Reducing Workload in Short Answer Grading Using Machine Learning.

Autor: Weegar, Rebecka, Idestam-Almquist, Peter

Publikováno v: International Journal of Artificial Intelligence in Education (Springer Science & Business Media B.V.); Jun2024, Vol. 34 Issue 2, p247-273, 27p

Semi-supervised medical entity recognition: A study on Spanish and Swedish clinical corpora

Autor: Pérez, Alicia, Weegar, Rebecka, Casillas, Arantza, Gojenola, Koldo, Oronoz, Maite, Dalianis, Hercules

Publikováno v: In Journal of Biomedical Informatics July 2017 71:16-30

Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches.

Autor: Weegar, Rebecka¹ (AUTHOR) rebeckaw@dsv.su.se, Pérez, Alicia² (AUTHOR), Casillas, Arantza² (AUTHOR), Oronoz, Maite² (AUTHOR)

Publikováno v: BMC Medical Informatics & Decision Making. 12/23/2019 Supplement 7, Vol. 19, p1-14. 14p. 1 Diagram, 11 Charts, 4 Graphs.

Agrupaciones para la extracción de entidades clínicas

Autor: Weegar, Rebecka, Pérez Ramírez, Alicia, Dalianis, Hercules, Gojenola Galletebeitia, Koldo, Casillas Rubio, Arantza, Oronoz Anchordoqui, Maite

Publikováno v: RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)

Health records are a valuable source of clinical knowledge and Natural Language Processing techniques have previously been applied to the text in health records for a number of applications. Often, a first step in clinical text processing is clinical

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::889121213bb76aec4149e101c758c94d
http://hdl.handle.net/10045/74611

Ensembles for clinical entity extraction.

Autor: Weegar, Rebecka, Pérez, Alicia, Dalianis, Hercules, Gojenola, Koldo, Casillas, Arantza, Oronoz, Maite

Publikováno v: Procesamiento del Lenguaje Natural; Mar2018, Vol. 60, p13-20, 8p