Health AI Central
Share:
Peer-Reviewed Publication
Sci Rep2024;14(1):30760.December 28, 2024Journal Article

A machine learning tool for early identification of celiac disease autoimmunity.

Michael Dreyfuss1, Benjamin Getz2, Benjamin Lebwohl3, Or Ramni2, Daniel Underberger2, Tahel Ilan Ber2,4,5, Shlomit Steinberg-Koch2, Yonatan Jenudi2, Sivan Gazit6, Tal Patalon6, Gabriel Chodick6, Yehuda Shoenfeld7, Amir Ben-Tov6,4
1Predicta Med Analytics Ltd., Ramat Gan, Israel. michael@predicta-med.com.
2Predicta Med Analytics Ltd., Ramat Gan, Israel.
3Celiac Disease Center, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
4Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
5PhaseV, Tel Aviv, Israel.
6Kahn Sagol Maccabi Research & Innovation Center, Maccabi Healthcare Services, Tel Aviv, Israel.
7Zabludowicz Center for Autoimmune Diseases, Chaim Sheba Medical Center, Tel Hashomer, Israel.
PubMed PMC DOI PDF

Abstract

Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagnostic delays or remain undiagnosed. Using anonymized outpatient data from the electronic medical records of Maccabi Healthcare Services, we developed and evaluated five machine learning models to classify patients as at-risk for CD autoimmunity prior…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.