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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Predicting population health with machine learning: a scoping review.

Jason Denzil Morgenstern1, Emmalin Buajitti2,3, Meghan O'Neill2

  • 1Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.

BMJ Open
|October 28, 2020
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Summary

Machine learning in population health prediction often uses traditional data and under-reports model development. Improved reporting and big data use are recommended for better machine learning applications.

Keywords:
epidemiologypublic healthstatistics & research methods

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Area of Science:

  • Population Health
  • Machine Learning Applications
  • Predictive Modeling

Background:

  • Machine learning (ML) offers potential for prediction in population health.
  • However, understanding its application, data sources, and adherence to reporting guidelines is crucial.

Purpose of the Study:

  • To review the application of machine learning in population health prediction.
  • To identify studied outcomes, data sources, and reporting practices for ML predictive models.

Main Methods:

  • A comprehensive scoping review of literature from 1980-2018.
  • Searched multiple databases (MEDLINE, EMBASE, etc.) for English articles using ML for population health outcomes, excluding logistic regression and purely clinical studies.

Main Results:

  • 231 studies were included, with the USA and China leading in research.
  • Cardiovascular disease was the most studied outcome; health records and investigator-generated data were common sources.
  • Many studies showed poor adherence to reporting guidelines, with common assessment of predictive discrimination but rare assessment of calibration.

Conclusions:

  • ML applications in population health are concentrated in well-resourced regions and diseases, with limited use of big data.
  • Under-reporting of model development aspects is prevalent.
  • Enhanced use of big data and adherence to reporting guidelines are essential for advancing ML in population health.