AIMC Topic: Machine Learning

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ARTIFICIAL INTELLIGENCE AND DEEP LEARNING IN DIAGNOSTIC RADIOLOGY-IS THIS THE NEXT PHASE OF SCIENTIFIC AND TECHNOLOGICAL DEVELOPMENT?

Radiation protection dosimetry
This paper is concerned with the role of science and technology in helping to create change in society. Diagnostic radiology is an example of an activity that has undergone significant change due to such developments, which over the past 40 years hav...

Introduction to the special issue on machine learning in acoustics.

The Journal of the Acoustical Society of America
The use of machine learning (ML) in acoustics has received much attention in the last decade. ML is unique in that it can be applied to all areas of acoustics. ML has transformative potentials as it can extract statistically based new information abo...

Machine learning evaluates changes in functional connectivity under a prolonged cognitive load.

Chaos (Woodbury, N.Y.)
One must be aware of the black-box problem by applying machine learning models to analyze high-dimensional neuroimaging data. It is due to a lack of understanding of the internal algorithms or the input features upon which most models make decisions ...

Classification of imbalanced oral cancer image data from high-risk population.

Journal of biomedical optics
SIGNIFICANCE: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often ...

Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients.

Open heart
OBJECTIVES: Identifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health record (EHR)-based machine-learning (ML) models can improve CVD risk stratification compared with a...

Machine Learning for Medical Coding in Healthcare Surveys.

Vital and health statistics. Ser. 1, Programs and collection procedures
Objectives Medical coding, or the translation of healthcare information into numeric codes, is expensive and time intensive. This exploratory study evaluates the use of machine learning classifiers to perform automated medical coding for large statis...

Machine learning for outcome predictions of patients with trauma during emergency department care.

BMJ health & care informatics
OBJECTIVES: To develop and evaluate a machine learning model for predicting patient with trauma mortality within the US emergency departments.

Machine learning in orthodontics: Challenges and perspectives.

Advances in clinical and experimental medicine : official organ Wroclaw Medical University
Artificial intelligence (AI) applications have significantly improved our everyday quality of life. The last decade has witnessed the emergence of up-and-coming applications in the field of dentistry. It is hopeful that AI, especially machine learnin...

Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach.

Journal for immunotherapy of cancer
BACKGROUND: Treatment with immune checkpoint inhibitors (ICIs) has been associated with an increased rate of cardiac events. There are limited data on the risk factors that predict cardiac events in patients treated with ICIs. Therefore, we created a...