↓ Skip to main content

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

Overview of attention for article published in BMC Medical Research Methodology, February 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

blogs
1 blog
twitter
36 tweeters
facebook
3 Facebook pages
reddit
1 Redditor

Citations

dimensions_citation
71 Dimensions

Readers on

mendeley
358 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
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network
Published in
BMC Medical Research Methodology, February 2018
DOI 10.1186/s12874-018-0482-1
Pubmed ID
Authors

Jared L. Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, Yuval Kluger

Abstract

Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

Twitter Demographics

The data shown below were collected from the profiles of 36 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 358 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 358 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 93 26%
Researcher 65 18%
Student > Master 48 13%
Student > Bachelor 22 6%
Student > Doctoral Student 19 5%
Other 47 13%
Unknown 64 18%
Readers by discipline Count As %
Computer Science 122 34%
Medicine and Dentistry 32 9%
Engineering 28 8%
Biochemistry, Genetics and Molecular Biology 21 6%
Mathematics 20 6%
Other 49 14%
Unknown 86 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 19 May 2019.
All research outputs
#696,418
of 15,062,424 outputs
Outputs from BMC Medical Research Methodology
#91
of 1,403 outputs
Outputs of similar age
#18,526
of 267,388 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 7 outputs
Altmetric has tracked 15,062,424 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,403 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one has done particularly well, scoring higher than 93% 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 267,388 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them