Adaptable graph neural networks design to support generalizability for clinical event prediction.

Journal: Journal of biomedical informatics
PMID: 39956347

Abstract

OBJECTIVE: While many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges when tested outside of their institution of training. These challenges are rooted not only in differences in patient population characteristics, but medical practice patterns of different institutions.

Authors

  • Amara Tariq
    Department of Biomedical Informatics, Emory School of Medicine, Atlanta, Georgia. Electronic address: amara.tariq2@emory.edu.
  • Gurkiran Kaur
    Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA.
  • Leon Su
    Mayo Clinic Arizona.
  • Judy Gichoya
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • Bhavik Patel
    Department of Radiology, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.
  • Imon Banerjee
    Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.

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