Distinction between medical and non-medical usages of short forms in clinical narratives.

Sungrim Moon, Donna Ihrke, Yuqun Zeng, Hongfang Liu

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023
Review

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Sungrim MoonDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.

Donna IhrkeDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.

Yuqun ZengDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.

Hongfang LiuDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.

Funding

Secondary use of EMRs for surgical complication surveillanceR01EB019403 · NIBIB · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, STORLIE, CURTIS · 2015 to 2022

$3.8M

Natural language processing for clinical and translational researchR01GM102282 · NIGMS · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, PAKHOMOV, SERGUEI VS · 2013 to 2016

$2.5M

NIBIB NIH HHS R01 EB019403NIGMS NIH HHS R01 GM102282

6 · The paper itself

Abstract

The short forms of medical concepts or expressions (i.e., acronyms/abbreviations) are prevalent in clinical documentation. Given the limited number of potential short forms, they are also highly ambiguous. Resolving the ambiguity of short forms is essential in clinical natural language processing (NLP). However, one prerequisite for resolving ambiguity of short forms is to have a sense inventory. This paper outlines our process of identifying 141 potential short forms with randomly sampled phrases from a large clinical corpus. We assessed various features in their ability to disambiguate medical and non-medical usages. We identified 68% of our short forms as primarily serving medical usages, whereas 12% had non-medical usages. The remaining 19% showed alternating usage based upon case form. Our short forms had an average of 3.58 senses. Usages could be distinguished using basic trigram/bigram/line information. Our initial findings will be applicable for automatic usage/sense resolution.

Indexed as

Abbreviations as TopicMedical RecordsNarrative MedicineHumansNatural Language ProcessingUnified Medical Language System

Identifiers

PMID29854199

PMCPMC5977717

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.