A5025394849- Machine Learning in Healthcare
- COVID-19 diagnosis using AI
- Sepsis Diagnosis and Treatment
- Artificial Intelligence in Healthcare and Education
- Clinical Reasoning and Diagnostic Skills
- Respiratory Support and Mechanisms
- Intensive Care Unit Cognitive Disorders
- Health and Medical Research Impacts
- Thermal Regulation in Medicine
- Biomedical and Engineering Education
- Cardiac Arrest and Resuscitation
- Non-Invasive Vital Sign Monitoring
- Radiology practices and education
- High Altitude and Hypoxia
- Cardiovascular and Diving-Related Complications
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging and Analysis
Undersea and Hyperbaric Medical Society
2022
University of California, San Diego
2020-2021
UC San Diego Health System
2020
Abstract Objective The coronavirus disease 2019 pandemic has inspired new innovations in diagnosing, treating, and dispositioning patients during high census conditions with constrained resources. Our objective is to describe first experiences of physician interaction a novel artificial intelligence (AI) algorithm designed enhance abilities identify ground‐glass opacities consolidation on chest radiographs. Methods During the wave pandemic, we deployed previously developed validated...
ABSTRACT IMPORTANCE Objective and early identification of hospitalized patients, particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation is great importance aid in delivering timely treatment. OBJECTIVE To develop, externally validate prospectively test a transparent deep learning algorithm for predicting 24 hours advance the need patients COVID-19. DESIGN Observational cohort study SETTING Two academic medical centers from January 01, 2016...
Introduction: The coronavirus disease 2019 (COVID-19) pandemic has seriously impacted clinical research operations in academic medical centers due to social distancing measures and stay-at-home orders. purpose of this paper is describe the implementation a program continue based out an emergency department (ED) using remote associates (RA).Methods: Remote RAs were trained granted access electronic health record (EHR) by system's core information technology team. Upon gaining access, used...
ABSTRACT Objective Machine-learning (ML) algorithms allow for improved prediction of sepsis syndromes in the ED using data from electronic medical records. Transfer learning, a new subfield ML, allows generalizability an algorithm across clinical sites. We aimed to validate Artificial Intelligence Sepsis Expert (AISE) delayed septic shock cohort patients treated and demonstrate feasibility transfer learning improve external validity at second site. Methods Observational study utilizing over...
Faults or errors during use of closed-circuit rebreathers (CCRs) can cause hypoxia. Military aviators face a similar risk hypoxia and undergo awareness training to determine their 'hypoxia signature', personalised, reproducible set symptoms. We aimed establish signature among divers, investigate ability detect self-rescue while cognitively overloaded.Eight CCR divers 12 scuba underwent an initial unblinded exposure followed by three trials; second hypoxic trial two normoxic trials in...