Pediatrics

Latest AI and machine learning research in pediatrics for healthcare professionals.

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Predicting malaria epidemics in Burkina Faso with machine learning.

Accurately forecasting the case rate of malaria would enable key decision makers to intervene months...

Development of Crime Scene Intelligence Using a Hand-Held Raman Spectrometer and Transfer Learning.

The classification of ignitable liquids, such as gasoline, is critical crime scene intelligence to a...

Human breast milk-based nutritherapy: A blueprint for pediatric healthcare.

Human Breast Milk (HBM) is a storehouse of micronutrients, macronutrients, immune factors, microbiot...

Decreased neutrophil-mediated bacterial killing in COVID-19 patients.

The coronavirus disease COVID-19 was first described in December 2019. The peripheral blood of COVID...

Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents.

Tuberculosis (TB) is an infectious disease and the leading cause of death globally. The rapidly emer...

Indian citizen's perspective about side effects of COVID-19 vaccine - A machine learning study.

BACKGROUND AND AIMS: Ever since the vaccination drive for COVID-19 has started in India, the citizen...

Deep Learning Assisted Neonatal Cry Classification Support Vector Machine Models.

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns de...

Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study.

BACKGROUND: Dysphonia influences the quality of life by interfering with communication. However, a l...

Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images.

Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising p...

Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.

Currently there are no reliable means of identifying infants at-risk for later language disorders. I...

Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI with deep learning.

BACKGROUND: Differentiating acute bilirubin encephalopathy (ABE) from non-ABE in neonates with hyper...

Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.

PURPOSE: Due to the increasing demand for intensive care unit (ICU) treatment, and to improve qualit...

Digital technologies to improve the precision of paediatric growth disorder diagnosis and management.

Paediatric disorders of impaired linear growth are challenging to manage, in part because of delays ...

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study.

BACKGROUND: The use of artificial intelligence has revolutionized every area of life such as busines...

Deep learning-based prediction of future growth potential of technologies.

Research papers are a repository of information on the various elements that make up science and tec...

Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates.

Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premat...

Detecting Medical Misinformation on Social Media Using Multimodal Deep Learning.

In 2019, outbreaks of vaccine-preventable diseases reached the highest number in the US since 1992. ...

Study on the identification and evaluation of growth years for Paris polyphylla var. yunnanensis using deep learning combined with 2DCOS.

Paris polyphylla var. yunnanensis, as perennial plants, its quality is closely related to growth per...

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