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: Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks.

Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identifying the span of ADE mentions, and ADE mention normalization to standardized terminologies. While the common goal of such systems is to detect ADE signals that can be used to inform public policy, it has been impeded [...]

The collection of patient reported outcomes (PROs) allows us to incorporate the patient's voice into their care in a quantifiable, validated manner. Large-scale collection of PROs is facilitated by the electronic health record and its portal, though, historically, patients have eschewed the portal and completed patient-reported outcome measures in the clinic via tablet. Furthermore, access and use of the portal is associated with known racial inequities. Our institution oversees the [...]

We propose an interpretable disease prediction model that efficiently fuses multiple types of patient records using a self-attentive fusion encoder. We assessed the model performance in predicting cardiovascular disease events, given the records of a general patient population.

This exploratory study investigated the web accessibility of 54 official COVID-19 vaccine registration websites in the US and their concordance with the WCAG 2.0 and 2.1 guidelines. We employed AChecker, WAVE, and SortSite web accessibility evaluation tools to conduct automated analyses of these websites. The results showed suboptimal compliance with WCAG 2.0 and 2.1 guidelines, as determined using the AChecker, WAVE, and SortSite tools. These shortcomings in compliance may pose [...]

COVID-19 quickly immobilized healthcare systems in the United States during the early stages of the outbreak. While much of the ensuing response focused on supporting the medical infrastructure, Columbia University College of Dental Medicine pursued a solution to triage and safely treat patients with dental emergencies amid the pandemic. Considering rapidly changing guidelines from governing bodies, dental infection control protocols, and our clinical faculty's expertise, we modeled, built, and implemented [...]

Clinical trials are an essential part of the effort to find safe and effective prevention and treatment for COVID-19. Given the rapid growth of COVID-19 clinical trials, there is an urgent need for a better clinical trial information retrieval tool that supports searching by specifying criteria, including both eligibility criteria and structured trial information.

In this perspective we want to highlight the rise of what we call "digital phenotyping" or inferring insights about peopleãs health and behavior from their digital devices and data, and the challenges this introduces. Indeed, the collection, processing, and storage of data comes with significant ethical, security and data governance considerations. The COVID-19 pandemic has laid bare the importance of scientific data and modeling, both to understand the nature and [...]

Rheumatic heart disease (RHD) affects an estimated 39 million people worldwide and is the most common acquired heart disease in children and young adults. Echocardiograms are the gold standard for diagnosis of RHD, but there is a shortage of skilled experts to allow widespread screenings for early detection and prevention of the disease progress. We propose an automated RHD diagnosis system that can help bridge this gap.