lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses
lab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management.
Authors
- Pavlos Mamouris (ORCID: https://orcid.org/0000-0001-7902-5355)
- Nicolas Delvaux (ORCID: https://orcid.org/0000-0002-7335-4075)
- Ahmed Medhat Zayed (ORCID: https://orcid.org/0000-0001-7797-1655)
- Arne Janssens (ORCID: https://orcid.org/0000-0002-6294-8951)
Institutions
- Department of Public Health (US)
- Menoufia University (EG)
- KU Leuven (BE)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-09-03
- DOI
- https://doi.org/10.1186/s12911-024-02652-7
- Citations
- 3
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.39