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Biomedical research involving social media data is gradually moving from population-level to targeted, cohort-level data analysis. Though crucial for biomedical studies, social media user's demographic information (eg, gender) is often not explicitly known from profiles. Here, we present an automatic gender classification system for social media and we illustrate how gender information can be incorporated into a social media-based health-related study.

To outline the development of a software solution to improve medication management after hospital discharge, including its design, data sources, intrinsic features, and to evaluate the usability and the perception of use by end-users.

The objective of this study is to facilitate monitoring of the quality of inpatient glycemic control by providing an open-source tool to compute glucometrics. To allay regulatory and privacy concerns, the tool is usable locally; no data are uploaded to the internet.

Feature engineering is a major bottleneck in phenotyping. Properly learned medical concept embeddings (MCEs) capture the semantics of medical concepts, thus are useful for retrieving relevant medical features in phenotyping tasks. We compared the effectiveness of MCEs learned from knowledge graphs and electronic healthcare records (EHR) data in retrieving relevant medical features for phenotyping tasks.

A new monitoring system was implemented to support nursing staff and physicians on the COVID-19 ward. This system was designed to remotely monitor vital signs, to calculate an automated Early Warning Score, and to help identify patients at risk of deterioration.

Guided by the concept of digital phenotypes, the objective of this study was to identify engagement phenotypes among individuals with atrial fibrillation (AF) using mobile health (mHealth) technology for 6 months.

Our study consists in aligning the interface terminology of the Bordeaux university hospital (TLAB) to the Logical Observation Identifiers Names and Codes (LOINC). The objective was to facilitate the shared and integrated use of biological results with other health information systems.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over [...]

In this paper, we discuss leveraging cloud-based platforms to collect, visualize, analyze, and share data in the context of a clinical trial. Our cloud-based infrastructure, Patient Repository of Biomolecular Entities (PRoBE), has given us the opportunity for uniform data structure, more efficient analysis of valuable data, and increased collaboration between researchers.

In the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability.