AIMC Topic: Middle Aged

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Characterizing patients at higher cardiovascular risk for prescribed stimulants: Learning from health records data with predictive analytics and data mining techniques.

Computers in biology and medicine
OBJECTIVE: Given the significantly increased number of individuals prescribed stimulants in the past decade, there has been growing concern regarding the risk of cardiovascular events among adults on stimulant therapy. We aimed to quantify the added ...

Development of a Machine-Learning Algorithm to Identify Cauda Equina Compression on Magnetic Resonance Imaging Scans.

World neurosurgery
OBJECTIVE: Cauda equina syndrome (CES) poses significant neurological risks if untreated. Diagnosis relies on clinical and radiological features. As the symptoms are often nonspecific and common, the diagnosis is usually made after a magnetic resonan...

Intracranial stenosis prediction using a small set of risk factors in the Tromsø Study.

BMC medical informatics and decision making
Intracranial atherosclerotic stenosis (ICAS) refers to a narrowing of intracranial arteries due to plaque buildup on the inside of the vessel walls restricting blood flow. Early detection of ICAS is crucial to prevent serious consequences such as str...

Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer.

BMC cancer
BACKGROUND: Bladder cancer (BLCA) exists a profound molecular diversity, with basal and luminal subtypes having different prognostic and therapeutic outcomes. Traditional methods for molecular subtyping are often time-consuming and resource-intensive...

Personalised screening tool for early detection of sarcopenia in stroke patients: a machine learning-based comparative study.

Aging clinical and experimental research
BACKGROUND: Sarcopenia is a common complication in patients with stroke, adversely affecting recovery and increasing mortality risk. However, no standardised tool exists for its screening in this population. This study aims to identify factors influe...

Predicting hepatocellular carcinoma survival with artificial intelligence.

Scientific reports
Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfactory. The aim of this research was to evaluate the ability of machine learning (ML) methods in pred...

Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.

Journal of cancer research and clinical oncology
PURPOSE: The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried to develop a model to predict the FIGO stage of OC through machine learning algorithms with patients...

Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography.

JCO clinical cancer informatics
PURPOSE: Primary barriers to application of immune checkpoint inhibitor (ICI) therapy for cancer include severe side effects (such as potentially life threatening pneumonitis [PN]), which can cause the discontinuation of treatment. Predicting which p...

Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach.

PloS one
Inter-individual cognitive variability, influenced by genetic and environmental factors, is crucial for understanding typical cognition and identifying early cognitive disorders. This study investigated the association between serum protein expressio...

Identification of a novel hypermethylation marker, ZSCAN18, and construction of a diagnostic model in cervical cancer.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Cervical cancer (CC), a common female malignancy, has been linked to alterations in DNA methylation. This study employed an integrated "dry-wet lab" strategy combining bioinformatics, machine learning, and experimental validation to identify...