From 98b42e7174aa32368e62699c42787e9492dbeab1 Mon Sep 17 00:00:00 2001
From: rolandroller <43855067+rolandroller@users.noreply.github.com>
Date: Fri, 1 Nov 2024 16:31:10 +0100
Subject: [PATCH] Update index.md

update article sage
---
 content/publication/sage2024/index.md | 68 +++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)

diff --git a/content/publication/sage2024/index.md b/content/publication/sage2024/index.md
index 8b13789..647f070 100644
--- a/content/publication/sage2024/index.md
+++ b/content/publication/sage2024/index.md
@@ -1 +1,69 @@
+---
+# 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:
+# - name: Follow
+#   url: https://twitter.com
+#   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
+# To use, add an image named `featured.jpg/png` to your page's folder. 
+# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
+image:
+  caption: ""
+  focal_point: ""
+  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+projects: []
+
+# Slides (optional).
+#   Associate this publication with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+slides: ""
+---