@inproceedings{b2ff5459a0874eca9e98b6293a763354,
title = "Multi-Dimensional Laboratory Test Score as a Proxy for Health",
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.",
keywords = "Electronic Health Records, Laboratory Tests, Machine Learning, UK Biobank",
author = "Ezra, {Bar H.} and Shreyas Havaldar and Benjamin Glicksberg and Nadav Rappoport",
note = "Publisher Copyright: {\textcopyright} 2022 European Federation for Medical Informatics (EFMI) and IOS Press.; 32nd Medical Informatics Europe Conference, MIE 2022 ; Conference date: 27-05-2022 Through 30-05-2022",
year = "2022",
month = may,
day = "25",
doi = "10.3233/SHTI220441",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "219--223",
editor = "Brigitte Seroussi and Patrick Weber and Ferdinand Dhombres and Cyril Grouin and Jan-David Liebe and Jan-David Liebe and Jan-David Liebe and Sylvia Pelayo and Andrea Pinna and Bastien Rance and Bastien Rance and Lucia Sacchi and Adrien Ugon and Adrien Ugon and Arriel Benis and Parisis Gallos",
booktitle = "Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022",
address = "Netherlands",
}