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Validating the representation of distance between infarct diseases using word embedding
BACKGROUND: The pivot and cluster strategy (PCS) is a diagnostic reasoning strategy that automatically elicits disease clusters similar to a differential diagnosis in a batch. Although physicians know empirically which disease clusters are similar, there has been no quantitative evaluation. This stu...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730570/ https://www.ncbi.nlm.nih.gov/pubmed/36476486 http://dx.doi.org/10.1186/s12911-022-02061-8 |
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author | Yokokawa, Daiki Noda, Kazutaka Yanagita, Yasutaka Uehara, Takanori Ohira, Yoshiyuki Shikino, Kiyoshi Tsukamoto, Tomoko Ikusaka, Masatomi |
author_facet | Yokokawa, Daiki Noda, Kazutaka Yanagita, Yasutaka Uehara, Takanori Ohira, Yoshiyuki Shikino, Kiyoshi Tsukamoto, Tomoko Ikusaka, Masatomi |
author_sort | Yokokawa, Daiki |
collection | PubMed |
description | BACKGROUND: The pivot and cluster strategy (PCS) is a diagnostic reasoning strategy that automatically elicits disease clusters similar to a differential diagnosis in a batch. Although physicians know empirically which disease clusters are similar, there has been no quantitative evaluation. This study aimed to determine whether inter-disease distances between word embedding vectors using the PCS are a valid quantitative representation of similar disease groups in a limited domain. METHODS: Abstracts were extracted from the Ichushi Web database and subjected to morphological analysis and training using Word2Vec, FastText, and GloVe. Consequently, word embedding vectors were obtained. For words including “infarction,” we calculated the cophenetic correlation coefficient (CCC) as an internal validity measure and the adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) with ICD-10 codes as the external validity measures. This was performed for each combination of metric and hierarchical clustering method. RESULTS: Seventy-one words included “infarction,” of which 38 diseases matched the ICD-10 standard with the appearance of 21 unique ICD-10 codes. When using Word2Vec, the CCC was most significant at 0.8690 (metric and method: euclidean and centroid), whereas the AMI was maximal at 0.4109 (metric and method: cosine and correlation, and average and weighted). The NMI and ARI were maximal at 0.8463 and 0.3593, respectively (metric and method: cosine and complete). FastText and GloVe generally resulted in the same trend as Word2Vec, and the metric and method that maximized CCC differed from the ones that maximized the external validity measures. CONCLUSIONS: The metric and method that maximized the internal validity measure differed from those that maximized the external validity measures; both produced different results. The cosine distance should be used when considering ICD-10, and the Euclidean distance when considering the frequency of word occurrence. The distributed representation, when trained by Word2Vec on the “infarction” domain from a Japanese academic corpus, provides an objective inter-disease distance used in PCS. |
format | Online Article Text |
id | pubmed-9730570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97305702022-12-09 Validating the representation of distance between infarct diseases using word embedding Yokokawa, Daiki Noda, Kazutaka Yanagita, Yasutaka Uehara, Takanori Ohira, Yoshiyuki Shikino, Kiyoshi Tsukamoto, Tomoko Ikusaka, Masatomi BMC Med Inform Decis Mak Research BACKGROUND: The pivot and cluster strategy (PCS) is a diagnostic reasoning strategy that automatically elicits disease clusters similar to a differential diagnosis in a batch. Although physicians know empirically which disease clusters are similar, there has been no quantitative evaluation. This study aimed to determine whether inter-disease distances between word embedding vectors using the PCS are a valid quantitative representation of similar disease groups in a limited domain. METHODS: Abstracts were extracted from the Ichushi Web database and subjected to morphological analysis and training using Word2Vec, FastText, and GloVe. Consequently, word embedding vectors were obtained. For words including “infarction,” we calculated the cophenetic correlation coefficient (CCC) as an internal validity measure and the adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) with ICD-10 codes as the external validity measures. This was performed for each combination of metric and hierarchical clustering method. RESULTS: Seventy-one words included “infarction,” of which 38 diseases matched the ICD-10 standard with the appearance of 21 unique ICD-10 codes. When using Word2Vec, the CCC was most significant at 0.8690 (metric and method: euclidean and centroid), whereas the AMI was maximal at 0.4109 (metric and method: cosine and correlation, and average and weighted). The NMI and ARI were maximal at 0.8463 and 0.3593, respectively (metric and method: cosine and complete). FastText and GloVe generally resulted in the same trend as Word2Vec, and the metric and method that maximized CCC differed from the ones that maximized the external validity measures. CONCLUSIONS: The metric and method that maximized the internal validity measure differed from those that maximized the external validity measures; both produced different results. The cosine distance should be used when considering ICD-10, and the Euclidean distance when considering the frequency of word occurrence. The distributed representation, when trained by Word2Vec on the “infarction” domain from a Japanese academic corpus, provides an objective inter-disease distance used in PCS. BioMed Central 2022-12-07 /pmc/articles/PMC9730570/ /pubmed/36476486 http://dx.doi.org/10.1186/s12911-022-02061-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Yokokawa, Daiki Noda, Kazutaka Yanagita, Yasutaka Uehara, Takanori Ohira, Yoshiyuki Shikino, Kiyoshi Tsukamoto, Tomoko Ikusaka, Masatomi Validating the representation of distance between infarct diseases using word embedding |
title | Validating the representation of distance between infarct diseases using word embedding |
title_full | Validating the representation of distance between infarct diseases using word embedding |
title_fullStr | Validating the representation of distance between infarct diseases using word embedding |
title_full_unstemmed | Validating the representation of distance between infarct diseases using word embedding |
title_short | Validating the representation of distance between infarct diseases using word embedding |
title_sort | validating the representation of distance between infarct diseases using word embedding |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730570/ https://www.ncbi.nlm.nih.gov/pubmed/36476486 http://dx.doi.org/10.1186/s12911-022-02061-8 |
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