AIMC Topic: Machine Learning

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Machine-learning approaches to substance-abuse research: emerging trends and their implications.

Current opinion in psychiatry
PURPOSE OF REVIEW: To provide an accessible overview of some of the most recent trends in the application of machine learning to the field of substance use disorders and their implications for future research and practice.

Can machine-learning methods really help predict suicide?

Current opinion in psychiatry
PURPOSE OF REVIEW: In recent years there has been interest in the use of machine learning in suicide research in reaction to the failure of traditional statistical methods to produce clinically useful models of future suicide. The current review summ...

A Novel Machine Learning Model Developed to Assist in Patient Selection for Outpatient Total Shoulder Arthroplasty.

The Journal of the American Academy of Orthopaedic Surgeons
INTRODUCTION: Patient selection for outpatient total shoulder arthroplasty (TSA) is important to optimizing patient outcomes. This study aims to develop a machine learning tool that may aid in patient selection for outpatient total should arthroplast...

A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables.

International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists
Benign normal (NL), premalignant (endometrial intraepithelial neoplasia, EIN) and malignant (cancer, EMCA) endometria must be precisely distinguished for optimal management. EIN was objectively defined previously as a regression model incorporating m...

The Classification of Scientific Literature for Its Topical Tracking on a Small Human-Prepared Dataset.

Studies in health technology and informatics
The number of scientific publications is constantly growing to make their processing extremely time-consuming. We hypothesized that a user-defined literature tracking may be augmented by machine learning on article summaries. A specific dataset of 67...

PrositNG - A Machine Learning Supported Disease Model Generation Software.

Studies in health technology and informatics
Decision models (DM), especially Markov Models, play an essential role in the economic evaluation of new medical interventions. The process of DM generation requires expert knowledge of the medical domain and is a time-consuming task. Therefore, the ...

Using Machine Learning Algorithms to Predict Antimicrobial Resistance and Assist Empirical Treatment.

Studies in health technology and informatics
Multi-drug-resistant (MDR) infections and their devastating consequences constitute a global problem and a constant threat to public health with immense costs for their treatment. Early identification of the pathogen and its antibiotic resistance pro...

Comparison of Machine Learning Algorithms for Classifying Adverse-Event Related 30-Day Hospital Readmissions: Potential Implications for Patient Safety.

Studies in health technology and informatics
Studies in the last decade have focused on identifying patients at risk of readmission using predictive models, in an objective to decrease costs to the healthcare system. However, real-time models specifically identifying readmissions related to hos...