Trends and features of the applications of natural language processing techniques for clinical trials text analysis
Natural language processing (NLP) is an effective tool for generating structured information
from unstructured data, the one that is commonly found in clinical trial texts. Such …
from unstructured data, the one that is commonly found in clinical trial texts. Such …
High-dimensional linear discriminant analysis classifier for spiked covariance model
Linear discriminant analysis (LDA) is a popular classifier that is built on the assumption of
common population covariance matrix across classes. The performance of LDA depends …
common population covariance matrix across classes. The performance of LDA depends …
Deep feature learning for disease risk assessment based on convolutional neural network with intra-layer recurrent connection by using hospital big data
This paper presents the analysis of real-life medical big data obtained from a hospital in
central China from 2013 to 2015 for risk assessment of cerebral infarction disease. We …
central China from 2013 to 2015 for risk assessment of cerebral infarction disease. We …
Machine Learning Methods in Student Mental Health Research: An Ethics-Centered Systematic Literature Review.
M Drira, S Ben Hassine, M Zhang… - Applied Sciences (2076 …, 2024 - search.ebscohost.com
This study conducts an ethics-centered analysis of the AI/ML models used in Student Mental
Health (SMH) research, considering the ethical principles of fairness, privacy, transparency …
Health (SMH) research, considering the ethical principles of fairness, privacy, transparency …
Social image captioning: Exploring visual attention and user attention
Image captioning with a natural language has been an emerging trend. However, the social
image, associated with a set of user-contributed tags, has been rarely investigated for a …
image, associated with a set of user-contributed tags, has been rarely investigated for a …
MP2SDA: Multi-Party Parallelized Sparse Discriminant Learning
Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of
classical Fisher's Linear Discriminant Analysis in supervised metric learning, feature …
classical Fisher's Linear Discriminant Analysis in supervised metric learning, feature …
Crledd: Regularized causalities learning for early detection of diseases using electronic health record (ehr) data
The availability of Electronic Health Records (EHR) in health care settings has provided
tremendous opportunities for early disease detection. While many supervised learning …
tremendous opportunities for early disease detection. While many supervised learning …
Improving covariance-regularized discriminant analysis for EHR-based predictive analytics of diseases
Abstract Linear Discriminant Analysis (LDA) is a well-known technique for feature extraction
and dimension reduction. The performance of classical LDA however, significantly degrades …
and dimension reduction. The performance of classical LDA however, significantly degrades …
Connection and Curation of Corpus (Labeled and Unlabeled)
S Dev, A Sharan - Text Mining Approaches for Biomedical Data, 2024 - Springer
The availability of large amount of unlabeled and unstructured real-time textual data poses a
challenge for training models in various tasks. Labeled data is essential for both training and …
challenge for training models in various tasks. Labeled data is essential for both training and …
OGM: Online gaussian graphical models on the fly
Abstract Gaussian Graphical Model is widely used to understand the dependencies
between variables from high-dimensional data and can enable a wide range of applications …
between variables from high-dimensional data and can enable a wide range of applications …