↓ Skip to main content

Predictive modeling of structured electronic health records for adverse drug event detection

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2015
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
40 Dimensions

Readers on

mendeley
366 Mendeley
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.
Title
Predictive modeling of structured electronic health records for adverse drug event detection
Published in
BMC Medical Informatics and Decision Making, November 2015
DOI 10.1186/1472-6947-15-s4-s1
Pubmed ID
Authors

Jing Zhao, Aron Henriksson, Lars Asker, Henrik Boström

Abstract

The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and combined. We have demonstrated how machine learning can be applied to electronic health records for the purpose of detecting adverse drug events and proposed solutions to some of the challenges this presents, including how to represent the various data types. Overall, clinical codes are more useful than measurements and, in specific cases, it is beneficial to combine the two.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 366 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Unknown 364 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 23 6%
Student > Ph. D. Student 19 5%
Researcher 17 5%
Student > Bachelor 10 3%
Other 10 3%
Other 25 7%
Unknown 262 72%
Readers by discipline Count As %
Medicine and Dentistry 33 9%
Computer Science 25 7%
Pharmacology, Toxicology and Pharmaceutical Science 6 2%
Engineering 5 1%
Agricultural and Biological Sciences 4 1%
Other 23 6%
Unknown 270 74%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 02 December 2015.
All research outputs
#8,745,063
of 11,360,604 outputs
Outputs from BMC Medical Informatics and Decision Making
#840
of 1,049 outputs
Outputs of similar age
#191,549
of 309,749 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#34
of 40 outputs
Altmetric has tracked 11,360,604 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,049 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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 309,749 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.