A machine learning algorithm for the detection of paroxysmal nocturnal haemoglobinuria (PNH) in UK primary care electronic health records
This is the first study to combine clinical understanding of PNH with machine learning, demonstrating the ability to discriminate between PNH and control patients in retrospective electronic health records. With further investigation and validation, this algorithm could be deployed on live health data, potentially leading to earlier diagnosis for patients who currently experience long diagnostic delays or remain undiagnosed.
Authors
- F. Boardman-Pretty (ORCID: https://orcid.org/0000-0001-8459-4739)
- Richard Kelly (ORCID: https://orcid.org/0000-0002-0622-5644)
- E Marchini
- Amanda Worker (ORCID: https://orcid.org/0000-0003-3103-867X)
- Hadley Mahon
- Jack Sams
- Rand Dubis
- Daniel Ollerenshaw
- Calum Grant
- Elizabeth Varones
- Jez Stockdale
- Jyothika Kumar
- P.J. Fish
- A. Warren
Institutions
- St James's University Hospital (GB)
- Mendeley (United Kingdom) (GB)
Publication Details
- Journal
- Orphanet Journal of Rare Diseases
- Published
- 2024-10-13
- DOI
- https://doi.org/10.1186/s13023-024-03406-4
- Primary Topic
- Complement system in diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- AstraZeneca
- Alexion Pharmaceuticals