AIMC Topic: Humans

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Exploring the Use of Natural Language Processing to Understand Emotions of Trainees and Faculty Regarding Entrustable Professional Activity Assessments.

Journal of graduate medical education
In medical education, artificial intelligence techniques such as natural language processing (NLP) are starting to be used to capture and analyze emotions through written text. To explore the application of NLP techniques to understand resident and...

The attentive reconstruction of objects facilitates robust object recognition.

PLoS computational biology
Humans are extremely robust in our ability to perceive and recognize objects-we see faces in tea stains and can recognize friends on dark streets. Yet, neurocomputational models of primate object recognition have focused on the initial feed-forward p...

Prediction Models for Intravenous Immunoglobulin Non-Responders of Kawasaki Disease Using Machine Learning.

Clinical drug investigation
BACKGROUND AND OBJECTIVE: Intravenous immunoglobulin (IVIG) is a prominent therapeutic agent for Kawasaki disease (KD) that significantly reduces the incidence of coronary artery anomalies. Various methodologies, including machine learning, have been...

Deep learning based ECG segmentation for delineation of diverse arrhythmias.

PloS one
Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T wave...

The efficacy of machine learning models in lung cancer risk prediction with explainability.

PloS one
Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagno...

Network science and explainable AI-based life cycle management of sustainability models.

PloS one
Model-based assessment of the potential impacts of variables on the Sustainable Development Goals (SDGs) can bring great additional information about possible policy intervention points. In the context of sustainability planning, machine learning tec...

The Impact of Botulinum Toxin Combined with Robot-Assisted Gait Training on Spasticity and Gross Motor Function on Children with Spastic Cerebral Palsy.

Developmental neurorehabilitation
OBJECTIVE: To evaluate the impact of combining botulinum toxin-A (BoNT-A) injection with robot-assisted gait training (RAGT) on lower limb spasticity and motor function in children with cerebral palsy.

Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection chal...

The Prediction of Recombination Hotspot Based on Automated Machine Learning.

Journal of molecular biology
Meiotic recombination plays a pivotal role in genetic evolution. Genetic variation induced by recombination is a crucial factor in generating biodiversity and a driving force for evolution. At present, the development of recombination hotspot predict...