A machine learning tool for identifying metastatic colorectal cancer in primary care
A machine learning method based on primary health care consultation frequency and diagnoses may be used to identify important variables for predicting presence of MCRC. Both primary health care consultations and associated diagnostic codes need to be taken into consideration.
Authors
- Marcela Ewing (ORCID: https://orcid.org/0000-0002-6849-0654)
- Andreas Rosenblad (ORCID: https://orcid.org/0000-0003-3691-8326)
- Annika Sjövall (ORCID: https://orcid.org/0000-0003-2221-5881)
- Axel Carl Carlsson (ORCID: https://orcid.org/0000-0001-6113-0472)
- Eliya Abedi (ORCID: https://orcid.org/0000-0002-6177-4136)
- Elinor Nemlander (ORCID: https://orcid.org/0000-0001-9589-4681)
- Jan Hasselström (ORCID: https://orcid.org/0000-0001-9521-2345)
Institutions
- Uppsala University (SE)
- Karolinska University Hospital (SE)
- Karolinska Institutet (SE)
- Region Västra Götaland (SE)
- Primary HealthCare (MT)
- Primary Health Care (QA)
- Stockholm Health Care Services (SE)
- University of Gothenburg (SE)
Publication Details
- Journal
- Scandinavian Journal of Primary Health Care
- Published
- 2025-03-13
- DOI
- https://doi.org/10.1080/02813432.2025.2477155
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36