An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study.

Background: Predicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatment. However, a lack of understanding and trust in these ML models impacts adoption among weight management experts. Recent advances in the field of explainable artificial intelligence enable the interpretation of ML models, yet it is unknown whether they enhance model understanding, trust, and adoption among weight management experts.

SmartAct: Energy Efficient and Real-Time Hand-to-Mouth Gesture Detection Using Wearable RGB-T.

Researchers have been leveraging wearable cameras to both visually confirm and automatically detect individuals' eating habits. However, energy-intensive tasks such as continuously collecting and storing RGB images in memory, or running algorithms in real-time to automate detection of eating, greatly impacts battery life. Since eating moments are spread sparsely throughout the day, battery life can be mitigated by recording and processing data only when there is a high likelihood of eating.

Open Surgical Tracheostomy in COVID 19 Patients and Their Outcomes During the Second Wave in India: Experience at a Tertiary Referral Centre.

Tracheostomy in COVID-19 is a debatable topic, with guidelines and recommendations evolving with every wave. Tracheostomy can help early weaning and potentially increase the availability of ICU beds. The aim of our study was to determine the outcomes of patients undergoing tracheostomy at different timings.

Optimizing drug-dose alerts using commercial software throughout an integrated health care system.

All default electronic health record and drug reference database vendor drug-dose alerting recommendations (single dose, daily dose, dose frequency, and dose duration) were silently turned on in inpatient, outpatient, and emergency department areas for pediatric-only and nonpediatric-only populations. Drug-dose alerts were evaluated during a 3-month period. Drug-dose alerts fired on 12% of orders (104 098/834 911).