AIMC Topic: Diarrhea

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Effects of environment and globalization on the double and triple burdens of infection symptoms among under-five children across low-middle income countries using machine learning algorithms.

Infectious diseases of poverty
BACKGROUND: Childhood infectious diseases and related symptoms, such as fever, cough, and diarrhea among children constitute the leading cause of death in low and middle-income countries (LMICs). We examined the environmental predictors of double and...

Rebuilding the gut ecosystem: Emerging strategies targeting the microbiota in antibiotic-associated diarrhea.

Acta microbiologica et immunologica Hungarica
Antibiotic-associated diarrhea (AAD) is a prevalent iatrogenic complication of antibiotic therapy, primarily triggered by dysbiosis and loss of intestinal homeostasis. The traditional interventions, such as empirical probiotic use, have shown a modes...

Comparative estimation of the spread of acute diarrhea and dengue in India using statistical mathematical and deep learning models.

Scientific reports
This study aims to forecast the spread of acute diarrhoea and dengue diseases in India by conducting a comparative analysis of statistical, mathematical (compartmental), and deep learning time series models. Utilizing weekly reported cases and fatali...

Automatic porcine diarrhea viruses classification using pathological images and hybrid semantic neural network.

Computers in biology and medicine
Porcine epidemic diarrhea is a highly contagious intestinal disease in pigs, caused by various strains of porcine epidemic diarrhea virus (PEDV). The infection rate in suckling piglets can reach 100%. Manual analysis of pathological images is the pri...

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 retrospective study using machine learning to develop predictive model to identify rotavirus-associated acute gastroenteritis in children.

PeerJ
BACKGROUND: Rotavirus is the leading cause of severe dehydrating diarrhea in children under 5 years worldwide. Timely diagnosis is critical, but access to confirmatory testing is limited in hospital settings. Machine learning (ML) models have shown p...

Interpretable machine learning-derived nomogram model for early detection of persistent diarrhea in Salmonella typhimurium enteritis: a propensity score matching based case-control study.

BMC infectious diseases
BACKGROUND: Salmonella typhimurium infection is a considerable global health concern, particularly in children, where it often leads to persistent diarrhea. This condition can result in severe health complications including malnutrition and cognitive...

Development of a one-step multiplex RT-qPCR method for rapid detection of bovine diarrhea viruses.

Frontiers in cellular and infection microbiology
INTRODUCTION: Viral calf diarrhea poses a significant challenge to the cattle industry worldwide due to its high morbidity and mortality rates, leading to substantial economic losses. The clinical symptoms associated with various diarrhea pathogens o...

Derivation and validation of a clinical predictive model for longer duration diarrhea among pediatric patients in Kenya using machine learning algorithms.

BMC medical informatics and decision making
BACKGROUND: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machin...