[PDF][PDF] Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation.
CF Aliferis, A Statnikov, I Tsamardinos, S Mani… - Journal of Machine …, 2010 - jmlr.org
We present an algorithmic framework for learning local causal structure around target
variables of interest in the form of direct causes/effects and Markov blankets applicable to …
variables of interest in the form of direct causes/effects and Markov blankets applicable to …
Data from clinical notes: a perspective on the tension between structure and flexible documentation
Clinical documentation is central to patient care. The success of electronic health record
system adoption may depend on how well such systems support clinical documentation. A …
system adoption may depend on how well such systems support clinical documentation. A …
Data-driven approach to detect and predict adverse drug reactions
TB Ho, L Le, DT Thai, S Taewijit - Current pharmaceutical …, 2016 - ingentaconnect.com
Background: Many factors that directly or indirectly cause adverse drug reaction (ADRs)
varying from pharmacological, immunological and genetic factors to ethnic, age, gender …
varying from pharmacological, immunological and genetic factors to ethnic, age, gender …
[HTML][HTML] Extracting drug–drug interactions from literature using a rich feature-based linear kernel approach
Identifying unknown drug interactions is of great benefit in the early detection of adverse
drug reactions. Despite existence of several resources for drug–drug interaction (DDI) …
drug reactions. Despite existence of several resources for drug–drug interaction (DDI) …
[HTML][HTML] Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data
The availability of large-scale metagenomic sequencing data can facilitate the
understanding of microbial ecosystems in unprecedented detail. However, current …
understanding of microbial ecosystems in unprecedented detail. However, current …
Automated acquisition of disease–drug knowledge from biomedical and clinical documents: an initial study
Objective: Explore the automated acquisition of knowledge in biomedical and clinical
documents using text mining and statistical techniques to identify disease-drug associations …
documents using text mining and statistical techniques to identify disease-drug associations …
[图书][B] Gentle introduction to support vector machines in biomedicine, A-volume 2: case studies and benchmarks
Support Vector Machines (SVMs) are among the most important recent developments in
pattern recognition and statistical machine learning. They have found a great range of …
pattern recognition and statistical machine learning. They have found a great range of …
Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction
V Suárez-Paniagua, I Segura-Bedmar - BMC bioinformatics, 2018 - Springer
Abstract Background Deep Neural Networks (DNN), in particular, Convolutional Neural
Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug …
Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug …
[HTML][HTML] Using a shallow linguistic kernel for drug–drug interaction extraction
I Segura-Bedmar, P Martinez… - Journal of biomedical …, 2011 - Elsevier
A drug–drug interaction (DDI) occurs when one drug influences the level or activity of
another drug. Information Extraction (IE) techniques can provide health care professionals …
another drug. Information Extraction (IE) techniques can provide health care professionals …
An efficient pattern recognition based method for drug-drug interaction diagnosis
The diagnosis of interactions between two drugs is an essential procedure in drug
development. Many medical tool's offer inclusive records related to DDI. However, this tool's …
development. Many medical tool's offer inclusive records related to DDI. However, this tool's …