De Novo exposomic geospatial assembly of chronic disease regions with machine learning & network analysis.

Journal: EBioMedicine
PMID: 39891994

Abstract

BACKGROUND: Determining spatial relationships between diseases and the exposome is limited by available methodologies. aPEER (algorithm for Projection of Exposome and Epidemiological Relationships) uses machine learning (ML) and network analysis to find spatial relationships between diseases and the exposome in the United States.

Authors

  • Andrew Deonarine
    XY Health, Cambridge, MA, United States.
  • Ayushi Batwara
    Boston Public Health Commission, 1010 Massachusetts Avenue, 6th Floor, Boston, MA 02118, USA; University of California, Berkeley, 110 Sproul Hall #5800, Berkeley, CA 94720-5800, USA.
  • Roy Wada
    Boston Public Health Commission, 1010 Massachusetts Avenue, 6th Floor, Boston, MA 02118, USA.
  • Puneet Sharma
    Digital Technologies and Innovation, Siemens Healthineers, Princeton, NJ, United States.
  • Joseph Loscalzo
    Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. Electronic address: jloscalzo@rics.bwh.harvard.edu.
  • Bisola Ojikutu
    Boston Public Health Commission, 1010 Massachusetts Avenue, 6th Floor, Boston, MA 02118, USA; Harvard Medical School, New Research Building, 77 Avenue Louis Pasteur, Room 630M, Boston, MA 02115, USA; Brigham and Women's Hospital, Department of Medicine, 75 Francis Street, Boston, MA 02115, USA.
  • Kathryn Hall
    Boston Public Health Commission, 1010 Massachusetts Avenue, 6th Floor, Boston, MA 02118, USA; Harvard Medical School, New Research Building, 77 Avenue Louis Pasteur, Room 630M, Boston, MA 02115, USA; Brigham and Women's Hospital, Department of Medicine, 75 Francis Street, Boston, MA 02115, USA; New York Academy of Medicine, 1216 5th Ave, New York, NY 10029, USA.

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