Bioinformatics Commons^™

Comprehensive Characterization Of Covid-19 Patients With Repeatedly Positive Sars-Cov-2 Tests Using A Large U.S. Electronic Health Record Database., Xiao Dong, Yujia Zhou, Xiao-Ou Shu, Elmer V Bernstam, Rebecca Stern, David M Aronoff, Hua Xu, Loren Lipworth

Journal Articles

In the absence of genome sequencing, two positive molecular tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible reinfection. Using a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results ≥60 days apart, separated by ≥2 consecutive negative test results. The prevalence of chronic medical conditions, symptoms, and severe outcomes related to coronavirus disease 19 (COVID-19) illness were ascertained. The median age of patients was 64.5 years, 40% were Black, and 39% …

Digital Technology Needs In Maternal Mental Health: A Qualitative Inquiry., Alexandra Zingg, Laura Carter, Deevakar Rogith, Amy Franklin, Sudhakar Selvaraj, Jerrie Refuerzo, Sahiti Myneni

Journal Articles

Digital technologies offer many opportunities to improve mental healthcare management for women seeking pre- and-postnatal care. They provide a discrete, practical medium that is well-suited for the sensitive nature of mental health. Women who are more prone to experiencing peripartum depression (PPD), such as those of low-socioeconomic background or in high-risk pregnancies, can benefit the most from such technologies. However, current digital interventions directed towards this population provide suboptimal support, and their responsiveness to end user needs is quite limited. Our objective is to understand the digital terrain of information needs for low-socioeconomic status women with high-risk pregnancies, specifically within …

Med-Bert: Pretrained Contextualized Embeddings On Large-Scale Structured Electronic Health Records For Disease Prediction., Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, Degui Zhi

Journal Articles

Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pretraining of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by BERT, we propose Med-BERT, which adapts the BERT framework originally developed …

Generalized And Transferable Patient Language Representation For Phenotyping With Limited Data., Yuqi Si, Elmer V Bernstam, Kirk Roberts

Journal Articles

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 …

Exchanges In A Virtual Environment For Diabetes Self-Management Education And Support: Social Network Analysis., Carlos A Pérez-Aldana, Allison A Lewinski, Constance M Johnson, Allison A Vorderstrasse, Sahiti Myneni

Journal Articles

BACKGROUND: Diabetes remains a major health problem in the United States, affecting an estimated 10.5% of the population. Diabetes self-management interventions improve diabetes knowledge, self-management behaviors, and clinical outcomes. Widespread internet connectivity facilitates the use of eHealth interventions, which positively impacts knowledge, social support, and clinical and behavioral outcomes. In particular, diabetes interventions based on virtual environments have the potential to improve diabetes self-efficacy and support, while being highly feasible and usable. However, little is known about the patterns of social interactions and support taking place within type 2 diabetes-specific virtual communities.

OBJECTIVE: The objective of this study was to …