8,045 research outputs found
Chi-square-based scoring function for categorization of MEDLINE citations
Objectives: Text categorization has been used in biomedical informatics for
identifying documents containing relevant topics of interest. We developed a
simple method that uses a chi-square-based scoring function to determine the
likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our
procedure requires construction of a genetic and a nongenetic domain document
corpus. We used MeSH descriptors assigned to MEDLINE citations for this
categorization task. We compared frequencies of MeSH descriptors between two
corpora applying chi-square test. A MeSH descriptor was considered to be a
positive indicator if its relative observed frequency in the genetic domain
corpus was greater than its relative observed frequency in the nongenetic
domain corpus. The output of the proposed method is a list of scores for all
the citations, with the highest score given to those citations containing MeSH
descriptors typical for the genetic domain. Results: Validation was done on a
set of 734 manually annotated MEDLINE citations. It achieved predictive
accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method
by comparing it to three machine learning algorithms (support vector machines,
decision trees, na\"ive Bayes). Although the differences were not statistically
significantly different, results showed that our chi-square scoring performs as
good as compared machine learning algorithms. Conclusions: We suggest that the
chi-square scoring is an effective solution to help categorize MEDLINE
citations. The algorithm is implemented in the BITOLA literature-based
discovery support system as a preprocessor for gene symbol disambiguation
process.Comment: 34 pages, 2 figure
Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models
Background: In this paper we present the approaches and methods employed in
order to deal with a large scale multi-label semantic indexing task of
biomedical papers. This work was mainly implemented within the context of the
BioASQ challenge of 2014. Methods: The main contribution of this work is a
multi-label ensemble method that incorporates a McNemar statistical
significance test in order to validate the combination of the constituent
machine learning algorithms. Some secondary contributions include a study on
the temporal aspects of the BioASQ corpus (observations apply also to the
BioASQ's super-set, the PubMed articles collection) and the proper adaptation
of the algorithms used to deal with this challenging classification task.
Results: The ensemble method we developed is compared to other approaches in
experimental scenarios with subsets of the BioASQ corpus giving positive
results. During the BioASQ 2014 challenge we obtained the first place during
the first batch and the third in the two following batches. Our success in the
BioASQ challenge proved that a fully automated machine-learning approach, which
does not implement any heuristics and rule-based approaches, can be highly
competitive and outperform other approaches in similar challenging contexts
Learning to predict distributions of words across domains
Although the distributional hypothesis has been applied successfully in many natural language processing tasks, systems using distributional information have been limited to a single domain because the distribution of a word can vary between domains as the word’s predominant meaning changes. However, if it were possible to predict how the distribution of a word changes from one domain to another, the predictions could be used to adapt a system trained in one domain to work in another. We propose an unsupervised method to predict the distribution of a word in one domain, given its distribution in another domain. We evaluate our method on two tasks: cross-domain part-of-speech tagging and cross-domain sentiment classification. In both tasks, our method significantly outperforms competitive baselines and returns results that are statistically comparable to current state-of-the-art methods, while requiring no task-specific customisations
Bibliometric Perspectives on Medical Innovation using the Medical Subject Headings (MeSH) of PubMed
Multiple perspectives on the nonlinear processes of medical innovations can
be distinguished and combined using the Medical Subject Headings (MeSH) of the
Medline database. Focusing on three main branches-"diseases," "drugs and
chemicals," and "techniques and equipment"-we use base maps and overlay
techniques to investigate the translations and interactions and thus to gain a
bibliometric perspective on the dynamics of medical innovations. To this end,
we first analyze the Medline database, the MeSH index tree, and the various
options for a static mapping from different perspectives and at different
levels of aggregation. Following a specific innovation (RNA interference) over
time, the notion of a trajectory which leaves a signature in the database is
elaborated. Can the detailed index terms describing the dynamics of research be
used to predict the diffusion dynamics of research results? Possibilities are
specified for further integration between the Medline database, on the one
hand, and the Science Citation Index and Scopus (containing citation
information), on the other.Comment: forthcoming in the Journal of the American Society for Information
Science and Technolog
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Adverse Drug Reaction Classification With Deep Neural Networks
We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs
A comparison of machine learning techniques for detection of drug target articles
Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure.This research paper is supported by Projects TIN2007-67407-
C03-01, S-0505/TIC-0267 and MICINN project TEXT-ENTERPRISE
2.0 TIN2009-13391-C04-03 (Plan I + D + i), as well as for the Juan
de la Cierva program of the MICINN of SpainPublicad
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