1,083 research outputs found
Ranking Medical Subject Headings using a factor graph model.
Automatically assigning MeSH (Medical Subject Headings) to articles is an active research topic. Recent work demonstrated the feasibility of improving the existing automated Medical Text Indexer (MTI) system, developed at the National Library of Medicine (NLM). Encouraged by this work, we propose a novel data-driven approach that uses semantic distances in the MeSH ontology for automated MeSH assignment. Specifically, we developed a graphical model to propagate belief through a citation network to provide robust MeSH main heading (MH) recommendation. Our preliminary results indicate that this approach can reach high Mean Average Precision (MAP) in some scenarios
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
Large scale biomedical texts classification: a kNN and an ESA-based approaches
With the large and increasing volume of textual data, automated methods for
identifying significant topics to classify textual documents have received a
growing interest. While many efforts have been made in this direction, it still
remains a real challenge. Moreover, the issue is even more complex as full
texts are not always freely available. Then, using only partial information to
annotate these documents is promising but remains a very ambitious issue.
MethodsWe propose two classification methods: a k-nearest neighbours
(kNN)-based approach and an explicit semantic analysis (ESA)-based approach.
Although the kNN-based approach is widely used in text classification, it needs
to be improved to perform well in this specific classification problem which
deals with partial information. Compared to existing kNN-based methods, our
method uses classical Machine Learning (ML) algorithms for ranking the labels.
Additional features are also investigated in order to improve the classifiers'
performance. In addition, the combination of several learning algorithms with
various techniques for fixing the number of relevant topics is performed. On
the other hand, ESA seems promising for this classification task as it yielded
interesting results in related issues, such as semantic relatedness computation
between texts and text classification. Unlike existing works, which use ESA for
enriching the bag-of-words approach with additional knowledge-based features,
our ESA-based method builds a standalone classifier. Furthermore, we
investigate if the results of this method could be useful as a complementary
feature of our kNN-based approach.ResultsExperimental evaluations performed on
large standard annotated datasets, provided by the BioASQ organizers, show that
the kNN-based method with the Random Forest learning algorithm achieves good
performances compared with the current state-of-the-art methods, reaching a
competitive f-measure of 0.55% while the ESA-based approach surprisingly
yielded reserved results.ConclusionsWe have proposed simple classification
methods suitable to annotate textual documents using only partial information.
They are therefore adequate for large multi-label classification and
particularly in the biomedical domain. Thus, our work contributes to the
extraction of relevant information from unstructured documents in order to
facilitate their automated processing. Consequently, it could be used for
various purposes, including document indexing, information retrieval, etc.Comment: Journal of Biomedical Semantics, BioMed Central, 201
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
Named entity aware transfer learning for biomedical factoid question answering
Biomedical factoid question answering is an important task in biomedical question answering application. It has attracted much attention because of its reliability of the answer. In question answering system, better representation of word is of much importance and a proper word embedding usually can improve the performance of system significantly. With the success of pre-trained models in general natural language process tasks, pretrained model has been widely used in biomedical area as well and a lot of pretrained model based approaches have been proven effective in biomedical question answering task. Besides the proper word embedding, name entity is also important information for biomedical question answering. Inspired by the concept of transfer learning, in this research we developed a mechanism to finetune BioBERT with name entity dataset to improve the question answering performance. Furthermore, we also apply BiLSTM to encode the question text to obtain sentence level information. To better combine the question level and token level information, we use bagging to further improve the overall performance. The proposed framework has been evaluated on BioASQ 6b and 7b datasets and the results have shown its promising potential
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