AIMC Topic: Machine Learning

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Machine learning-based classification of histological subtypes of invasive breast cancer using MRI contralateral breast texture features.

Scientific reports
Invasive Breast Cancer (IBC), encompassing Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC), is the most prevalent cancer in women. This study aimed to develop a machine learning (ML) model for distinguishing between its histologi...

Acute myeloid leukemia risk stratification in younger and older patients through transcriptomic machine learning models.

Scientific reports
Acute Myeloid Leukemia (AML) is a genetically and clinically heterogeneous disease that can develop at any age. While AML incidence increases with age and distinct genetic alterations are observed in younger versus older patients, current classificat...

Hemodynamic determinants of postoperative neurocognitive impairment using Random Forest analysis and partial dependence plots.

Scientific reports
This study investigated the effect of hemodynamic data during cardiopulmonary bypass (CPB) on neurocognitive impairment in patients undergoing coronary artery bypass graft (CABG) surgery using machine learning algorithms. Twenty-eight CABG patients w...

Modeling public trust in AI cognitive capabilities using statistical and machine learning approaches.

Scientific reports
As artificial intelligence (AI) systems increasingly perform cognitive functions, assessing public trust in these capabilities is critical. This study investigates the impact of age, gender, and familiarity with AI on confidence in AI's ability to ma...

Development of a machine learning model for automatic data extraction from breast cancer pathology reports.

Scientific reports
Data extraction from medical records is crucial for clinical research, with current methods relying on human annotation. Natural Language Processing (NLP) and Machine Learning-based approaches show promise. We develop and evaluate an NLP pipeline con...

Machine learning prediction of mortality in pediatric fungemia using the Candida score.

Scientific reports
Pediatric fungemia in pediatric intensive care units (PICUs) carries high mortality. We evaluated whether the Candida Score, combined with clinical variables, predicts mortality after diagnosis using a prespecified multivariable logistic regression (...

Using machine learning to predict student outcomes for early intervention and formative assessment.

Scientific reports
The increasing importance of early prediction of student performance has led to research into machine learning models that can be used to assess student outcomes more accurately.This study focused on developing a predictive model based on machine lea...

Using Digital Phenotypes to Identify Individuals With Alexithymia in Posttraumatic Stress Disorder: Cross-Sectional Study.

JMIR mental health
BACKGROUND: Alexithymia, defined as difficulty identifying and describing one's emotions, has been identified as a transdiagnostic emotional process that impacts the course, severity, and treatment outcomes of psychiatric conditions such as posttraum...

Ultrasensitive SERS-LFA for the detection of neurofilament light chain and machine learning-assisted Alzheimer's disease classification.

Nanoscale
Neurofilament light chain (NfL), a cytoskeletal protein released during neuronal injury, is a promising biomarker, with elevated levels consistently associated with disease severity and progression in multiple neurological conditions, including Alzhe...

Comparing machine learning, deep learning, and reinforcement learning performance in Culex pipiens predictive modeling.

PloS one
Several machine learning (ML) and deep learning (DL) methods have been used to predict the presence of species in classification problems. Another set of methods, called reinforcement learning (RL), has been used in training agents to perform various...