Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models Article Swipe
Yang Ren
,
Yuqi Wu
,
Jungwei Fan
,
Aditya Khurana
,
Sunyang Fu
,
Dezhi Wu
,
Hongfang Liu
,
Ming Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocae144
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocae144
Objectives The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs—a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care. Materials and Methods We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models. Results The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average. Discussion This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection. Conclusions The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jamia/ocae144
- https://academic.oup.com/jamia/article-pdf/31/8/1714/58591192/ocae144.pdf
- OA Status
- hybrid
- Cited By
- 4
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400090543
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400090543Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jamia/ocae144Digital Object Identifier
- Title
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Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language modelsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-27Full publication date if available
- Authors
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Yang Ren, Yuqi Wu, Jungwei Fan, Aditya Khurana, Sunyang Fu, Dezhi Wu, Hongfang Liu, Ming HuangList of authors in order
- Landing page
-
https://doi.org/10.1093/jamia/ocae144Publisher landing page
- PDF URL
-
https://academic.oup.com/jamia/article-pdf/31/8/1714/58591192/ocae144.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
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-
hybridOpen access status per OpenAlex
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https://academic.oup.com/jamia/article-pdf/31/8/1714/58591192/ocae144.pdfDirect OA link when available
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Triage, Computer science, Leverage (statistics), Machine learning, Workflow, Artificial intelligence, Identification (biology), Class (philosophy), Language model, Health care, Natural language processing, Medicine, Medical emergency, Biology, Database, Botany, Economic growth, EconomicsTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.patient-centered | 77 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5010869663 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I4210146710, https://openalex.org/I919571938 |
| citation_normalized_percentile.value | 0.92101449 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |