@article{ghassemi2018opportunities,
  title={Opportunities in machine learning for healthcare},
  author={Ghassemi, Marzyeh and Naumann, Tristan and Schulam, Peter and Beam, Andrew L and Ranganath, Rajesh},
  journal={arXiv preprint arXiv:1806.00388},
  url_Paper={https://www.dropbox.com/s/gb43h8wf6rn1w4p/ghassemi_opportunities_arxiv_2018.pdf?dl=1},
  abstract={Modern electronic health records (EHRs) provide data to answer clinically mean- ingful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique chal- lenges 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.
},
  keywords={Healthcare, Reviews},
  year={2018}
}

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