Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209360 | PMC |
| http://dx.doi.org/10.1007/s11356-022-20860-4 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Quantifying the genetic origins of body plan scaling.
Proc Natl Acad Sci U S A
January 2025
AMOLF, Amsterdam 1098 XG, The Netherlands.
Benchmarking the most popular XAI used for explaining clinical predictive models: Untrustworthy but could be useful.
Health Informatics J
December 2024
The University of Queensland, Brisbane, QLD, Australia.
Objective: This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.
Methods: Two popular XAIs used for explaining clinical predictive models were evaluated based on their ability to generate domain-appropriate representations, impact clinical workflow, and consistency. Explanations were benchmarked against true clinical deterioration triggers recorded in the data system and agreement was quantified.
Development of a web-based tool to assess daily rating of perceived exertion in agility dogs.
Front Vet Sci
December 2024
Department of Surgical and Radiological Sciences, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States.
Objective: To develop a web-based tool for daily use by agility handlers to log rating of perceived exertion (RPE) for dogs as an aid in quantifying daily exercise and training load and to improve training and conditioning strategies.
Procedures: Focus group meetings with small groups of handlers were conducted via internet-based video conferencing using a semi-structured interview format. Meeting notes were coded for reflexive thematic analysis.
Mechanical Thrombectomy Access Score: A Pragmatic Tool to Quantify Barriers to Global Mechanical Thrombectomy Access.
Stroke
January 2025
Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain (M.R.).
Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation.
Hosp Pediatr
January 2025
Department of Biomedical and Health Informatics, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Objective: Natural language processing (NLP) can enhance research studies for febrile infants by more comprehensive cohort identification. We aimed to refine and validate an NLP algorithm to identify and extract quantified temperature measurements from infants aged 90 days and younger with fevers at home or clinics prior to emergency department (ED) visits.
Patients And Methods: We conducted a cross-sectional study using electronic health record (EHR) data from 17 EDs in 10 health systems that are part of the Pediatric Emergency Care Applied Research Network Registry.
Want 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!