Predictive machine-learning model for screening iron deficiency without anaemia: a retrospective cohort study
The ML model provides an effective approach to screening for IDWA using readily available EHR data. Implementing this tool in clinical settings may facilitate early diagnosis of IDWA.
Authors
- Renana Robinson (ORCID: https://orcid.org/0000-0003-0239-9265)
- Eyal Klang (ORCID: https://orcid.org/0000-0002-4567-3108)
- Orly Efros (ORCID: https://orcid.org/0000-0002-6024-9110)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Shelly Soffer (ORCID: https://orcid.org/0000-0002-7853-2029)
- Aya Mudrik (ORCID: https://orcid.org/0009-0002-3429-9242)
Institutions
- Ben-Gurion University of the Negev (IL)
- Tel Aviv University (IL)
- Sheba Medical Center (IL)
- Rabin Medical Center (IL)
- Academic College of Tel Aviv-Yafo (IL)
- Weizmann Institute of Science (IL)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- BMJ Open
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1136/bmjopen-2024-097016
- Citations
- 2
- Primary Topic
- Iron Metabolism and Disorders
- Type
- article
- Field-Weighted Citation Impact
- 3.54