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Surveys, Reviews, and Foundations

This collection contains literature that gives general information about artificial intelligence and healthcare.

Big Data and Machine Learning in Health Care (April 3, 2018) 📖

(Andrew L. Beam, Isaac S. Kohane)

Link: https://doi.org/10.1001/jama.2017.18391

Abstract:

  • Nearly all aspects of modern life are in some way being changed by big data and machine learning.
  • Netflix knows what movies people like to watch and Google knows what people want to know based on their search histories.
  • Indeed, Google has recently begun to replace much of its existing non–machine learning technology with machine learning algorithms, and there is great optimism that these techniques can provide similar improvements across many sectors.
  • It is no surprise then that medicine is awash with claims of revolution from the application of machine learning to big health care data. Recent examples have demonstrated that big data and machine learning can create algorithms that perform on par with human physicians.
  • Though machine learning and big data may seem mysterious at first, they are in fact deeply related to traditional statistical models that are recognizable to most clinicians.
  • The authors hope that elucidating these connections will demystify these techniques and provide a set of reasonable expectations for the role of machine learning and big data in health care.

A Review of Challenges and Opportunities in Machine Learning for Health (2020-5-30) 📖

(Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Irene Y. Chen, Rajesh Ranganath)

Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233077/

Abstract:

  • Modern electronic health records (EHRs) provide data to answer clinically meaningful questions.
  • The growing data in EHRs makes healthcare ripe for the use of machine learning.
  • However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies.
    • For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented.
  • This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.

Bibliography

PubMed Central Full Text PDF. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233077/pdf/3268580.pdf. Accessed 23 Mar. 2021.
PubMed Central Link. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233077/. Accessed 23 Mar. 2021.
Ghassemi, Marzyeh, et al. “A Review of Challenges and Opportunities in Machine Learning for Health.” AMIA Summits on Translational Science Proceedings, vol. 2020, May 2020, pp. 191–200, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233077/.
Beam, Andrew L., and Isaac S. Kohane. “Big Data and Machine Learning in Health Care.” JAMA, vol. 319, no. 13, Apr. 2018, pp. 1317–18, doi:10.1001/jama.2017.18391.