The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Leveraging Foundation Models for Clinical Text Analysis
Open accessAbstract
Infectious diseases are a significant public health concern globally, and extracting relevant information from scientific literature can facilitate the development of effective prevention and treatment strategies. However, the large amount of clinical data available presents a challenge for information extraction. To address this challenge, this study proposes a natural language processing (NLP) framework that uses a pre-trained transformer model fine-tuned on task-specific data to extract key information related to infectious diseases from free-text clinical data. The proposed framework includes a data layer for preparing datasets from clinical texts, a foundation model layer for entity extraction, and an assessment layer for performance analysis. The results of the evaluation indicate that the proposed method outperforms standard methods, and leveraging prior knowledge through the pre-trained transformer model makes it useful for investigating other infectious diseases in the future.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
This includes constructing large foundation models for genomics, clinical images, and clinical text learning.
“This includes constructing large foundation models for genomics [186], clinical images [187], and clinical text learning [188].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [188]
The Introduction and Foundation model layer support the clinical-text portion: the paper uses a foundation model to extract information from clinical texts. But the Foundation model layer says it fine-tunes pre-trained BioBERT, not that it constructs a large foundation model. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Flagged first, then by citations
Research works printed but not linked: 26
- 10.1007/s11431-020-1647-3 · DOI · Paper page
- 10.1007/978-3-030-56978-5_12 · DOI
- 10.1075/li.30.1.03nad · DOI · Paper page
- 10.5772/51066 · DOI
- 10.1186/s12859-019-3321-4 · DOI
- Wang, 2019, A clinical text classification paradigm using weak supervision and deep representation 08 Information and Computing Sciences 0801 Artificial Intelligence and Image Processing 17 Psychology and Cognitive Sciences 1702 Cognitive Sciences, BMC Med. Inform. Decis. Mak., 19, 1, 10.1186/s12911-018-0723-6 · DOI
- 10.1007/978-3-030-68763-2_48 · DOI
- 10.1093/bioinformatics/btz682 · DOI · Paper page
- 10.3389/fcell.2020.00673 · DOI
- Wu, Combine factual medical knowledge and distributed word representation to improve clinical named entity recognition, 2018, 1110
- 10.1186/s44247-023-00035-y · DOI
- Lituiev, 2022, Automatic Extraction of Social Determinants of Health from Medical Notes of Chronic Lower Back Pain Patients, medRxiv, 2022
- 10.1093/jamia/ocab170 · DOI · Paper page
- 10.3912/OJIN.Vol17No03Man04 · DOI
- 10.1007/978-94-024-0881-2_11 · DOI
- Sang, Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition, 142
- 10.5772/51066 · DOI
- 10.1093/nar/gkaa952 · DOI · Paper page
- 10.1016/j.jbi.2013.12.006 · DOI
- 10.1136/amiajnl-2011-000203 · DOI
- 10.1136/amiajnl-2013-001628 · DOI
- 10.1162/tacl_a_00104 · DOI
- 10.1162/qss_a_00080 · DOI
- 10.1371/journal.pone.0247461 · DOI
- 10.1038/s41467-022-30052-w · DOI
- 2012, Methods for De-identification of PHI, HHS.gov
Cited by
Sorted by citations
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75
Metadata
- Authors
- Raza, S., & Bashir, S. R.
- Affiliations
- University of Toronto (CA)
- Toronto Metropolitan University (CA)
- Venue
- 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)
- Publisher
- IEEE
- Published
- 2023-06-26
- Pages
- 575-580
- OpenAlex
- W4389577465
- Cited by
- 2
- References
- 2
- Citations by year
- 2025: 1 · 2024: 1
- FWCI
- 0.38
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Healthcare Informatics (ICHI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- infectious diseases, clinical text mining, information extraction, named entity recognition, pre-trained transformer models, fine-tuning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
