Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach

JMIR Medical Informatics
Jinying ChenHong Yu

Abstract

Medical terms are a major obstacle for patients to comprehend their electronic health record (EHR) notes. Clinical natural language processing (NLP) systems that link EHR terms to lay terms or definitions allow patients to easily access helpful information when reading through their EHR notes, and have shown to improve patient EHR comprehension. However, high-quality lay language resources for EHR terms are very limited in the public domain. Because expanding and curating such a resource is a costly process, it is beneficial and even necessary to identify terms important for patient EHR comprehension first. We aimed to develop an NLP system, called adapted distant supervision (ADS), to rank candidate terms mined from EHR corpora. We will give EHR terms ranked as high by ADS a higher priority for lay language annotation-that is, creating lay definitions for these terms. Adapted distant supervision uses distant supervision from consumer health vocabulary and transfer learning to adapt itself to solve the problem of ranking EHR terms in the target domain. We investigated 2 state-of-the-art transfer learning algorithms (ie, feature space augmentation and supervised distant supervision) and designed 5 types of learning features, inc...Continue Reading

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Citations

Jan 9, 2019·BMC Medical Informatics and Decision Making·Yanshan WangHongfang Liu
Jan 27, 2021·Healthcare·Juan Uribe-TorilBruno José Nievas-Soriano
May 13, 2019··Robert-Jan SipsLaurens van den Bercken
Jul 26, 2019··Yu-An ChungWei-Hung Weng

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Software Mentioned

MetaMap
word2vec
Python
SciPy
Scikit
FSA
MedlinePlus
Java Automatic Term Extraction ( JATE ) toolkit
JATE
learn

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