AIMC Topic: Machine Learning

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

PloS one
OBJECTIVES: The FUTUREPAIN study develops a short general-purpose questionnaire, based on the biopsychosocial model, to predict the probability of developing or maintaining moderate-to-severe chronic pain 7-10 years into the future.

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

BMJ evidence-based medicine
From its origins in epidemiology, evidence-based medicine has promulgated a rigorous approach to assessing the validity, impact and applicability of hypothesis-driven empirical research used to evaluate the utility of diagnostic tests, prognostic too...

Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety.

Sensors (Basel, Switzerland)
To monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the...

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning.

Biomedical engineering online
BACKGROUND: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved exp...

Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks.

Journal of the Royal Society, Interface
A major issue in translation of the artificial intelligence platforms for automatic segmentation of echocardiograms to clinics is their generalizability. The present study introduces and verifies a novel generalizable and efficient fully automatic mu...

Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis.

Journal of electrocardiology
INTRODUCTION: Electrode misplacement and interchange errors are known problems when recording the 12‑lead electrocardiogram (ECG). Automatic detection of these errors could play an important role for improving clinical decision making and outcomes in...

Using Machine Learning to Make Predictions in Patients Who Fall.

The Journal of surgical research
BACKGROUND: As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model base...

Machine learning-based mortality rate prediction using optimized hyper-parameter.

Computer methods and programs in biomedicine
OBJECTIVE AND BACKGROUND: The current scenario of the Pandemic of COVID-19 demands multi-channel investigations and predictions. A variety of prediction models are available in the literature. The majority of these models are based on extrapolating b...

A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds.

Neural networks : the official journal of the International Neural Network Society
A new robust algorithm based on the explanation method SurvLIME called SurvLIME-KS is proposed for explaining machine learning survival models. The algorithm is developed to ensure robustness to cases of a small amount of training data or outliers of...

Machine learning in neurosurgery: a global survey.

Acta neurochirurgica
BACKGROUND: Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance...