AIMC Topic: Machine Learning

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Development and validation of inpatient mortality prediction models for patients with hyperglycemic crisis using machine learning approaches.

BMC endocrine disorders
BACKGROUND: Hyperglycemic crisis is one of the most common and severe complications of diabetes mellitus, associated with a high motarlity rate. Emergency admissions due to hyperglycemic crisis remain prevalent and challenging. This study aimed to de...

The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering.

Journal of neural engineering
Machine learning's (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the ca...

Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and Health Survey.

Frontiers in public health
INTRODUCTION: Khat chewing is a significant public health issue in Ethiopia, influenced by various demographic factors. Understanding the prevalence and determinants of khat chewing practices is essential to developing targeted interventions. Therefo...

Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.

Frontiers in endocrinology
PURPOSE: We aimed to identify the mitochondria-related feature genes associated with type 2 diabetes mellitus and explore their potential roles in immune cell infiltration.

Monocyte distribution width (MDW) as a reliable diagnostic biomarker for sepsis in patients with HIV.

Emerging microbes & infections
Sepsis is a leading cause of death among patients with HIV, but early diagnosis remains a challenge. This study evaluates the diagnostic performance of monocyte distribution width (MDW) in detecting sepsis in patients with HIV. A prospective observat...

Health shocks and health behavior: a long-term perspective.

The European journal of health economics : HEPAC : health economics in prevention and care
Several empirical papers suggest that individuals improve health-related behaviors in response to adverse shocks to physical health. However, little evidence exists regarding the questions of (i) how long-lasting these behavioral responses are and (i...

Validation of a cancer population derived AKI machine learning algorithm in a general critical care scenario.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Acute Kidney Injury (AKI) is the sudden onset of kidney damage. This damage usually comes without warning and can lead to increased mortality and inpatient costs and is of particular significance to patients undergoing cancer treatment. In p...

Cesarean Scar Pregnancy Prognostic Classification System Based on Machine-Learning and Traditional Linear Scoring Models.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: Cesarean scar pregnancy (CSP) refers to a special type of pregnancy with a variable prognosis. We aimed to establish a prognostic classification system using ultrasound and clinical features to provide a reference for management strategie...

Interpretable machine learning-based insights into early-life endocrine disruptor exposure and small vulnerable newborns.

Journal of hazardous materials
Early-life exposure to endocrine-disrupting chemicals (EDCs) may contribute to small vulnerable newborns, including conditions such as being small for gestational age (SGA) and preterm birth (PTB), yet evidence remains limited. This study, which is b...