AIMC Topic: Machine Learning

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Identifying dynamic regulation with machine learning using adversarial surrogates.

PloS one
Biological systems maintain stability of their function in spite of external and internal perturbations. An important challenge in studying biological regulation is to identify the control objectives based on empirical data. Very often these objectiv...

Recognition of flight cadets brain functional magnetic resonance imaging data based on machine learning analysis.

PloS one
The rapid advancement of the civil aviation industry has attracted significant attention to research on pilots. However, the brain changes experienced by flight cadets following their training remain, to some extent, an unexplored territory compared ...

A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens.

PloS one
Identifying protective antigens (PAs), i.e., targets for bacterial vaccines, is challenging as conducting in-vivo tests at the proteome scale is impractical. Reverse Vaccinology (RV) aids in narrowing down the pool of candidates through computational...

AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients.

PloS one
INTRODUCTION: In older patients, postoperative delirium (POD) is a major complication that can result in greater morbidity, longer hospital stays, and higher healthcare expenses. Accurate prediction models for POD can enhance patient outcomes by guid...

Empathy training for counselling novices: A randomized controlled trial using machine learning and natural language processing.

Psychology and psychotherapy
OBJECTIVE: Empathy is a critical skill for effective counselling, yet novice counsellors often struggle to develop it. Traditional training methods may not sufficiently address the complexities of empathic development. This study aims to develop and ...

Advancements and future trends in machine learning for lung cancer: a comprehensive bibliometric analysis.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
BACKGROUND: In recent years, significant progress has been made in lung cancer screening, diagnosis, and treatment with the continuous development of machine learning (ML).

Factors associated with coronary artery bypass grafting excess readmission ratios.

Surgery
BACKGROUND: The Hospital Readmissions Reduction Program determines Medicare readmission penalties through risk-adjusted excess readmissions ratios. This study uses interpretable machine learning to identify associations with coronary artery bypass gr...

Cognitive Lab: A dataset of biosignals and HCI features for cognitive process investigation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Attention, cognitive workload/fatigue, and emotional states significantly influence learning outcomes, cognitive performance, and human-machine interactions. However, existing assessment methodologies fail to fully capture t...

Predicting clinical outcomes using 18F-FDG PET/CT-based radiomic features and machine learning algorithms in patients with esophageal cancer.

Nuclear medicine communications
OBJECTIVE: This study evaluated the relationship between 18F-fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) radiomic features and clinical parameters, including tumor localization, histopathological subtype, lymph node metastasis, mortal...