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Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory [...]

Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models [...]

One challenge that arises when analyzing mobile health (mHealth) data is that updates to the proprietary algorithms that process these data can change apparent patterns. Since the timings of these updates are not publicized, an analytic approach is necessary to determine whether changes in mHealth data are due to lifestyle behaviors or algorithmic updates. Existing methods for identifying changepoints do not consider multiple types of changepoints, may require prespecifying the [...]

A connected system with smart devices could transform patient care and empower patients control of their asthma.

[This corrects the article DOI: 10.1093/jamiaopen/ooac039.].

Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data.

As part of ongoing implementation of electronic patient-reported outcome tools at the Dana-Farber Cancer Institute, here we describe the development of the electronic New Patient Intake Questionnaire.

Predicting daily trends in the Coronavirus Disease 2019 (COVID-19) case number is important to support individual decisions in taking preventative measures. This study aims to use COVID-19 case number history, demographic characteristics, and social distancing policies both independently/interdependently to predict the daily trend in the rise or fall of county-level cases.

Many states are continuing to struggle with opioids and other commonly abused drugs. Alabama, being the highest opioid prescription per-capita state since 2012, has pulled together state agencies, private companies, academia, and community organizations to form a data council and repository to provide unified insights and information to the public and partner stakeholders. The lessons learned in constructing this data environment are documented so that other states and organizations can [...]

To describe process innovations related to research informed consent documents, and development and formative evaluation of Consent Builder, a platform for generating consent documents for multicenter studies.