↓ Skip to main content
What is this page? Embed badge

Biomedical Literature Mining

Overview of attention for book
Cover of 'Biomedical Literature Mining'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction to Biomedical Literature Text Mining: Context and Objectives.
  3. Altmetric Badge
    Chapter 2 Accessing Biomedical Literature in the Current Information Landscape
  4. Altmetric Badge
    Chapter 3 Mapping of Biomedical Text to Concepts of Lexicons, Terminologies, and Ontologies
  5. Altmetric Badge
    Chapter 4 Text Mining for Drug–Drug Interaction
  6. Altmetric Badge
    Chapter 5 Biological Information Extraction and Co-occurrence Analysis.
  7. Altmetric Badge
    Chapter 6 Roles for Text Mining in Protein Function Prediction
  8. Altmetric Badge
    Chapter 7 Functional Molecular Units for Guiding Biomarker Panel Design
  9. Altmetric Badge
    Chapter 8 Mining biological networks from full-text articles.
  10. Altmetric Badge
    Chapter 9 Scientific Collaboration Networks Using Biomedical Text
  11. Altmetric Badge
    Chapter 10 Predicting Future Discoveries from Current Scientific Literature.
  12. Altmetric Badge
    Chapter 11 Biomedical Literature Mining
  13. Altmetric Badge
    Chapter 12 Integrative Literature and Data Mining to Rank Disease Candidate Genes
  14. Altmetric Badge
    Chapter 13 Role of Text Mining in Early Identification of Potential Drug Safety Issues.
  15. Altmetric Badge
    Chapter 14 Systematic Drug Repurposing Through Text Mining
  16. Altmetric Badge
    Chapter 15 Mining the Electronic Health Record for Disease Knowledge.
Attention for Chapter 15: Mining the Electronic Health Record for Disease Knowledge.
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

twitter
3 X users

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
59 Mendeley
Summary X Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Mining the Electronic Health Record for Disease Knowledge.
Chapter number 15
Book title
Biomedical Literature Mining
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-4939-0709-0_15
Pubmed ID
24788272
Book ISBNs
978-1-4939-0708-3, 978-1-4939-0709-0
Authors

Elizabeth S Chen, Indra Neil Sarkar, Elizabeth S. Chen, Chen, Elizabeth S., Sarkar, Indra Neil

Abstract

The growing amount and availability of electronic health record (EHR) data present enhanced opportunities for discovering new knowledge about diseases. In the past decade, there has been an increasing number of data and text mining studies focused on the identification of disease associations (e.g., disease-disease, disease-drug, and disease-gene) in structured and unstructured EHR data. This chapter presents a knowledge discovery framework for mining the EHR for disease knowledge and describes each step for data selection, preprocessing, transformation, data mining, and interpretation/validation. Topics including natural language processing, standards, and data privacy and security are also discussed in the context of this framework.

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 2%
Brazil 1 2%
Unknown 57 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 20%
Researcher 11 19%
Student > Master 7 12%
Professor 4 7%
Student > Bachelor 2 3%
Other 11 19%
Unknown 12 20%
Readers by discipline Count As %
Medicine and Dentistry 15 25%
Computer Science 15 25%
Engineering 4 7%
Mathematics 3 5%
Agricultural and Biological Sciences 2 3%
Other 6 10%
Unknown 14 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 January 2018.
All research outputs
#13,408,116
of 22,754,104 outputs
Outputs from Methods in molecular biology
#3,603
of 13,089 outputs
Outputs of similar age
#163,234
of 305,246 outputs
Outputs of similar age from Methods in molecular biology
#149
of 597 outputs
Altmetric has tracked 22,754,104 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,089 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 305,246 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 597 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.

This page is provided by Altmetric.