AIMC Topic: Machine Learning

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Machine Learning for Clinical Decision Support in the Neonatal Intensive Care Unit.

NeoReviews
The neonatal intensive care unit (NICU) is a data-rich environment that is an ideal setting for the implementation of machine learning (ML) and artificial intelligence (AI) in clinical decision support (CDS). Despite their potential, ML and AI applic...

Machine-learning models to predict iron recovery after blood donation: a model development and external validation study.

The Lancet. Haematology
BACKGROUND: Machine-learning models directly predicting iron biomarkers after blood donation could help to manage donation-associated iron deficiency and avoid low haemoglobin deferrals. No such models have been externally validated internationally. ...

Using Machine Learning to Identify Predictors of Maternal and Infant Hair Cortisol Concentration Before and During the COVID-19 Pandemic.

Stress and health : journal of the International Society for the Investigation of Stress
Hair cortisol concentration (HCC) has been theorized to reflect chronic stress, and maternal and infant HCC may be correlated due to shared genetic, physiological, behavioural, and environmental factors, such as stressful life circumstances. The curr...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

Critical care explorations
OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventi...

Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline.

Human brain mapping
There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four indiv...

Utilizing machine learning algorithms for cardiovascular disease prediction: "Detailed analysis based on medical parameters".

Medical engineering & physics
Among the most prevalent and dangerous ailments impacting human health are cardiovascular diseases (CVDs). Early diagnosis may help avoid or lessen CVDs, thereby lowering death rates. Several clinical methods have already been deployed for diagnosing...

An explainable machine learning framework for predicting driving states using electroencephalogram.

Medical engineering & physics
OBJECTIVES: Understanding drivers' cognitive load is essential for enhancing road safety, as cognitive demands fluctuate across different driving scenarios, potentially impacting performance, and safety, particularly for drivers with neurological dis...

Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.

Anticancer research
BACKGROUND/AIM: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed to develop a model for predicting the severity of bacteremia in patients with FN and hematologic ma...

Artificial intelligence assisted nutritional risk evaluation model for critically ill patients: Integration of explainable machine learning in intensive care nutrition.

Asia Pacific journal of clinical nutrition
BACKGROUND AND OBJECTIVES: Critically ill patients require individualized nutrition support, with assessment tools like Nutrition Risk Screening 2002 and Nutrition Risk in the Critically Ill scores. Challenges in continu-ous nutrition care prompt the...