AIMC Topic: Machine Learning

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Subphenotyping prone position responders with machine learning.

Critical care (London, England)
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with varying response to prone positioning. We aimed to identify subphenotypes of ARDS patients undergoing prone positioning using machine learning and assess their a...

Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms.

Scientific reports
In recent years, diabetes has become a global public health problem, and it is reported that the migrant Indians have more prevalence rate of Type-II diabetes. Also, the type-II diabetes in Indians are increased to a large extent due to modern lifest...

Utilization of machine learning approach for production of optimized PLGA nanoparticles for drug delivery applications.

Scientific reports
This study investigates utilization of machine learning for the regression task of predicting the size of PLGA (Poly lactic-co-glycolic acid) nanoparticles. Various inputs including category and numeric were considered for building the model to predi...

Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion.

Scientific reports
Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This results in numerous unfavourable circumstances that negatively impact people's life. The identification o...

Deep representation learning for clustering longitudinal survival data from electronic health records.

Nature communications
Precision medicine requires accurate identification of clinically relevant patient subgroups. Electronic health records provide major opportunities for leveraging machine learning approaches to uncover novel patient subgroups. However, many existing ...

Integrated machine learning based groundwater quality prediction through groundwater quality index for drinking purposes in a semi-arid river basin of south India.

Environmental geochemistry and health
The main objective of this study is to predict and monitor groundwater quality through the use of modern Machine Learning (ML) techniques. By employing ML techniques, the research effectively evaluates groundwater quality to forecast its future trend...

Social and economic predictors of under-five stunting in Mexico: a comprehensive approach through the XGB model.

Journal of global health
BACKGROUND: The multifaceted issue of childhood stunting in low- and middle-income countries has a profound and enduring impact on children's well-being, cognitive development, and future earning potential. Childhood stunting arises from a complex in...

Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study.

Frontiers in endocrinology
Hypothyroidism, a common endocrine disorder, has a high incidence in women and increases with age. Levothyroxine (LT4) is the standard therapy; however, achieving clinical and biochemical euthyroidism is challenging. Therefore, developing an accurate...

Exploring predictors of insomnia severity in shift workers using machine learning model.

Frontiers in public health
INTRODUCTION: Insomnia in shift workers has distinctive features due to circadian rhythm disruption caused by reversed or unstable sleep-wake cycle work schedules. While previous studies have primarily focused on a limited number of predictors for in...

An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China.

Frontiers in cellular and infection microbiology
PURPOSE: This study aimed to develop and validate a novel web-based calculator using machine learning algorithms to predict fragility fracture risk in People living with HIV (PLWH), who face increased morbidity and mortality from such fractures.