Surveys

One Algorithm May Not Fit All: How Selection Bias Affects Machine Learning Performance.

Machine learning (ML) algorithms have demonstrated high diagnostic accuracy in identifying and categorizing disease on radiologic images. Despite the results of initial research studies that report ML algorithm diagnostic accuracy similar to or exceeding that of radiologists, the results are less impressive when the algorithms are installed at new hospitals and are presented with new images. This ...

Biological batch normalisation: How intrinsic plasticity improves learning in deep neural networks.

In this work, we present a local intrinsic rule that we developed, dubbed IP, inspired by the Infomax rule. Like Infomax, this rule works by controlling the gain and bias of a neuron to regulate its rate of fire. We discuss the biological plausibility of the IP rule and compare it to batch normalisation. We demonstrate that the IP rule improves learning in deep networks, and provides networks with...

An Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population.

Frailty, one of the major public health problems in the elderly, can result from multiple etiologic factors including biological and physical changes ...

Patient reported outcome measures concerning urinary incontinence after robot assisted radical prostatectomy: development and validation of an online prediction model using clinical parameters, lower urinary tract symptoms and surgical experience.

The prediction of post-prostatectomy incontinence (PPI) after robot-assisted radical prostatectomy (RARP) depends on multiple clinical, anatomical and...

Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET.

Previous studies have demonstrated the feasibility of reducing noise with deep learning-based methods for low-dose fluorodeoxyglucose (FDG) positron e...

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals.

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous an...

Quantifying trunk neuromuscular control using seated balancing and stability threshold.

Performance during seated balancing is often used to assess trunk neuromuscular control, including evaluating impairments in back pain populations. Ba...

A Time Series Data Filling Method Based on LSTM-Taking the Stem Moisture as an Example.

In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the...

Artificial Intelligence-Driven Oncology Clinical Decision Support System for Multidisciplinary Teams.

Watson for Oncology (WfO) is a clinical decision support system driven by artificial intelligence. In Korea, WfO is used by multidisciplinary teams (M...

The FUTUREPAIN study: Validating a questionnaire to predict the probability of having chronic pain 7-10 years into the future.

OBJECTIVES: The FUTUREPAIN study develops a short general-purpose questionnaire, based on the biopsychosocial model, to predict the probability of dev...

Evidence-based medicine and machine learning: a partnership with a common purpose.

From its origins in epidemiology, evidence-based medicine has promulgated a rigorous approach to assessing the validity, impact and applicability of h...

A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols.

Multicenter magnetic resonance imaging is gaining more popularity in large-sample projects. Since both varying hardware and software across different ...

Evolution and impact of bias in human and machine learning algorithm interaction.

Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been re...

Artificial intelligence powered statistical genetics in biobanks.

Large-scale, sometimes nationwide, prospective genomic cohorts biobanking rich biological specimens such as blood, urine and tissues, have been establ...

On the use of machine learning algorithms in forensic anthropology.

The classification performance of the statistical methods binary logistic regression (BLR), multinomial and penalized multinomial logistic regression ...

Improving the Reliability of Pharmacokinetic Parameters at Dynamic Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach.

Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astr...

Test-Retest Reliability of Kinematic Assessments for Upper Limb Robotic Rehabilitation.

Robot-measured kinematic variables are increasingly used in neurorehabilitation to characterize motor recovery following stroke. However, few studies ...

Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques.

Food consumption patterns have undergone changes that in recent years have resulted in serious health problems. Studies based on the evaluation of the...

Reliability of the thumb localizing test and its validity against quantitative measures with a robotic device in patients with hemiparetic stroke.

OBJECTIVES: To examine the inter-rater reliability of the thumb localizing test (TLT) and its validity against quantitative measures of proprioception...

Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model.

It is an essential task to estimate the remaining useful life (RUL) of machinery in the mining sector aimed at ensuring the production and the custome...