AIMC Topic: Machine Learning

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Artificial Intelligence, Machine Learning, and Medicine: A Little Background Goes a Long Way Toward Understanding.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
Artificial intelligence (AI) and machine learning refer to computers built and programed by humans to perform tasks according to our design. This is vital to keep in mind as we try to understand the application of AI to medicine. AI is a tool with st...

Driftage: a multi-agent system framework for concept drift detection.

GigaScience
BACKGROUND: The amount of data and behavior changes in society happens at a swift pace in this interconnected world. Consequently, machine learning algorithms lose accuracy because they do not know these new patterns. This change in the data pattern ...

Big data and predictive modelling for the opioid crisis: existing research and future potential.

The Lancet. Digital health
A need exists to accurately estimate overdose risk and improve understanding of how to deliver treatments and interventions in people with opioid use disorder in a way that reduces such risk. We consider opportunities for predictive analytics and rou...

INTERMEDIATE AND DEEP CAPILLARY PLEXUSES IN MACHINE LEARNING SEGMENTATION OF HIGH-RESOLUTION OPTICAL COHERENCE TOMOGRAPHY IMAGING.

Retina (Philadelphia, Pa.)
PURPOSE: To describe imaging produced by machine learning-based segmentation of high-resolution optical coherence tomography imaging of the intermediate capillary plexus and deep capillary plexus, layers of vessels not imaged well by dye-based angiog...

Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction.

JAMA cardiology
IMPORTANCE: Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.

Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology.

Advances in nutrition (Bethesda, Md.)
The field of nutritional epidemiology faces challenges posed by measurement error, diet as a complex exposure, and residual confounding. The objective of this perspective article is to highlight how developments in big data and machine learning can h...

Predicting Antibiotic Resistance in Hospitalized Patients by Applying Machine Learning to Electronic Medical Records.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND: Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning (ML) algorithms. However, they are scarcely used for empiric antibiotic therapy. Here, we predict the antibiot...