Artificial intelligence in surgery
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery
remain relatively nascent. Here we review the integration of AI in the field of surgery …
remain relatively nascent. Here we review the integration of AI in the field of surgery …
Clinical errors from acronym use in electronic health record: A review of NLP-based disambiguation techniques
The adoption of Electronic Health Record (EHR) and other e-health infrastructures over the
years has been characterized by an increase in medical errors. This is primarily a result of …
years has been characterized by an increase in medical errors. This is primarily a result of …
[HTML][HTML] A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images
Malaria represents a potentially fatal communicable illness triggered by the Plasmodium
parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes …
parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes …
Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson's Disease
Artificial intelligence (AI) systems are increasingly used in healthcare applications, although
some challenges have not been completely overcome to make them fully trustworthy and …
some challenges have not been completely overcome to make them fully trustworthy and …
Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data
Alzheimer's disease (AD) is the most common form of dementia. Early and accurate
detection of AD is crucial to plan for disease modifying therapies that could prevent or delay …
detection of AD is crucial to plan for disease modifying therapies that could prevent or delay …
Predictive modeling and insight into protein fouling in microfiltration and ultrafiltration through one-dimensional convolutional models
Membrane fouling, a critical issue in membrane performance associated with proteins, is
often difficult to analyze due to limited knowledge of contamination scenarios. This study …
often difficult to analyze due to limited knowledge of contamination scenarios. This study …
Automated machine learning with interpretation: a systematic review of methodologies and applications in healthcare
Abstract Machine learning (ML) has achieved substantial success in performing healthcare
tasks in which the configuration of every part of the ML pipeline relies heavily on technical …
tasks in which the configuration of every part of the ML pipeline relies heavily on technical …
Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics
L Jovanovic, R Damaševičius, R Matic, M Kabiljo… - PeerJ Computer …, 2024 - peerj.com
Neurodegenerative conditions significantly impact patient quality of life. Many conditions do
not have a cure, but with appropriate and timely treatment the advance of the disease could …
not have a cure, but with appropriate and timely treatment the advance of the disease could …
[HTML][HTML] Explainable artificial intelligence-machine learning models to estimate overall scores in tertiary preparatory general science course
Educational data mining is valuable for uncovering latent relationships in educational
settings, particularly for predicting students' academic performance. This study introduces an …
settings, particularly for predicting students' academic performance. This study introduces an …
Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review
L van Gelderen, C Tejedor-García - arXiv preprint arXiv:2407.17844, 2024 - arxiv.org
Parkinson's disease (PD), the second most prevalent neurodegenerative disorder
worldwide, frequently presents with early-stage speech impairments. Recent advancements …
worldwide, frequently presents with early-stage speech impairments. Recent advancements …