AIMC Topic: Female

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How Employability Attributes Mediate the Link Between Knowledge Workers' Career Adaptation Concerns and Their Self-Perceived Employability.

Psychological reports
The study examines employability attributes as psychological mechanisms that explain the link between the career adaptation concerns and self-perceived employability of a sample of professionally qualified knowledge workers (N = 404). A cross-section...

Impact of the rise of artificial intelligence in radiology: What do radiologists think?

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to assess the perception, knowledge, wishes and expectations of a sample of French radiologists towards the rise of artificial intelligence (AI) in radiology.

Deep learning-based survival prediction of oral cancer patients.

Scientific reports
The Cox proportional hazards model commonly used to evaluate prognostic variables in survival of cancer patients may be too simplistic to properly predict a cancer patient's outcome since it assumes that the outcome is a linear combination of covaria...

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.

PloS one
OBJECTIVE: To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the ...

Development of a machine learning algorithm for prediction of failure of nonoperative management in spinal epidural abscess.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Data regarding risk of failure of nonoperative management in spinal epidural abscess (SEA) are limited. Given the potential for deterioration with treatment failure, a tool that predicts the probability of failure would be of grea...

Adaptive neural network classifier for decoding MEG signals.

NeuroImage
We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowin...

Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review.

Digestive diseases and sciences
Risk stratification of patients with gastrointestinal bleeding (GIB) is recommended, but current risk assessment tools have variable performance. Machine learning (ML) has promise to improve risk assessment. We performed a systematic review to evalua...

Can machine learning predict resecability of a peritoneal carcinomatosis?

Surgical oncology
BACKGROUND: Approximately 20% of initially eligible patients in a HIPEC procedure eventually underwent a simple surgical exploration. These procedures are called 'open & close' (O & C) representing up to 48% of surgery. The objective of this study wa...

Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters.

Journal of pharmacological sciences
Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to ...