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Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Overview of attention for article published in Brain Informatics, March 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#2 of 119)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

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48 X users
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5 patents
wikipedia
1 Wikipedia page
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1 YouTube creator

Citations

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594 Dimensions

Readers on

mendeley
857 Mendeley
Title
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Published in
Brain Informatics, March 2016
DOI 10.1007/s40708-016-0042-6
Pubmed ID
Authors

Andreas Holzinger

Abstract

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

X Demographics

X Demographics

The data shown below were collected from the profiles of 48 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 857 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 <1%
Spain 2 <1%
Brazil 2 <1%
Australia 1 <1%
Austria 1 <1%
Germany 1 <1%
Israel 1 <1%
France 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 843 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 181 21%
Student > Master 146 17%
Researcher 90 11%
Student > Bachelor 68 8%
Student > Doctoral Student 48 6%
Other 124 14%
Unknown 200 23%
Readers by discipline Count As %
Computer Science 286 33%
Engineering 92 11%
Business, Management and Accounting 32 4%
Medicine and Dentistry 31 4%
Social Sciences 24 3%
Other 137 16%
Unknown 255 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 47. 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 28 February 2024.
All research outputs
#893,987
of 25,440,205 outputs
Outputs from Brain Informatics
#2
of 119 outputs
Outputs of similar age
#15,365
of 313,009 outputs
Outputs of similar age from Brain Informatics
#2
of 14 outputs
Altmetric has tracked 25,440,205 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 119 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 99% 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 313,009 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 95% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.