AIMC Topic: Machine Learning

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Time series-based machine learning for forecasting multivariate water quality in full-scale drinking water treatment with various reagent dosages.

Water research
Accurately predicting drinking water quality is critical for intelligent water supply management and for maintaining the stability and efficiency of water treatment processes. This study presents an optimized time series machine learning approach for...

Explainable artificial intelligence for reliable water demand forecasting to increase trust in predictions.

Water research
The "EU Artificial Intelligence Act" sets a framework for the implementation of artificial intelligence (AI) in Europe. As a legal assessment reveals, AI applications in water supply systems are categorised as high-risk AI if a failure in the AI appl...

Machine learning adjusted sequential CUSUM-analyses are superior to cross-sectional analysis of excess mortality after surgery.

International journal of medical informatics
BACKGROUND: The assessment of clinical outcome quality, particularly in surgery, is crucial for healthcare improvement. Traditional cross-sectional analyses often fall short in timely and systematic identification of clinical quality issues. This stu...

Identifying Topics Around Nicotine Gum: A Machine Learning Approach with Twitter Data.

Substance use & misuse
BACKGROUND: Nicotine gum products from brands like Lucy and Rogue are relatively new arrivals to the tobacco marketplace. While studies of correlates of nicotine gum use are in their nascent stage, data from social media can be used to stay abreast o...

Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study.

SLAS technology
The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning ...

Improving the construction and prediction strategy of the Air Quality Health Index (AQHI) using machine learning: A case study in Guangzhou, China.

Ecotoxicology and environmental safety
Effectively capturing the risk of air pollution and informing residents is vital to public health. The widely used Air Quality Index (AQI) has been criticized for failing to accurately represent the non-threshold linear relationship between air pollu...

Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review.

Sensors (Basel, Switzerland)
Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alon...

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.

BMC bioinformatics
Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and ...

Building a machine learning-based risk prediction model for second-trimester miscarriage.

BMC pregnancy and childbirth
BACKGROUND: Second-trimester miscarriage is a common adverse pregnancy outcome that imposes substantial economic and psychological pressures on both the physical and mental well-being of patients and their families. Currently, there is a scarcity of ...

Height prediction of individuals with osteogenesis imperfecta by machine learning.

Orphanet journal of rare diseases
BACKGROUND: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of...