Exploring the Interplay of Dataset Size and Imbalance on CNN Performance in Healthcare: Using X-rays to Identify COVID-19 Patients.

Introduction: Convolutional Neural Network (CNN) systems in healthcare are influenced by unbalanced datasets and varying sizes. This article delves into the impact of dataset size, class imbalance, and their interplay on CNN systems, focusing on the size of the training set versus imbalance-a unique perspective compared to the prevailing literature. Furthermore, it addresses scenarios with more than two classification groups, often overlooked but prevalent in practical settings.

A novel digital tool for detection and monitoring of amyotrophic lateral sclerosis motor impairment and progression via keystroke dynamics.

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative condition leading to progressive muscle weakness, atrophy, and ultimately death. Traditional ALS clinical evaluations often depend on subjective metrics, making accurate disease detection and monitoring disease trajectory challenging. To address these limitations, we developed the nQiALS toolkit, a machine learning-powered system that leverages smartphone typing dynamics to detect and track motor impairment in people with ALS.

Tracking amyotrophic lateral sclerosis disease progression using passively collected smartphone sensor data.

Background: Passively collected smartphone sensor data provide an opportunity to study physical activity and mobility unobtrusively over long periods of time and may enable disease monitoring in people with amyotrophic lateral sclerosis (PALS).

Methods: We enrolled 63 PALS who used Beiwe mobile application that collected their smartphone accelerometer and GPS data and administered the self-entry ALS Functional Rating Scale-Revised (ALSFRS-RSE) survey. We identified individual steps from accelerometer data and used the Activity Index to summarize activity at the minute level.

The HOPE study: evaluating the impact of an online educational resource for heavy menstrual bleeding on the patient-physician dynamic.

The Heavy menstrual bleeding: Evidence-based Learning for best Practice (HELP) Group developed an educational website about heavy menstrual bleeding (HMB). The "HMB improving Outcomes with Patient counseling and Education" (HOPE) project examined the website's impact on women's knowledge, confidence, and consultations with healthcare providers (HCPs). HOPE was a quantitative online survey of gynecologists and women with HMB in Brazil.

Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures.

Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months.