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A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data.

PloS one
The prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early intervention and can lead to higher success rates in treatment. Self-reported feelings by patients repre...

DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disease (AD). In particular, convolutional neural network (CNN)-based methods are highly sensitive to subtle changes caused by brain atrophy in medical ima...

Oral ketamine effects on dynamics of functional network connectivity in patients treated for chronic suicidality.

European archives of psychiatry and clinical neuroscience
The underlying brain mechanisms of ketamine in treating chronic suicidality and the characteristics of patients who will benefit from ketamine treatment remain unclear. To address these gaps, we investigated temporal variations of brain functional sy...

Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
PURPOSE: To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).

Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).

Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences
BACKGROUND: Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was designed to develop and evaluate machine learning algorithms for predicting the maintenance dose and du...

Colour fusion effect on deep learning classification of uveal melanoma.

Eye (London, England)
BACKGROUND: Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of th...

Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study.

Physical and engineering sciences in medicine
Surgical excision is the most effective treatment of skin carcinomas (basal cell carcinoma or squamous cell carcinoma). Preoperative assessment of tumoral margins plays a decisive role for a successful result. The aim of this work was to evaluate the...

A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics.

Computers in biology and medicine
Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. F...

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm.

Journal of applied clinical medical physics
BACKGROUND: CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice.