AIMC Topic: Machine Learning

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In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study.

BMC medical informatics and decision making
BACKGROUND: Falls are one of the most common accidents in medical institutions, which can threaten the safety of inpatients and negatively affect their prognosis. Herein, we developed a machine learning (ML) model for fall prediction in patients with...

Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning.

PloS one
Language is both a cause and a consequence of the social processes that lead to conflict or peace. "Hate speech" can mobilize violence and destruction. What are the characteristics of "peace speech" that reflect and support the social processes that ...

Predictive modeling of dose-volume parameters of carcinoma tongue cases using machine learning models.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
The aim of this study is to create a single institution-based machine learning model for a dose prediction generation tool for post-operative carcinoma of the tongue cases prospectively. Intensity-modulated radiotherapy (IMRT) plans for 20 patients w...

Correlation Between Statin Use and Symptomatic Venous Thromboembolism Incidence in Patients With Ankle Fracture: A Machine Learning Approach.

Foot & ankle specialist
BACKGROUND: Identifying factors that correlate with the incidence of venous thromboembolism (VTE) has the potential to improve VTE prevention and positively influence decision-making regarding prophylaxis. In this study, we aimed to investigate the c...

Reducing Gadolinium Contrast With Artificial Intelligence.

Journal of magnetic resonance imaging : JMRI
Gadolinium contrast is an important agent in magnetic resonance imaging (MRI), particularly in neuroimaging where it can help identify blood-brain barrier breakdown from an inflammatory, infectious, or neoplastic process. However, gadolinium contrast...

A survey on few-shot class-incremental learning.

Neural networks : the official journal of the International Neural Network Society
Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples w...

Artificial intelligence in emergency medicine. A systematic literature review.

International journal of medical informatics
UNLABELLED: Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis...

Multi-Cat Monitoring System Based on Concept Drift Adaptive Machine Learning Architecture.

Sensors (Basel, Switzerland)
In multi-cat households, monitoring individual cats' various behaviors is essential for diagnosing their health and ensuring their well-being. This study focuses on the defecation and urination activities of cats, and introduces an adaptive cat ident...

Extracting medicinal chemistry intuition via preference machine learning.

Nature communications
The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects...