AIMC Topic: Infant

Clear Filters Showing 21 to 30 of 1049 articles

Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.

BMC cancer
PURPOSE: Objectives were to develop a machine learning (ML) model based on electronic health record (EHR) data to predict the risk of vomiting within a 96-hour window after admission to the pediatric oncology and hematopoietic cell transplant (HCT) s...

Multimodal pathomics and clinical features predict postresection permanent hydrocephalus in pediatric medulloblastoma.

Journal of neuro-oncology
PURPOSE: Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydroce...

Predictors of childhood vaccination uptake in England: an explainable machine learning analysis of regional data (2021-2024).

Vaccine
BACKGROUND: Childhood vaccination is a cornerstone of public health, yet disparities in vaccination coverage persist across England. These disparities arise from complex interactions among geographic, demographic, socioeconomic, and cultural (GDSC) f...

Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.

Population health metrics
BACKGROUND: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mortality rates than the national averages. Additionally, many of these deaths are from prev...

Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis.

Nature communications
Tuberculosis (TB) remains a major global health burden, particularly in low-resource, high-prevalence regions. Pediatric TB diagnosis poses challenges with non-specific symptoms and less distinct radiological manifestations than adult TB. Many affect...

Neglected brucellosis in pediatric populations from non-endemic regions: Clinical manifestations and prediction of severe disease in Yunnan Province, China.

PLoS neglected tropical diseases
BACKGROUND: Although Yunnan Province is not an endemic region for brucellosis, the disease remains a diagnostic and therapeutic challenge in children due to its atypical clinical manifestations and potential for severe complications.

Comparison of serum lactate and lactate-derived ratios as prognostic biomarkers in pediatric dengue shock syndrome using supervised machine learning models.

PloS one
BACKGROUND: Dengue shock syndrome (DSS), with critical complications encompassing mechanical ventilation (MV), dengue-associated acute liver failure (PALF), and encephalitis, is associated with high mortality in children. Although serum lactate is a ...

Artificial intelligence based sonographic differentiation between skull fractures and normal sutures in young children.

Scientific reports
Accurate differentiation between skull fractures and sutures is challenging in young children. Traditional diagnostic modalities like computed tomography involve ionizing radiation, while sonography is safer but demands expertise. This study explores...

Digital Twins for Monitoring Neuromotor Development in Preterm Infants: Conceptual Framework and Proof-of-concept Study.

Journal of medical systems
Preterm birth leads to an increased risk of long-term consequences, with over 50% of children born <30 weeks facing motor, cognitive, or behavioural impairments. Early monitoring of motor developmental trajectories, strongly associated with neurodeve...

Mass spectrometry combined with machine learning identifies novel protein signatures as demonstrated with multisystem inflammatory syndrome in children.

Scientific reports
Rapid and accurate diagnosis of emerging inflammatory illnesses is challenging due to overlapping clinical features with existing conditions. We demonstrate an approach that integrates proteomic analysis with machine learning to identify diagnostic p...