AIMC Topic: Mexico

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Parallel prediction of dengue cases with different risks in Mexico using an artificial neural network model considering meteorological data.

International journal of biometeorology
In 2022, Mexico registered an increase in dengue cases compared to the previous year. On the other hand, the amount of precipitation reported annually was slightly less than the previous year. Similarly, the minimum-mean-maximum temperatures recorded...

Evaluation of height prediction models: from traditional methods to artificial intelligence.

Pediatric research
BACKGROUND: Traditional methods for predicting adult height (AHP) rely on manual readings of bone age (BA). However, the incorporation of artificial intelligence has recently improved the accuracy of BA readings and their incorporation into AHP model...

Prediction of daily mean and one-hour maximum PM concentrations and applications in Central Mexico using satellite-based machine-learning models.

Journal of exposure science & environmental epidemiology
BACKGROUND: Machine-learning algorithms are becoming popular techniques to predict ambient air PM concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to pred...

Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches.

PloS one
The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with comorbilities and COVID-19. This manuscript aims to present models to predict, firstly, the number of co...

An ontology network for Diabetes Mellitus in Mexico.

Journal of biomedical semantics
BACKGROUND: Medical experts in the domain of Diabetes Mellitus (DM) acquire specific knowledge from diabetic patients through monitoring and interaction. This allows them to know the disease and information about other conditions or comorbidities, tr...

A Machine Learning Model for Evaluating Imported Disease Screening Strategies in Immigrant Populations.

The American journal of tropical medicine and hygiene
Given the high prevalence of imported diseases in immigrant populations, it has postulated the need to establish screening programs that allow their early diagnosis and treatment. We present a mathematical model based on machine learning methodologie...

Microplanning for designing vaccination campaigns in low-resource settings: A geospatial artificial intelligence-based framework.

Vaccine
Existing campaign-based healthcare delivery programs used for immunization often fall short of established health coverage targets due to a lack of accurate estimates for population size and location. A microplan, an integrated set of detailed planni...

Comparative study of machine learning methods for COVID-19 transmission forecasting.

Journal of biomedical informatics
Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new...

Understanding Attitudes to Change to Healthier Hydration Habits: The Case of High Sugar: Low Water Drinkers in Mexico.

Annals of nutrition & metabolism
Adults consuming sugar-sweetened beverages (SSBs) are at increased risk of becoming overweight/obese and developing lifestyle-related diseases. Furthermore, a low water intake is associated with increased health risks, such as CKD. These issues are e...

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

International journal of environmental research and public health
Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due to the limited space in the emergency department, it is important to identify when thoracic pain is...