A Glycemic Status Classification Model Using a Radiofrequency Noninvasive Blood Glucose Monitor.

Despite significant efforts in the development of noninvasive blood glucose (BG) monitoring solutions, delivering an accurate, real-time BG measurement remains challenging. We sought to address this by using a novel radiofrequency (RF) glucose sensor to noninvasively classify glycemic status. The study included 31 participants aged 18-65 with prediabetes or type 2 diabetes and no other significant medical history.

Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study.

Background: The dynamics of the COVID-19 pandemic vary owing to local population density and policy measures. During decision-making, policymakers consider an estimate of the effective reproduction number R, which is the expected number of secondary infections spread by a single infected individual.

Objective: We propose a simple method for estimating the time-varying infection rate and the R.

Adversarial Attacks on Time Series.

Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern.

Multivariate LSTM-FCNs for time series classification.

Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classification model by augmenting the fully convolutional block with a squeeze-and-excitation block to further improve accuracy. Our proposed models outperform most state-of-the-art models while requiring minimum preprocessing.

A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis.

Background: Out-of-hospital cardiac arrest (OHCA) affects nearly 400,000 people each year in the United States of which only 10% survive. Using data from the Cardiac Arrest Registry to Enhance Survival (CARES), and machine learning (ML) techniques, we developed a model of neurological outcome prediction for OHCA in Chicago, Illinois.

Methods: Rescue workflow data of 2639 patients with witnessed OHCA were retrieved from Chicago's CARES.

The Heartfile Lodhran CVD prevention project--end of project evaluation.

Mainstream preventive interventions often fail to reach poor populations with a high risk of cardiovascular diseases (CVDs) in Pakistan. A community-based CVD primary prevention project aimed at developing approaches to reduce risk factors in such populations was established by Heartfile in collaboration with the National Rural Support Program in the district of Lodhran. The project implemented a range of activities integrated with existing social and health service mechanisms during a three year intervention period 2000/01-03/04.