Comparison of Machine-Learning Algorithms for the Prediction of Current Procedural Terminology (CPT) Codes from Pathology Reports.

Levy, Joshua; Vattikonda, Nishitha; Haudenschild, Christian; Christensen, Brock; Vaickus, Louis

Levy, Joshua; Vattikonda, Nishitha; Haudenschild, Christian; Christensen, Brock; Vaickus, Louis.

Afiliación

Levy J; Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA.
Vattikonda N; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
Haudenschild C; Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
Christensen B; Thomas Jefferson High School for Science and Technology, Alexandria, Virginia, USA.
Vaickus L; University of Minnesota Medical School, Minneapolis, Minnesota, USA.

J Pathol Inform ; 13: 3, 2022.

Article en En | MEDLINE | ID: mdl-35127232

Palabras clave

BERT; XGBoost; current procedural terminology; deep learning; machine learning; pathology reports

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Pathol Inform Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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