Publications by authors named "S K Bhavnani"
Background: Social determinants of health (SDoH), such as financial resources and housing stability, account for between 30% and 55% of people's health outcomes. While many studies have identified strong associations between specific SDoH and health outcomes, little is known about how SDoH co-occur to form subtypes critical for designing targeted interventions. Such analysis has only now become possible through the All of Us program.
View Article and Find Full Text PDFDevelopments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human technicians. Here we tested an artificial intelligence (AI) algorithm for direct-to-physician reporting of ambulatory ECGs. Beat-by-beat annotation of 14,606 individual ambulatory ECG recordings (mean duration = 14 ± 10 days) was performed by certified ECG technicians (n = 167) and an ensemble AI model, called DeepRhythmAI.
View Article and Find Full Text PDFBackground: Over 250 million children are developing sub-optimally due to their exposure to early life adversities. While previous studies have examined the effects of nutritional status, psychosocial adversities, and environmental pollutants on children's outcomes, little is known about their interaction and cumulative effects.
Objectives: This study aims to investigate the independent, interaction, and cumulative effects of nutritional, psychosocial, and environmental factors on children's cognitive development and mental health in urban and rural India.
Article Synopsis
- Sulbactam-durlobactam is a newly approved combination drug for treating specific types of pneumonia in adults, using data from multiple clinical studies to assess its pharmacokinetics.
- A complex four-compartment model was created to understand how the drug behaves in the body, factoring in both kidney and non-kidney clearance methods.
- Key findings showed that factors like body weight and renal function impact drug clearance, with the model being suitable for predicting how the drug performs in different patient populations.
Article Synopsis
- Despite advancements in emergency response, mortality rates from sudden cardiac arrest remain high due to gaps in bystander intervention and disparities in access to care.
- Recent innovations like machine learning algorithms show promise in identifying at-risk individuals and recognizing emergencies but need to be integrated into public health strategies.
- The authors suggest a system that combines data-driven technology with equitable public health approaches to enhance outcomes for sudden cardiac arrest.