FasTag: Automatic text classification of unstructured medical narratives Article Swipe
Guhan Venkataraman
,
Arturo López Pineda
,
Oliver J. Bear Don’t Walk
,
Ashley Zehnder
,
Sandeep Ayyar
,
Rodney L. Page
,
Carlos D. Bustamante
,
Manuel A. Rivas
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0234647
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0234647
Unstructured clinical narratives are continuously being recorded as part of delivery of care in electronic health records, and dedicated tagging staff spend considerable effort manually assigning clinical codes for billing purposes. Despite these efforts, however, label availability and accuracy are both suboptimal. In this retrospective study, we aimed to automate the assignment of top-level International Classification of Diseases version 9 (ICD-9) codes to clinical records from human and veterinary data stores using minimal manual labor and feature curation. Automating top-level annotations could in turn enable rapid cohort identification, especially in a veterinary setting. To this end, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We investigated the accuracy of both separate-domain and combined-domain models and probed model portability. We established relevant baseline classification performances by training Decision Trees (DT) and Random Forests (RF). We also investigated whether transforming the data using MetaMap Lite, a clinical natural language processing tool, affected classification performance. We showed that the LSTM-RNNs accurately classify veterinary and human text narratives into top-level categories with an average weighted macro F1 score of 0.74 and 0.68 respectively. In the "neoplasia" category, the model trained on veterinary data had a high validation accuracy in veterinary data and moderate accuracy in human data, with F1 scores of 0.91 and 0.70 respectively. Our LSTM method scored slightly higher than that of the DT and RF models. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort identification for comparative oncology studies. Digitization of human and veterinary health information will continue to be a reality, particularly in the form of unstructured narratives. Our approach is a step forward for these two domains to learn from and inform one another.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0234647
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0234647&type=printable
- OA Status
- gold
- Cited By
- 41
- References
- 81
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3036927603
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- OpenAlex ID
-
https://openalex.org/W3036927603Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0234647Digital Object Identifier
- Title
-
FasTag: Automatic text classification of unstructured medical narrativesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-22Full publication date if available
- Authors
-
Guhan Venkataraman, Arturo López Pineda, Oliver J. Bear Don’t Walk, Ashley Zehnder, Sandeep Ayyar, Rodney L. Page, Carlos D. Bustamante, Manuel A. RivasList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0234647Publisher landing page
- PDF URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0234647&type=printableDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0234647&type=printableDirect OA link when available
- Concepts
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F1 score, Recurrent neural network, Artificial intelligence, Computer science, Random forest, Medical record, Software portability, Machine learning, Natural language processing, Medicine, Artificial neural network, Programming language, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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41Total citation count in OpenAlex
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2025: 7, 2024: 6, 2023: 5, 2022: 8, 2021: 12Per-year citation counts (last 5 years)
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81Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.dedicated | 18 |
| abstract_inverted_index.purposes. | 30 |
| abstract_inverted_index.recurrent | 102 |
| abstract_inverted_index.structure | 242 |
| abstract_inverted_index.top-level | 53, 79, 174 |
| abstract_inverted_index.Automating | 78 |
| abstract_inverted_index.accurately | 166 |
| abstract_inverted_index.assignment | 51 |
| abstract_inverted_index.categories | 175 |
| abstract_inverted_index.electronic | 14 |
| abstract_inverted_index.especially | 88 |
| abstract_inverted_index.narratives | 2, 172 |
| abstract_inverted_index.processing | 156 |
| abstract_inverted_index.represents | 239 |
| abstract_inverted_index.short-term | 99 |
| abstract_inverted_index.validation | 201 |
| abstract_inverted_index.veterinary | 68, 91, 111, 168, 196, 204, 258 |
| abstract_inverted_index."neoplasia" | 190 |
| abstract_inverted_index.annotations | 80 |
| abstract_inverted_index.comparative | 251 |
| abstract_inverted_index.established | 128 |
| abstract_inverted_index.information | 260 |
| abstract_inverted_index.narratives. | 273 |
| abstract_inverted_index.suboptimal. | 41 |
| abstract_inverted_index.Digitization | 254 |
| abstract_inverted_index.Unstructured | 0 |
| abstract_inverted_index.availability | 36 |
| abstract_inverted_index.considerable | 22 |
| abstract_inverted_index.continuously | 4 |
| abstract_inverted_index.investigated | 114, 144 |
| abstract_inverted_index.particularly | 267 |
| abstract_inverted_index.performance. | 160 |
| abstract_inverted_index.performances | 132 |
| abstract_inverted_index.portability. | 126 |
| abstract_inverted_index.transforming | 146 |
| abstract_inverted_index.unstructured | 272 |
| abstract_inverted_index.International | 54 |
| abstract_inverted_index.respectively. | 187, 219 |
| abstract_inverted_index.retrospective | 44 |
| abstract_inverted_index.Classification | 55 |
| abstract_inverted_index.classification | 131, 159 |
| abstract_inverted_index.identification | 249 |
| abstract_inverted_index.combined-domain | 121 |
| abstract_inverted_index.identification, | 87 |
| abstract_inverted_index.separate-domain | 119 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5073710029, https://openalex.org/A5002430972, https://openalex.org/A5053857674, https://openalex.org/A5008925649, https://openalex.org/A5089454973, https://openalex.org/A5025246130, https://openalex.org/A5012523508, https://openalex.org/A5090097828 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I4210121800, https://openalex.org/I78577930, https://openalex.org/I92446798, https://openalex.org/I97018004 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.89547509 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |