The basic concepts of machine learning are introduced with a focus on intuition and examples. The simpler and widely used logistic regression model is introduced first, and from this, multilayered neural network are introduced as a generalization.
PyTorch is an open source machine learning framework popular for building neural networks. In this hands-on session, we'll walk through building and training a neural network, introducing the basic mechanics of PyTorch.
Recent technological advancements make it possible to closely and continuously monitor patients on multiple scales, both inside and outside of the clinic.
We will overview several important issues in causal inference methods for evaluating the comparative effectiveness and efficacy of potential COVID medications and vaccines. These issues cover both design and analysis of randomized trials, natural experiments, and observational studies.
As the COVID-19 pandemic hit Durham County, the Duke University Health System (DUHS) responded by postponing most non-urgent (i.e. elective) operations.
Medical image analysis with machine learning holds immense promise for accelerating the radiology workflow and benefiting patient care. Chest computed tomography (CT) is a medical imaging technique that produces a high-resolution volumetric image of the heart and lungs.
The goal of computer vision is for computers to be able to understand visual content (e.g. images, videos, 3D, stereo), usually for the purpose of making predictions (classification, detection, captioning, generation, etc.).
PyTorch is an open source framework for building neural networks. In this lesson, we will build a foundational understanding of PyTorch by developing a simple neural network, the Multilayer Perceptron (MLP).
A key aspect of analysis of data involves classification and regression; these play a key role in the analysis of many types of data connected to COVID-19. To perform such analyses, one typically must extract features from the data, with which classification/regression is performed.
Molecular analysis of gene expression, microbiome, and proteomics data aims to understand biological processes by leveraging high-throughput technologies and data science. Aided by subject matter expertise, this combination has resulted in accelerated discoveries in health and disease.