AIMC Topic: Machine Learning

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Identification of Suicide Attempt Risk Factors in a National US Survey Using Machine Learning.

JAMA psychiatry
IMPORTANCE: Because more than one-third of people making nonfatal suicide attempts do not receive mental health treatment, it is essential to extend suicide attempt risk factors beyond high-risk clinical populations to the general adult population.

Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVES: To create a machine-learning model identifying potentially avoidable blood draws for serum potassium among pediatric patients following cardiac surgery.

Machine Learning for Surgical Phase Recognition: A Systematic Review.

Annals of surgery
OBJECTIVE: To provide an overview of ML models and data streams utilized for automated surgical phase recognition.

Real-Time Analysis of the Dynamic Foot Function: A Machine Learning and Finite Element Approach.

Journal of biomechanical engineering
Finite element analysis (FEA) has been widely used to study foot biomechanics and pathological functions or effects of therapeutic solutions. However, development and analysis of such foot modeling is complex and time-consuming. The purpose of this s...

Tractography Processing with the Sparse Closest Point Transform.

Neuroinformatics
We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them...

The Future Role of Machine Learning in Clinical Transplantation.

Transplantation
The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increa...

Progress in robotics for combating infectious diseases.

Science robotics
The world was unprepared for the COVID-19 pandemic, and recovery is likely to be a long process. Robots have long been heralded to take on dangerous, dull, and dirty jobs, often in environments that are unsuitable for humans. Could robots be used to ...

Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of old...