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Biomedical Research

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Artificial intelligence in medicine: humans need not apply?

The New Zealand medical journal
Artificial intelligence (AI) is a rapidly growing field with a wide range of applications. Driven by economic constraints and the potential to reduce human error, we believe that over the coming years AI will perform a significant amount of the diagn...

miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

Journal of biomedical semantics
BACKGROUND: MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-diseas...

Applications of Deep Learning in Biomedicine.

Molecular pharmaceutics
Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may b...

Supporting systematic reviews using LDA-based document representations.

Systematic reviews
BACKGROUND: Identifying relevant studies for inclusion in a systematic review (i.e. screening) is a complex, laborious and expensive task. Recently, a number of studies has shown that the use of machine learning and text mining methods to automatical...

An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining.

IEEE/ACM transactions on computational biology and bioinformatics
In biomedical text mining tasks, distributed word representation has succeeded in capturing semantic regularities, but most of them are shallow-window based models, which are not sufficient for expressing the meaning of words. To represent words usin...

Machine learning in burn care and research: A systematic review of the literature.

Burns : journal of the International Society for Burn Injuries
BACKGROUND: To date, there are no reviews on machine learning (ML) in burn care. Considering the growth of ML in medicine and the complexities and challenges of burn care, this review specializes on ML applications in burn care. The objective was to ...

Extracting biomedical events from pairs of text entities.

BMC bioinformatics
BACKGROUND: Huge amounts of electronic biomedical documents, such as molecular biology reports or genomic papers are generated daily. Nowadays, these documents are mainly available in the form of unstructured free texts, which require heavy processin...

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus.

BMC medical informatics and decision making
BACKGROUND: Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mini...

Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network.

Journal of biomedical informatics
Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may ...