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Performance analysis of text classification algorithms for PubMed articles
The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary developed by the US National Library of Medicine (NLM) for indexing articles in Pubmed Central (PMC) archive. The annotation process is a complex and time-consuming task relying on subjective manual assignment of MeSH concepts....
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2022
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| _version_ | 1867613941942714368 |
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| access_status_str | Open Access |
| author | Savvi, Suzana |
| author2 | Bonenkamp, Koen |
| author_browse | Bonenkamp, Koen Savvi, Suzana |
| author_facet | Bonenkamp, Koen Savvi, Suzana |
| author_sort | Savvi, Suzana |
| collection | Thesis |
| description | The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary developed by the US National Library of Medicine (NLM) for indexing articles in Pubmed Central (PMC) archive. The annotation process is a complex and time-consuming task relying on subjective manual assignment of MeSH concepts. Automating such tasks with machine learning may provide a more efficient way of organizing biomedical literature in a less ambiguous way. This research provides a case study which compares the performance of several different machine learning algorithms (Topic Modelling, Random Forest, Logistic Regression, Support Vector Classifiers, Multinomial Naive Bayes, Convolutional Neural Network and Long Short-Term Memory (LSTM)) in reproducing manually assigned MeSH annotations. Records for this study were retrieved from Pubmed using the E-utilities API to the Entrez system of databases at NCBI (National Centre for Biotechnology Information). The MeSH vocabulary is organised in a hierarchical structure and article abstracts labelled with a single MeSH term from the top second two layers were selected for training the machine learning models. Various strategies for text multiclass classification were considered. One was a Chi-square test for feature selection which identified words relevant to each MeSH label. The second approach used Named Entity Recognition (NER) to extract entities from the unstructured text and another approach relied on word embeddings able to capture latent knowledge from literature. At the start of the study text was tokenised using the Term Frequency Inverse Document Frequency (Tf-idf) technique and topic modelling performed with the objective to ascertain the correlation between assigned topics (unsupervised learning task) and MeSH terms in PubMed. Findings revealed the degree of coupling was low although significant. Of all of the classifier models trained, logistic regression on Tf-idf vectorised entities achieved highest accuracy. Performance varied across the different MeSH categories. In conclusion automated curation of articles by abstract may be possible for those target classes classified reliably and reproducibly. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/36059 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:44:09.393Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/36059 Performance analysis of text classification algorithms for PubMed articles Savvi, Suzana Bonenkamp, Koen Little, Francesca Statistical Sciences The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary developed by the US National Library of Medicine (NLM) for indexing articles in Pubmed Central (PMC) archive. The annotation process is a complex and time-consuming task relying on subjective manual assignment of MeSH concepts. Automating such tasks with machine learning may provide a more efficient way of organizing biomedical literature in a less ambiguous way. This research provides a case study which compares the performance of several different machine learning algorithms (Topic Modelling, Random Forest, Logistic Regression, Support Vector Classifiers, Multinomial Naive Bayes, Convolutional Neural Network and Long Short-Term Memory (LSTM)) in reproducing manually assigned MeSH annotations. Records for this study were retrieved from Pubmed using the E-utilities API to the Entrez system of databases at NCBI (National Centre for Biotechnology Information). The MeSH vocabulary is organised in a hierarchical structure and article abstracts labelled with a single MeSH term from the top second two layers were selected for training the machine learning models. Various strategies for text multiclass classification were considered. One was a Chi-square test for feature selection which identified words relevant to each MeSH label. The second approach used Named Entity Recognition (NER) to extract entities from the unstructured text and another approach relied on word embeddings able to capture latent knowledge from literature. At the start of the study text was tokenised using the Term Frequency Inverse Document Frequency (Tf-idf) technique and topic modelling performed with the objective to ascertain the correlation between assigned topics (unsupervised learning task) and MeSH terms in PubMed. Findings revealed the degree of coupling was low although significant. Of all of the classifier models trained, logistic regression on Tf-idf vectorised entities achieved highest accuracy. Performance varied across the different MeSH categories. In conclusion automated curation of articles by abstract may be possible for those target classes classified reliably and reproducibly. 2022-03-14T05:21:47Z 2022-03-14T05:21:47Z 2021 2022-03-14T05:18:11Z Master Thesis Masters MSc http://hdl.handle.net/11427/36059 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Savvi, Suzana Performance analysis of text classification algorithms for PubMed articles |
| thesis_degree_str | Master's |
| title | Performance analysis of text classification algorithms for PubMed articles |
| title_full | Performance analysis of text classification algorithms for PubMed articles |
| title_fullStr | Performance analysis of text classification algorithms for PubMed articles |
| title_full_unstemmed | Performance analysis of text classification algorithms for PubMed articles |
| title_short | Performance analysis of text classification algorithms for PubMed articles |
| title_sort | performance analysis of text classification algorithms for pubmed articles |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/36059 |
| work_keys_str_mv | AT savvisuzana performanceanalysisoftextclassificationalgorithmsforpubmedarticles |