Artificial intelligence-driven structurization of diagnostic information in free-text pathology reports

Artificial intelligence-driven structurization of diagnostic information in free-text pathology reports

Background: Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic en...

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Bibliographic Details
Main Authors: Pericles S Giannaris, Zainab Al-Taie, Mikhail Kovalenko, Nattapon Thanintorn, Olha Kholod, Yulia Innokenteva, Emily Coberly, Shellaine Frazier, Katsiarina Laziuk, Mihail Popescu, Chi-Ren Shyu, Dong Xu, Richard D Hammer, Dmitriy Shin
Format: Article
Language:English
Published: Wolters Kluwer Medknow Publications 2020-01-01
Series:Journal of Pathology Informatics
Subjects:
free-text pathology reports
information extraction
n-ary modeling
structurization
Online Access:http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=4;epage=4;aulast=Giannaris

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http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=4;epage=4;aulast=Giannaris

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