AIMC Topic: Machine Learning

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Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives.

Computational intelligence and neuroscience
Chronic kidney disease (CKD) is a global health issue with a high rate of morbidity and mortality and a high rate of disease progression. Because there are no visible symptoms in the early stages of CKD, patients frequently go unnoticed. The early de...

A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.

Clinical chemistry and laboratory medicine
OBJECTIVES: Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection...

Big Data/AI in Neurocritical Care: Maybe/Summary.

Neurocritical care
Big data (BD) and artificial intelligence (AI) have increasingly been used in neurocritical care. "BD" can be operationally defined as extremely large datasets that are so large and complex that they cannot be analyzed by using traditional statistica...

SurvNAM: The machine learning survival model explanation.

Neural networks : the official journal of the International Neural Network Society
An extension of the Neural Additive Model (NAM) called SurvNAM and its modifications are proposed to explain predictions of a black-box machine learning survival model. The method is based on applying the original NAM to solving the explanation probl...

Characterizing accident narratives with word embeddings: Improving accuracy, richness, and generalizability.

Journal of safety research
INTRODUCTION: Ensuring occupational health and safety is an enormous concern for organizations, as accidents not only harm workers but also result in financial losses. Analysis of accident data has the potential to reveal insights that may improve ca...

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning.

Sensors (Basel, Switzerland)
Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In t...

Evaluation of Machine Learning Methods for Monitoring the Health of Guyed Towers.

Sensors (Basel, Switzerland)
This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil's interconnected electrical system. The proposed method increases system reliability and minimiz...

The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life.

PLoS computational biology
The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-s...

Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users.

Pharmacoepidemiology and drug safety
BACKGROUND: Fluoroquinolones are associated with central (CNS) and peripheral (PNS) nervous system symptoms, and predicting the risk of these outcomes may have important clinical implications. Both LASSO and random forest are appealing modeling metho...

Understanding the potential of emerging digital technologies for improving road safety.

Accident; analysis and prevention
Each year, 1.35 million people are killed on the world's roads and another 20-50 million are seriously injured. Morbidity or serious injury from road traffic collisions is estimated to increase to 265 million people between 2015 and 2030. Current roa...