AIMC Topic: Machine Learning

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Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.

Clinical chemistry and laboratory medicine
OBJECTIVES: The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expen...

Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.

Frontiers in endocrinology
BACKGROUND: Central lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic central lymph node dissection is still controversial. There are no reliable models for predicting CLNM. Th...

New Insights Into Learning With Correntropy-Based Regression.

Neural computation
Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively studied and explored. Its application to regression problems leads to the robustness-enhanced regression para...

Information-Theoretic Representation Learning for Positive-Unlabeled Classification.

Neural computation
Recent advances in weakly supervised classification allow us to train a classifier from only positive and unlabeled (PU) data. However, existing PU classification methods typically require an accurate estimate of the class-prior probability, a critic...

Breast ultrasound lesion classification based on image decomposition and transfer learning.

Medical physics
PURPOSE: In medical image analysis, deep learning has great application potential. Discovering a method for extracting valuable information from medical images and integrating that information closely with medical treatment has recently become a majo...

Role of machine learning in gait analysis: a review.

Journal of medical engineering & technology
Human biomechanics and gait form an integral part of life. The gait analysis involves a large number of interdependent parameters that were difficult to interpret due to a vast amount of data and their inter-relations. To simplify evaluation, the int...

Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning.

Scientific reports
For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in hors...

Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study.

BMC medical research methodology
BACKGROUND: Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with res...

Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study.

PloS one
BACKGROUND & AIMS: Liver ultrasound scan (US) use in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) causes costs and waiting lists overloads. We aimed to compare various Machine learning algorithms with a Meta learner approach to find the best ...