Unsupervised entity and relation extraction from clinical records in Italian

By Anita Alicante, Anna Corazza, Francesco Isgrò, Stefano Silvestri. 2016

58 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tristan Naumann
2. Zexian Zeng
3. Xiaoyu Li
4. Yu Deng
5. Hercules Dalianis
6. Yuan Luo
7. Aurélie Névéol
8. Guergana K. Savova

In Molecular Biology

1. Zexian Zeng
2. Hercules Dalianis
3. Tristan Naumann
4. Xiaoyu Li
5. Yu Deng
6. Yuan Luo
7. Guergana K. Savova
8. Pierre Zweigenbaum

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[187] proposed using unsupervised methods for both entity and relation extraction from clinical notes.”
• Clinical Natural Language Processing in languages other than English: opportunities and challenges (2018, background): “- Relations BG [64] DE [84, 115] IT [46, 47]”