Dataset Information

A Large-Scale Comparison of Main Concept Production Between Persons With Aphasia and Persons Without Brain Injury.

ABSTRACT: Purpose The purposes of this study are to provide clinicians and researchers with introductory psychometric data for the main concept analysis (MCA), a measure of discourse informativeness, and specifically, to provide descriptive and comparative statistical information about the performance of a large sample of persons not brain injured (PNBIs) and persons with aphasia (PWAs) on AphasiaBank discourse tasks. Method Transcripts of 5 semi-spontaneous discourse tasks were retrieved from the AphasiaBank database and scored according to detailed checklists and scoring procedures. Transcripts from 145 PNBIs and 238 PWAs were scored; descriptive statistics, median tests, and effect sizes are reported. Results PWAs demonstrated overall lower informativeness scores and more frequent production of statements that were inaccurate and/or incomplete. Differences between PNBIs and PWAs were observed for all main concept measures and stories. Comparisons of PNBIs and aphasia subtypes revealed significant differences for all groups, although the pattern of differences and strength of effect sizes varied by group and discourse task. Conclusions These results may improve the investigative and clinical utility of the MCA by providing descriptive and comparative information for PNBIs and PWAs for standardized discourse tasks that can be reliably scored. The results indicate that the MCA is sensitive to differences in discourse as a result of aphasia. Supplemental Material https://doi.org/10.23641/asha.7485647.

SUBMITTER: Dalton SGH

PROVIDER: S-EPMC6437704 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Large-Scale Comparison of Main Concept Production Between Persons With Aphasia and Persons Without Brain Injury.

Dalton Sarah Grace Hudspeth SGH Richardson Jessica D JD

American journal of speech-language pathology 20190301 1S

Purpose The purposes of this study are to provide clinicians and researchers with introductory psychometric data for the main concept analysis (MCA), a measure of discourse informativeness, and specifically, to provide descriptive and comparative statistical information about the performance of a large sample of persons not brain injured (PNBIs) and persons with aphasia (PWAs) on AphasiaBank discourse tasks. Method Transcripts of 5 semi-spontaneous discourse tasks were retrieved from the Aphasia ...[more]

PMID: 31072179

Similar Datasets

Project description:A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein interaction prediction and literature-based discovery. Concept embeddings-which involve the learning of vector representations of concepts using machine learning models-have been employed to capture the semantics of concepts. To develop concept embeddings, named-entity recognition (NER) tools are first used to identify and normalize concepts from the literature, and then different machine learning models are used to train the embeddings. Despite multiple attempts, existing biomedical concept embeddings generally suffer from suboptimal NER tools, small-scale evaluation, and limited availability. In response, we employed high-performance machine learning-based NER tools for concept recognition and trained our concept embeddings, BioConceptVec, via four different machine learning models on ~30 million PubMed abstracts. BioConceptVec covers over 400,000 biomedical concepts mentioned in the literature and is of the largest among the publicly available biomedical concept embeddings to date. To evaluate the validity and utility of BioConceptVec, we respectively performed two intrinsic evaluations (identifying related concepts based on drug-gene and gene-gene interactions) and two extrinsic evaluations (protein-protein interaction prediction and drug-drug interaction extraction), collectively using over 25 million instances from nine independent datasets (17 million instances from six intrinsic evaluation tasks and 8 million instances from three extrinsic evaluation tasks), which is, by far, the most comprehensive to our best knowledge. The intrinsic evaluation results demonstrate that BioConceptVec consistently has, by a large margin, better performance than existing concept embeddings in identifying similar and related concepts. More importantly, the extrinsic evaluation results demonstrate that using BioConceptVec with advanced deep learning models can significantly improve performance in downstream bioinformatics studies and biomedical text-mining applications. Our BioConceptVec embeddings and benchmarking datasets are publicly available at https://github.com/ncbi-nlp/BioConceptVec.

Dataset Information

A Large-Scale Comparison of Main Concept Production Between Persons With Aphasia and Persons Without Brain Injury.

Publications

A Large-Scale Comparison of Main Concept Production Between Persons With Aphasia and Persons Without Brain Injury.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure