AIMC Topic: Humans

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A New Fingerprint and Graph Hybrid Neural Network for Predicting Molecular Properties.

Journal of chemical information and modeling
Machine learning plays a role in accelerating drug discovery, and the design of effective machine learning models is crucial for accurately predicting molecular properties. Characterizing molecules typically involves the use of molecular fingerprints...

AI in Nursing: The Wild West?

The American journal of nursing
Survey highlights potential patient safety problems with AI-generated reports.

Integration of Tracking, Re-Identification, and Gesture Recognition for Facilitating Human-Robot Interaction.

Sensors (Basel, Switzerland)
For successful human-robot collaboration, it is crucial to establish and sustain quality interaction between humans and robots, making it essential to facilitate human-robot interaction (HRI) effectively. The evolution of robot intelligence now enabl...

A Comprehensive Survey on Emerging Assistive Technologies for Visually Impaired Persons: Lighting the Path with Visible Light Communications and Artificial Intelligence Innovations.

Sensors (Basel, Switzerland)
In the context in which severe visual impairment significantly affects human life, this article emphasizes the potential of Artificial Intelligence (AI) and Visible Light Communications (VLC) in developing future assistive technologies. Toward this p...

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sensors (Basel, Switzerland)
Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are c...

Detection of Unfocused EEG Epochs by the Application of Machine Learning Algorithm.

Sensors (Basel, Switzerland)
Electroencephalography (EEG) is a non-invasive method used to track human brain activity over time. The time-locked EEG to an external event is known as event-related potential (ERP). ERP can be a biomarker of human perception and other cognitive pro...

SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models.

Genome biology
Accurate inference of orthologous genes constitutes a prerequisite for comparative and evolutionary genomics. SonicParanoid is one of the fastest tools for orthology inference; however, its scalability and accuracy have been hampered by time-consumin...

Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).

European journal of medical research
BACKGROUND: Tuberculosis spondylitis (TS), commonly known as Pott's disease, is a severe type of skeletal tuberculosis that typically requires surgical treatment. However, this treatment option has led to an increase in healthcare costs due to prolon...

Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.

BMC medical informatics and decision making
INTRODUCTION: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI.