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Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources

Overview of attention for article published in Journal of Biomedical Informatics, December 2016
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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6 tweeters

Citations

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16 Dimensions

Readers on

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108 Mendeley
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Title
Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources
Published in
Journal of Biomedical Informatics, December 2016
DOI 10.1016/j.jbi.2016.10.008
Pubmed ID
Authors

Simon Kocbek, Lawrence Cavedon, David Martinez, Christopher Bain, Chris Mac Manus, Gholamreza Haffari, Ingrid Zukerman, Karin Verspoor

Abstract

Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast Cancer, Colon Cancer, Secondary Malignant Neoplasm of Respiratory and Digestive Organs, Multiple Myeloma and Malignant Plasma Cell Neoplasms, Pneumonia, and Pulmonary Embolism. We specifically examine the effect of linking multiple data sources on text classification performance. Support Vector Machine classifiers are built for eight data source combinations, and evaluated using the metrics of Precision, Recall and F-Score. Sub-sampling techniques are used to address unbalanced datasets of medical records. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient and hospital admission data, in order to assess the research question regarding the impact of the value of multiple data sources. Statistical significance is measured using the Wilcoxon signed-rank test. A second set of experiments explores aspects of the system in greater depth, focusing on Lung Cancer. We explore the impact of feature selection; analyse the learning curve; examine the effect of restricting admissions to only those containing reports from all data sources; and examine the impact of reducing the sub-sampling. These experiments provide better understanding of how to best apply text classification in the context of imbalanced data of variable completeness. Radiology questions plus patient and hospital admission data contribute valuable information for detecting most of the diseases, significantly improving performance when added to radiology reports alone or to the combination of radiology and pathology reports. Overall, linking data sources significantly improved classification performance for all the diseases examined. However, there is no single approach that suits all scenarios; the choice of the most effective combination of data sources depends on the specific disease to be classified.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Australia 1 <1%
Unknown 106 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 20%
Researcher 17 16%
Student > Master 12 11%
Student > Postgraduate 9 8%
Student > Doctoral Student 9 8%
Other 25 23%
Unknown 14 13%
Readers by discipline Count As %
Computer Science 36 33%
Medicine and Dentistry 21 19%
Engineering 11 10%
Nursing and Health Professions 5 5%
Psychology 3 3%
Other 11 10%
Unknown 21 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 12 December 2016.
All research outputs
#3,269,495
of 12,214,198 outputs
Outputs from Journal of Biomedical Informatics
#285
of 1,163 outputs
Outputs of similar age
#86,481
of 263,299 outputs
Outputs of similar age from Journal of Biomedical Informatics
#18
of 66 outputs
Altmetric has tracked 12,214,198 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,163 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 73% 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 263,299 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 66 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 66% of its contemporaries.