Pediatrics

A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level.

We demonstrate that a neural network pretrained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's Codex transformer and execute them to solve course problems at 81% automatic accuracy. We curate a dataset of questions from Massachusetts Institute ...

Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity.

Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their ability to search for interactions. This paper shows evidence that an explainable artificial intelligence approach, commonly employed in genomics research...

Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework.

The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that chall...

Health Information Prediction System of Infant Sports Based on Deep Learning Network.

The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalitie...

Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities.

The rise of machine learning in healthcare has significant implications for paediatrics. Long-term conditions with significant disease heterogeneity c...

Artificial intelligence-based clinical decision support in pediatrics.

Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical ...

A deep learning framework for epileptic seizure detection based on neonatal EEG signals.

Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection of epileptic activity is usually performed by a human exper...

Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks.

In many countries, mental health issues are among the most serious public health concerns. National mental health statistics are frequently collected ...

Correlation between an annotation-free embryo scoring system based on deep learning and live birth/neonatal outcomes after single vitrified-warmed blastocyst transfer: a single-centre, large-cohort retrospective study.

PROPOSE: Does an annotation-free embryo scoring system based on deep learning and time-lapse sequence images correlate with live birth (LB) and neonat...

Surgical and functional outcomes of robot-assisted laparoscopic partial nephrectomy for renal cell carcinoma in adolescents and young adults: a propensity score matching study.

BACKGROUND: Cancer development in adolescents and young adults (AYAs) has elicited recent interest. We investigated the surgical and functional outcom...

Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images.

Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neu...

ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification.

Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every yea...

Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury.

The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians...

The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric.

Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in ch...

Sentiment Analysis of Animated Film Reviews Using Intelligent Machine Learning.

Film is an essential expression of a country's cultural soft power in terms of cross-cultural exchange. In addition, film is also the most direct and ...

Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans.

Background Assessment of appropriate brain myelination on T1- and T2-weighted MRI scans is based on gestationally corrected age (GCA) and requires sub...

Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network.

Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not ...

Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside.

Artificial intelligence (AI)-based technologies are the most rapidly growing field of innovation in healthcare with the promise to achieve substantial...

Co-design of digital learning resources for care workers: reflections on the neurocare knowhow project.

Neurocare Knowhow is an online learning platform for care workers who support people with neurological conditions. Care workers often do not receive s...

Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research.

In the era of Internet +, modern industry has developed rapidly, the network economy has promoted the great development of the industrial economy, and...