Analyzing patterns of literature-based phenotyping definitions for text mining applications

Samar Binkheder, Heng Yi Wu, Sara Quinney, Lang Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Phenotyping definitions are widely used in observational studies that utilize population data from Electronic Health Records (EHRs). Biomedical text mining supports biomedical knowledge discovery. Therefore, we believe that mining phenotyping definitions from the literature can support EHR-based clinical research. However, information about these definitions presented in the literature is inconsistent, diverse, and unknown, especially for text mining usage. Therefore, we aim to analyze patterns of phenotyping definitions as a first step toward developing a text mining application to improve phenotype definition. A set random of observational studies was used for this analysis. Term frequency-inverse document frequency (TF-IDF) and Term Frequency (TF) were used to rank the terms in the 3958 sentences. Finally, we present preliminary results analyzing phenotyping definitions patterns.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages374-376
Number of pages3
ISBN (Electronic)9781538653777
DOIs
StatePublished - Jul 24 2018
Event6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States
Duration: Jun 4 2018Jun 7 2018

Other

Other6th IEEE International Conference on Healthcare Informatics, ICHI 2018
CountryUnited States
CityNew York
Period6/4/186/7/18

Fingerprint

Data Mining
Electronic Health Records
Health
Observational Studies
Data mining
Phenotype
Research
Population

Keywords

  • Biomedical literature
  • Electronic Health Records
  • Phenotyping
  • Text Mining

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Health Informatics

Cite this

Binkheder, S., Wu, H. Y., Quinney, S., & Li, L. (2018). Analyzing patterns of literature-based phenotyping definitions for text mining applications. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018 (pp. 374-376). [8419394] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2018.00061

Analyzing patterns of literature-based phenotyping definitions for text mining applications. / Binkheder, Samar; Wu, Heng Yi; Quinney, Sara; Li, Lang.

Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 374-376 8419394.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Binkheder, S, Wu, HY, Quinney, S & Li, L 2018, Analyzing patterns of literature-based phenotyping definitions for text mining applications. in Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018., 8419394, Institute of Electrical and Electronics Engineers Inc., pp. 374-376, 6th IEEE International Conference on Healthcare Informatics, ICHI 2018, New York, United States, 6/4/18. https://doi.org/10.1109/ICHI.2018.00061
Binkheder S, Wu HY, Quinney S, Li L. Analyzing patterns of literature-based phenotyping definitions for text mining applications. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 374-376. 8419394 https://doi.org/10.1109/ICHI.2018.00061
Binkheder, Samar ; Wu, Heng Yi ; Quinney, Sara ; Li, Lang. / Analyzing patterns of literature-based phenotyping definitions for text mining applications. Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 374-376
@inproceedings{5d83007a2cb4439cbb72e638c2d73e53,
title = "Analyzing patterns of literature-based phenotyping definitions for text mining applications",
abstract = "Phenotyping definitions are widely used in observational studies that utilize population data from Electronic Health Records (EHRs). Biomedical text mining supports biomedical knowledge discovery. Therefore, we believe that mining phenotyping definitions from the literature can support EHR-based clinical research. However, information about these definitions presented in the literature is inconsistent, diverse, and unknown, especially for text mining usage. Therefore, we aim to analyze patterns of phenotyping definitions as a first step toward developing a text mining application to improve phenotype definition. A set random of observational studies was used for this analysis. Term frequency-inverse document frequency (TF-IDF) and Term Frequency (TF) were used to rank the terms in the 3958 sentences. Finally, we present preliminary results analyzing phenotyping definitions patterns.",
keywords = "Biomedical literature, Electronic Health Records, Phenotyping, Text Mining",
author = "Samar Binkheder and Wu, {Heng Yi} and Sara Quinney and Lang Li",
year = "2018",
month = "7",
day = "24",
doi = "10.1109/ICHI.2018.00061",
language = "English (US)",
pages = "374--376",
booktitle = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Analyzing patterns of literature-based phenotyping definitions for text mining applications

AU - Binkheder, Samar

AU - Wu, Heng Yi

AU - Quinney, Sara

AU - Li, Lang

PY - 2018/7/24

Y1 - 2018/7/24

N2 - Phenotyping definitions are widely used in observational studies that utilize population data from Electronic Health Records (EHRs). Biomedical text mining supports biomedical knowledge discovery. Therefore, we believe that mining phenotyping definitions from the literature can support EHR-based clinical research. However, information about these definitions presented in the literature is inconsistent, diverse, and unknown, especially for text mining usage. Therefore, we aim to analyze patterns of phenotyping definitions as a first step toward developing a text mining application to improve phenotype definition. A set random of observational studies was used for this analysis. Term frequency-inverse document frequency (TF-IDF) and Term Frequency (TF) were used to rank the terms in the 3958 sentences. Finally, we present preliminary results analyzing phenotyping definitions patterns.

AB - Phenotyping definitions are widely used in observational studies that utilize population data from Electronic Health Records (EHRs). Biomedical text mining supports biomedical knowledge discovery. Therefore, we believe that mining phenotyping definitions from the literature can support EHR-based clinical research. However, information about these definitions presented in the literature is inconsistent, diverse, and unknown, especially for text mining usage. Therefore, we aim to analyze patterns of phenotyping definitions as a first step toward developing a text mining application to improve phenotype definition. A set random of observational studies was used for this analysis. Term frequency-inverse document frequency (TF-IDF) and Term Frequency (TF) were used to rank the terms in the 3958 sentences. Finally, we present preliminary results analyzing phenotyping definitions patterns.

KW - Biomedical literature

KW - Electronic Health Records

KW - Phenotyping

KW - Text Mining

UR - http://www.scopus.com/inward/record.url?scp=85051134217&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051134217&partnerID=8YFLogxK

U2 - 10.1109/ICHI.2018.00061

DO - 10.1109/ICHI.2018.00061

M3 - Conference contribution

AN - SCOPUS:85051134217

SP - 374

EP - 376

BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -