AI Medical Compendium Topic

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Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy.

Mathematical biosciences and engineering : MBE
Hypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aim...

Residential water and energy consumption prediction at hourly resolution based on a hybrid machine learning approach.

Water research
Predicting water and energy consumption at high resolution over a short-term horizon is critical for water and energy resource management. Water and energy are shown to be closely interlinked in household consumption. However, hourly predictions are ...

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition.

PloS one
Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-ho...

Establishment of a machine learning predictive model for non-alcoholic fatty liver disease: A longitudinal cohort study.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease, which lacks effective drug treatments. This study aimed to construct an eXtreme Gradient Boosting (XGBoost) prediction model to identify or evaluate pot...

Multi-objective location-routing optimization based on machine learning for green municipal waste management.

Waste management (New York, N.Y.)
Most of the existing municipal waste management (MWM) systems focus on the optimization of the waste disposal center locations and waste collection paths, which can be modeled based on the location-routing problem (LRP). This study models a green MWM...

Identifying influence factors and thresholds of the next day's pollen concentration in different seasons using interpretable machine learning.

The Science of the total environment
The prevalence of pollen allergies is a pressing global issue, with projections suggesting that half of the world's population will be affected by 2050 according to the estimation of the World Health Organization (WHO). Accurately forecasting pollen ...

Predictive analysis and risk assessment of potentially toxic elements in Beijing gas station soils using machine learning and two-dimensional Monte Carlo simulations.

Journal of hazardous materials
Gas stations not only serve as sites for oil storage and refueling but also as locations where vehicles frequently brake, significantly enriching the surrounding soil with potentially toxic elements (PTEs). Herein, 117 topsoil samples from gas statio...

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation.

Journal of environmental management
This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air...

Planning and layout of tourism and leisure facilities based on POI big data and machine learning.

PloS one
The spatial arrangement of tourism cities and the strategic placement of tourism and leisure facilities are pivotal to the development of smart tourism cities. The integration of Point of Interest (POI) data, enriched with location-specific insights,...

PM concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China.

Environmental pollution (Barking, Essex : 1987)
PM is a significant global atmospheric pollutant impacting visibility, climate, and public health. Accurate prediction of PM concentrations is critical for assessing air pollution risks and providing early warnings for effective management. This stud...