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The objective of this study was to systematically review all literature studying the effect of patient education on patient engagement through patient portals.

A shallow convolutional neural network (CNN), TextCNN, has become nearly ubiquitous for classification among clinical and medical text. This research presents a novel eXplainable-AI (X-AI) software, Red Flag/Blue Flag (RFBF), designed for binary classification with TextCNN. RFBF visualizes each convolutional filter's discriminative capability. This is a more informative approach than direct assessment of logit contribution, features that overfit to train set nuances on smaller datasets may indiscriminately activate large logits [...]

The aim of this study was to test the feasibility of PICO (participants, interventions, comparators, outcomes) entity extraction using weak supervision and natural language processing.

The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient [...]

Tumor registries are a rich source of real-world data which can be used to test important hypotheses that inform clinical care. Exploratory data analysis at the level of individual subjects, when enhanced by interactive data visualizations, has the potential to provide novel insights and generate new hypothesis.

Global Health Informatics (GHI) as a branch of health informatics has been established for 2 decades now. During that time, great strides have been made in the creation and implementation of informatics tools to improve healthcare delivery and outcomes in the most vulnerable and remote communities worldwide. In many of the most successful projects, innovation has been shared between teams in high- and low- or middle-income countries (LMICs). In this [...]

The objective of this study is to provide a method to calculate model performance measures in the presence of resource constraints, with a focus on net benefit (NB).

Extracorporeal membrane oxygenation (ECMO) resource allocation tools are currently lacking. We developed machine learning (ML) models for predicting COVID-19 patients at risk of receiving ECMO to guide patient triage and resource allocation.

Recently, the coronavirus disease 2019 (Covid-19) pandemic has led to an increase in the use of telehealth technology. It seems that the application of this technology in low- and middle-income countries (LMICs) has been limited, and few studies have been undertaken to review the current state of knowledge in this area. The aim of the present study was to explore the utilization of telehealth to manage the Covid-19 pandemic in [...]