Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis

@article{Shweta2020AugmentedCO,
  title={Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis},
  author={Fnu Shweta and K. Murugadoss and Samir Awasthi and A. Venkatakrishnan and A. Puranik and Martin Kang and B. Pickering and J. C. O’Horo and P. Bauer and R. Razonable and P. Vergidis and Z. Temesgen and S. Rizza and Maryam Mahmood and W. R. Wilson and Douglas Challener and P. Anand and Matt Liebers and Zainab M. Doctor and Eli Silvert and Hugo Solomon and T. Wagner and G. Gores and A. Williams and J. Halamka and V. Soundararajan and A. Badley},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.09338}
}
  • Fnu Shweta, K. Murugadoss, +24 authors A. Badley
  • Published 2020
  • Medicine, Computer Science, Mathematics, Biology
  • ArXiv
  • Understanding the temporal dynamics of COVID-19 patient phenotypes is necessary to derive fine-grained resolution of the pathophysiology. Here we use state-of-the-art deep neural networks over an institution-wide machine intelligence platform for the augmented curation of 8.2 million clinical notes from 14,967 patients subjected to COVID-19 PCR diagnostic testing. By contrasting the Electronic Health Record (EHR)-derived clinical phenotypes of COVID-19-positive (COVIDpos, n=272) versus COVID-19… CONTINUE READING
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