, we first perform image fusion by combining three imaging modalities to create a single image modality which serves as input to the Convolutional Neural Network (CNN).

For data scientists, a key part of interpreting machine learning models is understanding which factors impact predictions. In order to effectively use machine learning in their decision-making processes, companies need to know which factors are most important. For example, if a company wants to predict the likelihood of customer churn, it might also want to know what exactly drives a customer to leave a company. In this example, the model might indicate that customers who purchase products that rarely go on sale are much more likely to stop purchasing. Armed with this knowledge, a company can make smarter pricing decisions in the future.

This trend also brings about a unique opportunity and good assurance for solving different critical problems in medical and healthcare systems as well as engineering applications of Artificial Intelligence (AI) and Operations Research (OR). However, such an assurance strongly depends on the extent to which researchers can discover useful patterns, find informative mechanisms underlying the fragmented and diverse data sets, as well as convert this knowledge into intelligent decisions. AI techniques have been recently studied and applied as promising tools for the development and application of intelligent systems in the healthcare context. AI-based systems can generally learn from data and evolve according to real-time changes and fluctuations by considering the indisputable uncertainty of health data and processes. Many attempts have been made so far that employ different techniques including, inter alia, Machine Learning (ML), neural networks, optimization, computational intelligence and human–machine interface.

While solving classification problems using deep learning models, we use cross entropy to tell the model how good or bad it's predictions are during training. What is this cross entropy loss? Cross entropy in a way can be looked as the difference between 2 probability distributions in the case of supervised learning with one-hot encoded labels. Let's say we are trying to classify an input between 3 categories. It is okay if you don't understand this next piece of code, this is just to show us the cross entropy value. If we consider the probabilities as 2 vectors and find the squared distance between them, we get the L2 loss.

In this article, we will understand why graphical data are essential and how they can be processed with graph neural networks, and we will see how they are used in drug repositioning. The unique capability of graphs enables capturing the structural relations among data and thus allows for harvesting more insights than analyzing data in isolation. Graphs are among the most versatile data structures. They naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. Euclidean data is modeled as being plotted in n-dimensional linear space.

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance remains still unsatisfactory. To further cope with these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations: 1) Since the discrimination of features learned via square convolutional kernels needs to be further improved, we propose to utilize non-square vertical and horizontal convolutional kernels in the double-branch encoder, so features learned by the two branches can be expected to complement each other. 2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets. Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net. The experiments validate the effectiveness of our model on four datasets. The code is released at https://github.com/Qianyu1226/Crosslink-Net.

Modeling the dynamics of epidemics helps to propose control strategies based on pharmaceuticaland non-pharmaceutical interventions (contact limitation, lockdown, vaccination,etc). Hand-designing such strategies is not trivial because of the number of possibleinterventions and the difficulty to predict long-term effects. This task can be cast as an optimization problem where state-of-the-art machine learning methods such as deep reinforcement learning might bring significant value. However, the specificity of each domain|epidemic modeling or solving optimization problems|requires strong collaborationsbetween researchers from different fields of expertise. This is why we introduce EpidemiOptim, a Python toolbox that facilitates collaborations between researchers inepidemiology and optimization. EpidemiOptim turns epidemiological models and cost functions into optimization problems via a standard interface commonly used by optimization practitioners (OpenAI Gym). Reinforcement learning algorithms based on QLearning with deep neural networks (DQN) and evolutionary algorithms (NSGA-II) are already implemented. We illustrate the use of EpidemiOptim to find optimal policies fordynamical on-o  lockdown control under the optimization of the death toll and economic recess using a Susceptible-Exposed-Infectious-Removed (SEIR) model for COVID-19. Using EpidemiOptim and its interactive visualization platform in Jupyter notebooks, epidemiologists, optimization practitioners and others (e.g. economists) can easily compare epidemiological models, costs functions and optimization algorithms to address important choicesto be made by health decision-makers. Trained models can be explored by experts and non-experts via a web interface. This article is part of the special track on AI and COVID-19.