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Related Experiment Video

Updated: May 29, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Predicting biomedical document access as a function of past use.

J Caleb Goodwin1, Todd R Johnson, Trevor Cohen

  • 1School of Biomedical Informatics, The University of Texas Health Science Center, Houston, Texas 77030, USA.

Journal of the American Medical Informatics Association : JAMIA
|September 16, 2011
PubMed
Summary
This summary is machine-generated.

Past access frequency accurately predicts future biomedical document access. A simple frequency-based model is highly effective and computationally efficient for large databases like MEDLINE.

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Area of Science:

  • Bibliometrics
  • Information Science
  • Computational Biology

Background:

  • Predicting user behavior in digital libraries is crucial for resource management.
  • Understanding document access patterns can optimize information retrieval systems.

Purpose of the Study:

  • To evaluate the predictive power of past document access on future access.
  • To assess the efficacy of different models for predicting document popularity.

Main Methods:

  • Analysis of 394 days of PubMed user query logs from the Texas Medical Center.
  • Evaluation of two models: one based on access frequency, another on frequency and recency.
  • Correlation analysis (R²) to compare model predictions with empirical data.

Main Results:

  • The frequency-only model showed a high correlation with empirical data (R²=0.932).
  • The model incorporating both frequency and recency had a lower correlation (R²=0.668).
  • Past access frequency is a strong predictor of future document access.

Conclusions:

  • A frequency-based model accurately predicts future document access.
  • This model is computationally efficient and requires minimal storage, making it scalable for large corpora like MEDLINE.
  • It is feasible to model future document access probability based on historical access data.