AIMC Topic: Machine Learning

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RIPTOSO: The development of a screening tool for adverse events during forensic-psychiatric inpatient treatments of offenders with schizophrenia spectrum disorders.

Psychiatry research
Adverse events such as compulsory measures, absconding, illicit substance use, self-harm, aggressive behavior, and prolonged hospitalization pose significant challenges in forensic psychiatric inpatient care. This study introduces a machine learning-...

Development and Validation of a Machine Learning-Based Predictive Model for Postoperative Frailty in Patients with Non-Small Cell Lung Cancer and Its Relation to Early Recovery.

Annals of surgical oncology
PURPOSE: This study was designed to evaluate the postoperative frailty status of patients with non-small cell lung cancer, identify influencing factors, establish a machine learning-based prediction model, and explore the correlation between frailty ...

Enhancing PI3Kγ inhibitor discovery: a machine learning-based virtual screening approach integrating pharmacophores, docking, and molecular descriptors.

Molecular diversity
PI3Kγ is a lipid kinase that is expressed primarily in leukocytes and plays a significant role in tumors, inflammation, and autoimmune diseases. Consequently, considerable attention has been given to the development of pharmacological inhibitors of P...

Breast cancer neoadjuvant therapy outcome prediction based on clinical patient and tumor features: A cross-sectional study.

Current problems in cancer
INTRODUCTION: Breast cancer is the most common malignant disease in the female population and one of the most common diseases in developed countries. Many factors which may impact the development and outcome of this complex disease have been investig...

Development of patient-specific apparent blood viscosity predictive models for computational fluid dynamics analysis of intracranial aneurysms with machine learning approaches.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: A model to predict patient-specific apparent viscosity as a computational condition in computational fluid dynamics (CFD) analysis, which is used in research on intracranial aneurysms, is important. The purpose of this stud...

A novel approach for ECG signal classification using sliding Euclidean quantization and bitwise pattern encoding.

Computer methods in biomechanics and biomedical engineering
This study aims to introduce a novel, computationally lightweight feature extraction technique called Sliding Euclidean Pattern Quantization (SEPQ), which encodes local morphological patterns of ECG signals using Euclidean distance-based binary repre...

Machine learning-based approaches for distinguishing viral and bacterial pneumonia in paediatrics: A scoping review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Pneumonia is the leading cause of hospitalisation and mortality among children under five, particularly in low-resource settings. Accurate differentiation between viral and bacterial pneumonia is essential for guiding approp...

Algorithmic approaches in hand imaging for rheumatic musculoskeletal diseases: A systematic literature review.

Seminars in arthritis and rheumatism
OBJECTIVE: This systematic literature review provides a comprehensive overview of the use of machine learning (ML) in hand imaging of rheumatic musculoskeletal diseases (RMDs). The review evaluates ML algorithms, imaging modalities, patient populatio...

TopCysteineDB: A Cysteinome-wide Database Integrating Structural and Chemoproteomics Data for Cysteine Ligandability Prediction.

Journal of molecular biology
Development of targeted covalent inhibitors and covalent ligand-first approaches have emerged as a powerful strategy in drug design, with cysteines being attractive targets due to their nucleophilicity and relative scarcity. While structural biology ...

A systematic review of AI as a digital twin for prostate cancer care.

Computer methods and programs in biomedicine
Artificial Intelligence (AI) and Digital Twin (DT) technologies are rapidly transforming healthcare, offering the potential for personalized, accurate, and efficient medical care. This systematic review focuses on the intersection of AI-based digital...