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

Bar H. Ezra, Shreyas Havaldar, Benjamin S. Glicksberg, Nadav Rappoport

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageEnglish
Title of host publicationChallenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
EditorsBrigitte Seroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Lucia Sacchi, Adrien Ugon, Arriel Benis, Parisis Gallos
PublisherIOS Press BV
Number of pages5
ISBN (Electronic)9781643682846
StatePublished - 2022
Event32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France
Duration: 27 May 202230 May 2022

Publication series

NameStudies in Health Technology and Informatics


Conference32nd Medical Informatics Europe Conference, MIE 2022


  • Electronic Health Records
  • Laboratory Tests
  • Machine Learning
  • UK Biobank

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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