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Medical Equipment Replacement Prioritisation: A Comparison Between Linear and Fuzzy System Models.

Studies in health technology and informatics
In hospital management, health technology assessment techniques are being increasingly developed. This paper presents a comparison of the results obtained using two models for replacement priority value calculation applied to the Galliera hospital in...

Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering.

Neuroinformatics
Amyotrophic lateral sclerosis (ALS) is a complex progressive neurodegenerative disorder with an estimated prevalence of about 5 per 100,000 people in the United States. In this study, the ALS disease progression is measured by the change of Amyotroph...

Feasibility study: Towards Estimation of Fatigue Level in Robot-Assisted Exercise for Cardiac Rehabilitation.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Socially Assistive Robotics (SAR) has shown to be an important tool to assist patients in physical rehabilitation. SAR is used to provide feedback about patient's state and performance to users and health professionals, therefore, patients are monito...

Quantifying risk factors in medical reports with a context-aware linear model.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We seek to quantify the mortality risk associated with mentions of medical concepts in textual electronic health records (EHRs). Recognizing mentions of named entities of relevant types (eg, conditions, symptoms, laboratory tests or behavi...

A new ensemble residual convolutional neural network for remaining useful life estimation.

Mathematical biosciences and engineering : MBE
Remaining useful life (RUL) estimation is one of the most important component in prognostic health management (PHM) system in modern industry. It defined as the length from the current time to the end of the useful life. With the rapid development of...

Imaging Connectomics and the Understanding of Brain Diseases.

Advances in experimental medicine and biology
Neuroimaging-based personalized medicine is emerging to characterize brain disorders and their evolution at the patient level. In this chapter, we present the most classic methods used to infer large-scale brain connectivity based on functional MRI. ...

Wastewater treatment plant performance analysis using artificial intelligence - an ensemble approach.

Water science and technology : a journal of the International Association on Water Pollution Research
In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regres...

Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.

The plant genome
New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive natur...

Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting.

IEEE transactions on medical imaging
Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework using a deep learning-based prior. We utilized the denoising convolutional neural network (DnCNN) meth...

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning.

Combinatorial chemistry & high throughput screening
BACKGROUND: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.