A survey of deep active learning
Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …
Advanced internet of things for personalised healthcare systems: A survey
As a new revolution of the Internet, Internet of Things (IoT) is rapidly gaining ground as a
new research topic in many academic and industrial disciplines, especially in healthcare …
new research topic in many academic and industrial disciplines, especially in healthcare …
A comparative analysis of logistic regression, random forest and KNN models for the text classification
In the current generation, a huge amount of textual documents are generated and there is an
urgent need to organize them in a proper structure so that classification can be performed …
urgent need to organize them in a proper structure so that classification can be performed …
Natural language processing in radiology: a systematic review
Radiological reporting has generated large quantities of digital content within the electronic
health record, which is potentially a valuable source of information for improving clinical care …
health record, which is potentially a valuable source of information for improving clinical care …
Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology
TM Noguerol, F Paulano-Godino… - Journal of the American …, 2019 - Elsevier
Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning
(ML), has become a reality in clinical practice. Since the end of the last century, several ML …
(ML), has become a reality in clinical practice. Since the end of the last century, several ML …
A survey of active learning for text classification using deep neural networks
C Schröder, A Niekler - arXiv preprint arXiv:2008.07267, 2020 - arxiv.org
Natural language processing (NLP) and neural networks (NNs) have both undergone
significant changes in recent years. For active learning (AL) purposes, NNs are, however …
significant changes in recent years. For active learning (AL) purposes, NNs are, however …
Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
Background Clinical text classification is an fundamental problem in medical natural
language processing. Existing studies have cocnventionally focused on rules or knowledge …
language processing. Existing studies have cocnventionally focused on rules or knowledge …
Boosting deep learning risk prediction with generative adversarial networks for electronic health records
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied
opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests …
opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests …
Natural language processing for EHR-based computational phenotyping
This article reviews recent advances in applying natural language processing (NLP) to
Electronic Health Records (EHRs) for computational phenotyping. NLP-based …
Electronic Health Records (EHRs) for computational phenotyping. NLP-based …
Driver distraction detection using semi-supervised machine learning
Real-time driver distraction detection is the core to many distraction countermeasures and
fundamental for constructing a driver-centered driver assistance system. While data-driven …
fundamental for constructing a driver-centered driver assistance system. While data-driven …