Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing

被引：0

作者：

Yang, Audrey ^{[1
]}

Kamien, Sam ^{[1
]}

Davoudi, Anahita ^{[2
]}

Hwang, Sy ^{[1
]}

Gandhi, Meet ^{[1
]}

Urbanowicz, Ryan ^{[3
]}

Mowery, Danielle ^{[1
]}

机构：

[1] Univ Penn, Philadelphia, PA 19104 USA

[2] VNS Hlth, New York, NY USA

[3] Cedars Sinai Med Ctr, Los Angeles, CA USA

来源：

MEDINFO 2023 - THE FUTURE IS ACCESSIBLE | 2024年 / 310卷

关键词：

Natural language processing; machine learning; electronic health records;

D O I：

10.3233/SHTI231039

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

According to the World Stroke Organization, 12.2 million people worldwide will have their first stroke this year almost half of which will die as a result. Natural Language Processing (NLP) may improve stroke phenotyping; however, existing rule-based classifiers are rigid, resulting in inadequate performance. We report findings from a pilot study using NLP to improve relation detection for stroke assertion detection to support research studies and healthcare operations.

引用

页码：619 / 623

页数：5

共 5 条

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