AIMC Topic: Middle Aged

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Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Given the challenges faced during percutaneous coronary intervention (PCI) for heavily calcified lesions, accurately predicting PCI success is crucial for enhancing patient outcomes and optimizing procedural strategies.

Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis.

JMIR cancer
BACKGROUND: Surgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumors, until diagnostic surgery is performed.

SPP1 promotes malignant characteristics and drug resistance in hepatocellular carcinoma by activating fatty acid metabolic pathway.

Functional & integrative genomics
Hepatocellular carcinoma (HCC) progression and prognosis are influenced by various molecular markers. This study aimed to identify the hub gene associated with HCC clinical characteristics and its role in HCC progression. Differentially expressed gen...

Urine-based Raman markers for prostate cancer diagnosis: A machine learning approach using fingerprint and lipid spectral region.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study investigates the potential of Raman spectroscopy in distinguishing between healthy individuals and prostate cancer patients using urine samples. The Boruta algorithm was applied to Raman spectral data in two distinct wavenumber regions: 80...

A noninvasive and highly efficient epigenetic predictive model for efficacy of the GOLP regimen in patients with intrahepatic cholangiocarcinoma.

Cancer letters
The GOLP regimen (Gemcitabine, Oxaliplatin, Lenvatinib and anti-PD1 antibody) has been a promising first-line treatment for advanced intrahepatic cholangiocarcinoma (iCCA). A noninvasive tool to predict the response to GOLP regimen is needed for clin...

Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer.

Journal of translational medicine
BACKGROUND: Accurate prediction of pathological complete response (pCR) to neoadjuvant chemotherapy has significant clinical utility in the management of breast cancer treatment. Although multimodal deep learning models have shown promise for predict...

Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025.

BMC medical informatics and decision making
BACKGROUND: Adherence with Anti-Retroviral Therapy (ART) reduces viral load, as well as HIV-related morbidity and mortality. Despite the expanded availability of ART, non-adherence remains a series problem, leads increased viral load, a decline CD4 c...

Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome.

BMC microbiology
Hyperuricemia (HUA) and gout result from imbalances in uric acid metabolism and are closely associated with the gut microbiota. Advanced analytical methods facilitate the exploration of microbiota complexity. In this study, 16S rRNA sequencing data f...