Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/0142159X.2023.2232527 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Artificial intelligence in triage of COVID-19 patients.
Front Artif Intell
December 2024
School of Medicine, University of Brasilia, Brasilia, Brazil.
In 2019, COVID-19 began one of the greatest public health challenges in history, reaching pandemic status the following year. Systems capable of predicting individuals at higher risk of progressing to severe forms of the disease could optimize the allocation and direction of resources. In this work, we evaluated the performance of different Machine Learning algorithms when predicting clinical outcomes of patients hospitalized with COVID-19, using clinical data from hospital admission alone.
View Article and Find Full Text PDFThe Use of Remote Learning Techniques for Pediatric Cardiopulmonary Resuscitation Simulation Highlighting the Intersection Between Pediatric Advanced Life Support (PALS) and Advanced Cardiac Life Support (ACLS) Pathways.
Cureus
December 2024
Department of Pediatrics/Division of Hospital Medicine, Children's National Hospital, Washington, DC, USA.
Introduction: Medical simulation education has expanded in the remote learning sphere, providing educational opportunities to under-resourced areas and the ability to engage learners limited by time or geographic location. Pediatric resuscitation training has historically been in-person relying on Pediatric Advanced Life Support (PALS) algorithms, yet many pediatric providers are often faced with treating adult or adult-sized patients. Our goal was to develop a tele-simulation remote learning module highlighting possible diagnoses and scenarios that require adult treatment-minded approaches for the pediatric clinician, including the use of Advanced Cardiac Life Support (ACLS) algorithms.
View Article and Find Full Text PDFUsing Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.
J Surg Res
December 2024
Department of Surgery, University of Wisconsin, Madison, Wisconsin; Department of Surgery, William S. Middleton Memorial VA, Madison, Wisconsin. Electronic address:
Introduction: Obesity, defined as a body mass index ≥30 kg/m, is a major public health concern in the United States. Preventative approaches are essential, but they are limited by an inability to accurately predict individuals at highest risk of weight gain. Our objective was to develop accurate weight gain prediction models using the National Institutes of Health All of Us dataset.
View Article and Find Full Text PDFPredicting benefit from PARP inhibitors using deep learning on H&E-stained ovarian cancer slides.
Eur J Cancer
December 2024
Division of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address:
Purpose: Ovarian cancer patients with a Homologous Recombination Deficiency (HRD) often benefit from polyadenosine diphosphate-ribose polymerase (PARP) inhibitor maintenance therapy after response to platinum-based chemotherapy. HR status is currently analyzed via complex molecular tests. Predicting benefit from PARP inhibitors directly on histological whole slide images (WSIs) could be a fast and cheap alternative.
View Article and Find Full Text PDFImproving robustness by action correction via multi-step maximum risk estimation.
Neural Netw
December 2024
School of Computer Science and Technology, Soochow University, Suzhou, 215006, China. Electronic address:
Certifying robustness against external uncertainties throughout the control process to reduce the risk of instability is very important. Most existing approaches based on adversarial learning use a fixed parameter to adjust the intensity of adversarial perturbations and design these perturbations in a greedy manner without considering future implications. However, they often lead to severe vulnerabilities when attack budgets vary dynamically or under foresighted attacks.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!