Resource library

There’s a lot of excitement about the potential for AI to improve healthcare. This is driven by compelling advances across a wide range of applications including drug discovery, radiology, pathology, electronic medical record (EMR) intelligence, clinical trials, and more. There are also many challenges for development and deployment of AI for healthcare.

Background: The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. Methods: From a sample of 34 287 white British ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac magnetic resonance imaging sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by…

In this paper, we propose a learning algorithm for training deep neural networks when there is not sufficient labeled data. To improve the generalization capabilities of the deep model, we adopt a learning scheme to train two related tasks simultaneously. One is the original task (target), and the other is an auxiliary task (source). In order to create a related…

We are inventing a new way to build enterprise AI applications. Taking a data-centric approach, we are making machine learning iterable, faster to deploy, and ultimately more practical.That is a fantastic opportunity, but it also presents one of our biggest challenges – figuring out how to bridge the gap between developers at the vanguard of machine learning and business leaders…

Objective: Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order. Materials and Methods: 16,103 SARS-CoV-2 RT-PCR tests were performed on 15,807 patients at Stanford facilities between March 2 and April 11,…

Today I’m excited to announce Snorkel AI’s launch out of stealth! Snorkel AI, which spun out of the Stanford AI Lab in 2019, was founded on two simple premises: first, that the labeled training data machine learning models learn from is increasingly what determines the success or failure of AI applications. And second, that we can do much better than labeling this…

There is substantial interest in using presenting symptoms to prioritize testing for COVID-19 and establish symptom-based surveillance. However, little is currently known about the specificity of COVID-19 symptoms. To assess the feasibility of symptom-based screening for COVID-19, we used data from tests for common respiratory viruses and SARS-CoV-2 in our health system to measure the ability to correctly classify virus…

Knowledge graph (KG) embeddings learn lowdimensional representations of entities and relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which must be preserved in the embedding space. For hierarchical data, hyperbolic embedding methods have shown promise for high-fidelity and parsimonious representations. However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs. In…

Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, and their physical properties, which has limited CNNs’ success on tasks that require structured understanding of visual scenes. To overcome these limitations, we introduce the idea of “Physical Scene Graphs” (PSGs), which represent scenes as hierarchical graphs, with…