AIMC Topic: Machine Learning

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An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis.

Scientific reports
The modern development of healthcare is characterized by a set of large volumes of tabular data for monitoring and diagnosing the patient's condition. In addition, modern methods of data engineering allow the synthesizing of a large number of feature...

Machine learning predicts upper secondary education dropout as early as the end of primary school.

Scientific reports
Education plays a pivotal role in alleviating poverty, driving economic growth, and empowering individuals, thereby significantly influencing societal and personal development. However, the persistent issue of school dropout poses a significant chall...

Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites.

Scientific reports
This study addresses the challenge of accurately diagnosing sepsis subtypes in elderly patients, particularly distinguishing between Escherichia coli (E. coli) and non-E. coli infections. Utilizing machine learning, we conducted a retrospective analy...

AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features.

Scientific reports
Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficie...

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma.

Scientific reports
Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients' quality of life. As facial nerve outcomes re...

Ensemble machine learning using hydrometeorological information to improve modeling of quality parameter of raw water supplying treatment plants.

Journal of environmental management
Source and raw water quality may deteriorate due to rainfall and river flow events that occur in watersheds. The effects on raw water quality are normally detected in drinking water treatment plants (DWTPs) with a time-lag after these events in the w...

Oral anticoagulant treatment in atrial fibrillation: the AFIRMA real-world study using natural language processing and machine learning.

Revista clinica espanola
INTRODUCTION: Oral anticoagulation (OAC) is key in atrial fibrillation (AF) thromboprophylaxis, but Spain lacks substantial real-world evidence. We aimed to analyze the prevalence, clinical characteristics, and treatment patterns among patients with ...

Does lifelong learning matter for the subjective wellbeing of the elderly? A machine learning analysis on Singapore data.

PloS one
Our study explores whether lifelong learning is associated with the subjective wellbeing among the elderly in Singapore. Through a primary survey of 300 individuals aged 65 and above, we develop a novel index to capture three different aspects of sub...

Functional Neural Networks for High-Dimensional Genetic Data Analysis.

IEEE/ACM transactions on computational biology and bioinformatics
Artificial intelligence (AI) is a thriving research field with many successful applications in areas such as computer vision and speech recognition. Machine learning methods, such as artificial neural networks (ANN), play a central role in modern AI ...

Applications of Machine Learning in Prognostication of Mild Traumatic Brain Injury: A Systematic Review.

American journal of physical medicine & rehabilitation
OBJECTIVE: The aim of the study is to review the literature regarding the current state and clinical applicability of machine learning models in prognosticating the outcomes of patients with mild traumatic brain injury in the early clinical presentat...