AIMC Topic: Child, Preschool
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Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.
Journal of pediatric hematology/oncology
Dec 2, 2024
OBJECTIVES: This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) v...
Predictive modelling of linear growth faltering among pediatric patients with Diarrhea in Rural Western Kenya: an explainable machine learning approach.
BMC medical informatics and decision making
Dec 2, 2024
INTRODUCTION: Stunting affects one-fifth of children globally with diarrhea accounting for an estimated 13.5% of stunting. Identifying risk factors for its precursor, linear growth faltering (LGF), is critical to designing interventions. Moreover, de...
Predicting Epidural Hematoma Expansion in Traumatic Brain Injury: A Machine Learning Approach.
The neuroradiology journal
Nov 24, 2024
IntroductionTraumatic brain injury (TBI) is a leading cause of disability and mortality worldwide, with epidural hematoma (EDH) being a severe consequence. This study focuses on identifying factors predicting EDH volume changes in TBI patients and de...
Exploring sensory alterations and repetitive behaviors in children with autism spectrum disorder from the perspective of artificial neural networks.
Research in developmental disabilities
Nov 21, 2024
BACKGROUND: Restrictive repetitive behaviors (RRBs) and sensory processing disorders are core symptoms of autism spectrum disorder (ASD). Their relationship is reported, but existing data are conflicting as to whether they are related but distinct, o...
A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity During Brain Development.
IEEE transactions on bio-medical engineering
Nov 21, 2024
OBJECTIVE: Brain dynamic effective connectivity (dEC), characterizes the information transmission patterns between brain regions that change over time, which provides insight into the biological mechanism underlying brain development. However, most e...
Predictive modeling of outcomes in acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation using machine learning techniques.
Leukemia research
Nov 20, 2024
BACKGROUND: Leukemia necessitates continuous research for effective therapeutic techniques. Acute leukemia (AL) patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) focus on key outcomes such as overall survival (OS), re...
Leukemia
Graft vs Host Disease
Hematopoietic Stem Cell Transplantation
Transplantation, Homologous
Precursor Cell Lymphoblastic Leukemia-Lymphoma
Survival Rate
Female
Child
Adolescent
Leukemia, Myeloid, Acute
Aged
Humans
Treatment Outcome
Child, Preschool
Male
Machine Learning
Young Adult
Follow-Up Studies
Middle Aged
Adult
Prognosis
Retrospective Studies
Prediction of Survival After Pediatric Cardiac Arrest Using Quantitative EEG and Machine Learning Techniques.
Neurology
Nov 20, 2024
BACKGROUND AND OBJECTIVES: Early neuroprognostication in children with reduced consciousness after cardiac arrest (CA) is a major clinical challenge. EEG is frequently used for neuroprognostication in adults, but has not been sufficiently validated f...
Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.
Current medical science
Nov 20, 2024
OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism spectrum disorder (ASD), while studies have also demonstrated that mirror neuron mu rhythm-based EE...
Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia.
Neurosurgery
Nov 18, 2024
BACKGROUND AND OBJECTIVES: Chiari type-1 malformation (CM1) and syringomyelia (SM) are common related pediatric neurosurgical conditions with heterogeneous clinical and radiological presentations that offer challenges related to diagnosis and managem...
ResNet-Transformer deep learning model-aided detection of dens evaginatus.
International journal of paediatric dentistry
Nov 15, 2024
BACKGROUND: Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it may lead to tubercles fracture and pulpal/periapical disease. Consequently, early detection and intervention of dens evaginatus are significant to prese...
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