Risk Management

Artificial immune system features added to breast cancer clinical data for machine learning (ML) applications.

We here propose a new method of combining a mathematical model that describes a chemotherapy treatment for breast cancer with a machine-learning (ML) algorithm to increase performance in predicting tumor size using a five-step procedure. The first step involves modeling the chemotherapy treatment protocol using an analytical function. In the second step, the ML algorithm is trained to predict the ...

Automatic medical protocol classification using machine learning approaches.

BACKGROUND AND OBJECTIVE: Assignment of medical imaging procedure protocols requires extensive knowledge about patient's data, usually included in radiological request forms and radiological reports. Assignment of protocol is required prior to radiological study acquisition, determining procedure for each patient. The automation of this protocol assignment process could improve the efficiency of p...

Deep learning magnetic resonance spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data.

Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disorders. 1H magnetic resonance spectroscopy (MRS) is ...

Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system.

Visual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of th...

Extended Robust Exponential Stability of Fuzzy Switched Memristive Inertial Neural Networks With Time-Varying Delays on Mode-Dependent Destabilizing Impulsive Control Protocol.

This article investigates the problem of robust exponential stability of fuzzy switched memristive inertial neural networks (FSMINNs) with time-varyin...

The diagnosis of tuberculous meningitis in adults and adolescents: protocol for a systematic review and individual patient data meta-analysis to inform a multivariable prediction model.

Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor ...

Application of machine learning to improve dairy farm management: A systematic literature review.

In recent years, several researchers and practitioners applied machine learning algorithms in the dairy farm context and discussed several solutions t...

Hip Fracture Surgery without Transfusion in Patients with Hemoglobin Less Than 10 g/dL.

BACKGROUD: Hip fracture surgery is associated with blood loss, which may lead to adverse patient outcomes. The hemoglobin level declines gradually in ...

Intractable airsickness associated with COVID-19: A case report.

A 29-yr-old male Melicopter co-pilot with high flight hours in air transport missions of the patients with COVID-19 tested positive for his RT-PCR due...

Test-time adaptable neural networks for robust medical image segmentation.

Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations exp...

Intelligent checklists improve checklist compliance in the intensive care unit: a prospective before-and-after mixed-method study.

BACKGROUND: We examined whether a context and process-sensitive 'intelligent' checklist increases compliance with best practice compared with a paper ...

Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations.

Drug-induced cardiotoxicity is a potentially lethal and yet one of the most common side effects with the drugs in clinical use. Most of the drug-induc...

Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study.

Machine learning (ML)-based algorithms for cardiovascular disease (CVD) risk assessment have shown promise in clinical decisions. However, they usuall...

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.

INTRODUCTION: Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domain...

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second...

How Much Can Potential Jurors Tell Us About Liability for Medical Artificial Intelligence?

Text Classification for Clinical Trial Operations: Evaluation and Comparison of Natural Language Processing Techniques.

The ability to detect patterns and trends across protocol deviations (PDs) is key to ensure high data quality and sufficient oversight of patient safe...

A Machine Learning Protocol for Predicting Protein Infrared Spectra.

Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tediou...

Artificial intelligence in medicine: A matter of joy or concern?

Artificial Intelligence (AI), a concept which dates back to the 1950s, is increasingly being developed by many medical specialties, especially those b...

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population.

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for strok...