Information Extraction from Clinical Documents: Towards Disease/Disorder Template Filling

Chikka, Veera Raghavendra; Mariyasagayam, Nestor; Niwa, Yasumasa; Karlapalem, Kamalakar

doi:10.1007/978-3-319-24027-5_41

Cited by 8 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Subject module indicates whether the patient or someone else (e.g., “mom attempted suicide”) experiences the event. The values for the Subject module include “patient,” “family member,” “other,” and “null.” [ 43 ] The terms tagged as “patient” were considered as subject relevant for this analysis.…”

Section: Methodsmentioning

confidence: 99%

Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing

Zhong

Karlson

Gelaye

et al. 2018

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

BackgroundWe examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening suicidal behavior among pregnant women in electronic medical records (EMRs).MethodsWomen aged 10–64 years with at least one diagnostic code related to pregnancy or delivery (N = 275,843) from Partners HealthCare were included as our “datamart.” Diagnostic codes related to suicidal behavior were applied to the datamart to screen women for suicidal behavior. Among women without any diagnostic codes related to suicidal behavior (n = 273,410), 5880 women were randomly sampled, of whom 1120 had at least one mention of terms related to suicidal behavior in clinical notes. NLP was then used to process clinical notes for the 1120 women. Chart reviews were performed for subsamples of women.ResultsUsing diagnostic codes, 196 pregnant women were screened positive for suicidal behavior, among whom 149 (76%) had confirmed suicidal behavior by chart review. Using NLP among those without diagnostic codes, 486 pregnant women were screened positive for suicidal behavior, among whom 146 (30%) had confirmed suicidal behavior by chart review.ConclusionsThe use of NLP substantially improves the sensitivity of screening suicidal behavior in EMRs. However, the prevalence of confirmed suicidal behavior was lower among women who did not have diagnostic codes for suicidal behavior but screened positive by NLP. NLP should be used together with diagnostic codes for future EMR-based phenotyping studies for suicidal behavior.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0617-7) contains supplementary material, which is available to authorized users.

show abstract

Section: Methodsmentioning

confidence: 99%

Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing

Zhong

Karlson

Gelaye

et al. 2018

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…With the development of deep learning, particularly the emergence of pre-trained models, text mining tools significantly advanced in their capacity to grasp semantic information and predict biological entities [21] , [22] . Several deep learning-based text mining tools have been developed to extract and organize the inside textual information related to various biological entities [23] , [24] , [25] , such as proteins, drugs and diseases [26] , [27] , [28] . However, there is a lack of tools designed for comprehensive scanning of RNA biomedical relations.…”

Section: Introductionmentioning

confidence: 99%

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature

Zheng,

Xu,

Xie

et al. 2024

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

“…10 Most electronic health record data are in freetext form, a more natural and expressive method, differing from the semistructured style of research articles. 11 Thus, information extraction technologies involving electronic health records typically concentrate on extracting specific clinical concepts 12,13 (e.g., substance usage and disease status), or extracting the diagnosis and treatment information. This is to establish a structured diagnosis and treatment database with key-value pairs, 14 applying extraction methods based on natural language processing and machine learning.…”

Section: Introductionmentioning

confidence: 99%

A precision‐preferred comprehensive information extraction system for clinical articles in traditional Chinese Medicine

Xia

Cai

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

This study established a precision-preferred system specially designed for the data extraction of traditional Chinese medicine (TCM) articles, providing foundational data for subsequent clinical article analysis and synthesis of TCM clinical evidence.Information extraction is commonly used in many fields to identify relevant concepts and the relationship between pairs of concepts from the vast information sources. Previous studies that performed information extraction primarily focused on scattering targeted fields to achieve a balance between precision and recall. Therefore, this study aims to create a comprehensive information extraction system for TCM articles. This system will extract all relevant information from research articles on a broad research field, including the 11 diseases that

show abstract

Information Extraction from Clinical Documents: Towards Disease/Disorder Template Filling

Cited by 8 publications

References 16 publications

Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing

Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature

A precision‐preferred comprehensive information extraction system for clinical articles in traditional Chinese Medicine

Contact Info

Product

Resources

About