Multi-Dimensional Laboratory Test Score as a Proxy for Health.

Journal: Studies in health technology and informatics
PMID: 35612060

Abstract

The standard of care for a physician to review laboratory tests results is to weigh each individual laboratory test result and compare it to against a standard reference range. Such a method of scanning can lead to missing high-level information. Different methods have tried to overcome a part of the problem by creating new types of reference values. This research proposes looking at test scores in a higher dimension space. And using machine learning approach, determine whether a subject has abnormal tests result that, according to current practice, would be defined as valid - and thus indicating a possible disease or illness. To determine health status, we look both at a disease-specific level and disease-independent level, while looking at several different outcomes.

Authors

  • Bar H Ezra
    Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Israel.
  • Shreyas Havaldar
    Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Benjamin Glicksberg
    Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Nadav Rappoport
    Department of Software and Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.

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