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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Machine learning in scientific grant review: algorithmically predicting project efficiency in high energy physics.

Vlasta Sikimić1, Sandro Radovanović2

  • 1Cluster of Excellence - Machine Learning for Science and the Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Tübingen, Germany.

European Journal for Philosophy of Science
|August 1, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning can predict the efficiency of high energy physics (HEP) projects using proposal data, offering a potential aid to grant reviews. While accurate, caution is advised due to limitations in citation data and unobserved factors.

Keywords:
Data envelopment analysisEfficiency of experimentsEpistemic utilityHigh energy physicsPeer-reviewPredictive analysis

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Area of Science:

  • High Energy Physics (HEP)
  • Scientific Grant Review

Background:

  • Traditional grant peer-review is costly and time-consuming.
  • There is a growing need for efficient and objective methods to assess research proposals.

Purpose of the Study:

  • To investigate the potential of machine learning algorithms in predicting the epistemic efficiency of high energy physics (HEP) projects.
  • To identify team structures that maximize the epistemic performance of research groups.

Main Methods:

  • Data Envelopment Analysis (DEA) was used to assess the efficiency of 67 HEP experiments at Fermilab.
  • Predictive algorithms including lasso and ridge linear regression, neural network, and gradient boosted trees were applied to efficiency scores.
  • Project structure data (duration, team number, team size) and outcomes (citations per paper) were utilized.

Main Results:

  • Machine learning models demonstrated moderately high accuracy in predicting project efficiency (mean absolute error of 0.123).
  • The study identified correlations between team structures and epistemic performance.

Conclusions:

  • Algorithmic prediction shows promise as a supplementary tool in the grant review process for high energy physics.
  • Caution is recommended due to limitations such as citation pattern unreliability, unobservable variables, and potential model predictability.