2 research outputs found
The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain
Eliciting semantic similarity between concepts in the biomedical domain
remains a challenging task. Recent approaches founded on embedding vectors have
gained in popularity as they risen to efficiently capture semantic
relationships The underlying idea is that two words that have close meaning
gather similar contexts. In this study, we propose a new neural network model
named MeSH-gram which relies on a straighforward approach that extends the
skip-gram neural network model by considering MeSH (Medical Subject Headings)
descriptors instead words. Trained on publicly available corpus PubMed MEDLINE,
MeSH-gram is evaluated on reference standards manually annotated for semantic
similarity. MeSH-gram is first compared to skip-gram with vectors of size 300
and at several windows contexts. A deeper comparison is performed with tewenty
existing models. All the obtained results of Spearman's rank correlations
between human scores and computed similarities show that MeSH-gram outperforms
the skip-gram model, and is comparable to the best methods but that need more
computation and external resources.Comment: 6 pages, 2 table
Biomedical Literature Mining and Knowledge Discovery of Phenotyping Definitions
Indiana University-Purdue University Indianapolis (IUPUI)Phenotyping definitions are essential in cohort identification when conducting
clinical research, but they become an obstacle when they are not readily available.
Developing new definitions manually requires expert involvement that is labor-intensive,
time-consuming, and unscalable. Moreover, automated approaches rely mostly on
electronic health records’ data that suffer from bias, confounding, and incompleteness.
Limited efforts established in utilizing text-mining and data-driven approaches to automate
extraction and literature-based knowledge discovery of phenotyping definitions and to
support their scalability. In this dissertation, we proposed a text-mining pipeline combining
rule-based and machine-learning methods to automate retrieval, classification, and
extraction of phenotyping definitions’ information from literature. To achieve this, we first
developed an annotation guideline with ten dimensions to annotate sentences with evidence
of phenotyping definitions' modalities, such as phenotypes and laboratories. Two
annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text
observational studies’ methods sections (n=86). Percent and Kappa statistics showed high
inter-annotator agreement on sentence-level annotations. Second, we constructed two
validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level.
We applied the abstract-level classifier on a large-scale biomedical literature of over
20 million abstracts published between 1975 and 2018 to classify positive abstracts
(n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from
their methods sections and used the full-text sentence-level classifier to extract positive
sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the
positively classified sentences. Lexica-based methods were used to recognize medical
concepts in these sentences (n=19,423). Co-occurrence and association methods were used
to identify and rank phenotype candidates that are associated with a phenotype of interest.
We derived 12,616,465 associations from our large-scale corpus. Our literature-based
associations and large-scale corpus contribute in building new data-driven phenotyping
definitions and expanding existing definitions with minimal expert involvement