AIMC Topic: Female

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Personalized azithromycin treatment rules for children with watery diarrhea using machine learning.

Nature communications
We use machine learning to identify innovative strategies to target azithromycin to the children with watery diarrhea who are most likely to benefit. Using data from a randomized trial of azithromycin for watery diarrhea (NCT03130114), we develop per...

A machine learning model using clinical notes to identify physician fatigue.

Nature communications
Clinical notes should capture important information from a physician-patient encounter, but they may also contain signals indicative of physician fatigue. Using data from 129,228 emergency department (ED) visits, we train a model to identify notes wr...

The Future of Questions.

Journal of continuing education in nursing
As we move into the artificial intelligence decade, with more emphasis on creativity and innovation, it will be important to ask questions in a different way. It will be critical for leaders and faculty to think about questions they haven't asked in ...

Harnessing artificial intelligence for detection of pancreatic cancer: a machine learning approach.

Clinical and experimental medicine
PURPOSE: Pancreatic cancer (PC) is one of the most lethal malignancies, often presenting with nonspecific symptoms and a dismal prognosis. Despite advancements in treatments, the 5-year survival rate remains low, highlighting the urgent need for effe...

Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study.

JMIR medical informatics
BACKGROUND: Cytomegalovirus (CMV) reactivation in patients with severe ulcerative colitis (UC) leads to worse outcomes; yet, early detection remains challenging due to the reliance on time-intensive biopsy procedures.

Fully automated measurement of paediatric cerebral palsy pelvic radiographs with BoneFinder : external validation using a national surveillance database.

The bone & joint journal
AIMS: BoneFinder is a machine-learning tool that can automatically calculate Reimers migration percentage (RMP) and head-shaft angle (HSA) from paediatric cerebral palsy (CP) pelvic radiographs. This study's primary aim was to compare BoneFinder's fu...

Artificial Intelligence Iterative Reconstruction for Dose Reduction in Pediatric Chest CT: A Clinical Assessment via Below 3 Years Patients With Congenital Heart Disease.

Journal of thoracic imaging
PURPOSE: To assess the performance of a newly introduced deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in reducing the dose of pediatric chest CT by using the image data of below 3-y...

How does social support influence autonomous physical learning in adolescents? Evidence from a chain mediation and latent profile analysis.

PloS one
PURPOSE: This study examines how social support influences adolescents' autonomous physical learning behavior, exploring the mediating roles of self-efficacy and exercise motivation, and the moderating effects of gender and behavioral typologies. The...

Identification of plasma proteins associated with seizures in epilepsy: A consensus machine learning approach.

PloS one
Blood-based biomarkers in epilepsy could constitute important research tools advancing neurobiological understanding and valuable clinical tools for better diagnosis and follow-up. An interesting question is whether biomarker patterns could contribut...

Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept study.

PloS one
BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are frequent complications of hospitalization, resulting in reduced outcomes and increased cost burden. However, these conditions are only sometimes recognized and promptly treated....