Update index.md

update article sage

content/publication/sage2024/index.md

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+---
+# Documentation: https://wowchemy.com/docs/managing-content/
 
+title: "Unsupervised SapBERT-based bi-encoders for medical concept annotation of clinical narratives with SNOMED CT"
+authors: [Akhila Abdulnazar, Roland Roller, Stefan Schulz, Markus Kreuzthaler]
+date: 2024-11-01T00:00:00+00:00
+doi: ""
+
+# Schedule page publish date (NOT publication's date).
+publishDate: 2024-1-01T00:00:00+00:00
+
+# Publication type.
+# Legend: 0 = Uncategorized; 1 = Conference paper; 2 = Journal article;
+# 3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;
+# 7 = Thesis; 8 = Patent
+publication_types: ["2"]
+
+# Publication name and optional abbreviated publication name.
+publication: "SAGE Digital Health"
+publication_short: ""
+
+abstract: "Clinical narratives provide comprehensive patient information. Achieving interoperability involves mapping relevant details to standardized medical vocabularies. Typically, natural language processing divides this task into named entity recognition (NER) and medical concept normalization (MCN). State-of-the-art results require supervised setups with abundant training data. However, the limited availability of annotated data due to sensitivity and time constraints poses challenges. This study addressed the need for unsupervised medical concept annotation (MCA) to overcome these limitations and support the creation of annotated datasets."
+
+# Summary. An optional shortened abstract.
+summary: ""
+
+tags: []
+categories: []
+featured: false
+
+# Custom links (optional).
+#   Uncomment and edit lines below to show custom links.
+# links:
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+#   icon_pack: fab
+#   icon: twitter
+
+url_pdf: "https://journals.sagepub.com/doi/full/10.1177/20552076241288681"
+url_code: 
+url_dataset:
+url_poster:
+url_project:
+url_slides:
+url_source:
+url_video:
+
+# Featured image
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+  focal_point: ""
+  preview_only: false
+
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+---