Data Mining in Oncology: The ukCAT Project and the Practicalities of Working with Routine Patient Data

Price, G.; van Herk, M.; Faivre-Finn, C.

doi:10.1016/j.clon.2017.07.011

Cited by 25Artificial Intelligence in HealthcareElectronic Health Records SystemsMachine Learning in Healthcare

Data Mining in Oncology: The ukCAT Project and the Practicalities of Working with Routine Patient Data

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Price, G., van Herk, M., Faivre-Finn, C.·2017·Clinical Oncology

DOI 10.1016/j.clon.2017.07.011

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Many existing studies have exploited distributed learning and federated learning across multiple institutions in fields such as medical image processing, clinical decision support, and clinical oncology, to facilitate privacy-preserving multi-centric rapid learning of health care.
“Many existing studies have exploited distributed learning and federated learning across multiple institutions in fields such as medical image processing [129,130], clinical decision support [131], and clinical oncology [132], to facilitate privacy-preserving multi-centric rapid learning of health care.”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [132]

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Authors: Price, G., van Herk, M., & Faivre-Finn, C.
Affiliations: Manchester Academic Health Science Centre (GB)
Corresponding author: Price, G.
ORCID: Price, G.
Venue: Clinical Oncology
Publisher: Elsevier BV
Published: 2017-12
Volume: 29
Issue: 12
Pages: 814-817
ISSN: 0936-6555 (ISSN-L)
DOI: 10.1016/j.clon.2017.07.011
OpenAlex: W2744605892
Cited by: 25
References: 19
Citations by year: 2026: 1 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 1 · 2020: 2 · 2018: 2 · 2017: 2
Type: journal article
Language: en
Open access: green
research.manchester.ac.uk
Licence: other-oa
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Clinical Oncology (publishedVersion)
PubMed (publishedVersion)
Pure Amsterdam UMC (publishedVersion)
Research Explorer (The University of Manchester) (publishedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion)
Funders: Cancer Research Manchester Centre (C147/A18083, C147/a25254)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: data mining, oncology, generalizability of trial results, elderly patients, comorbidities, real-world data

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Data Mining in Oncology: The ukCAT Project and the Practicalities of Working with Routine Patient Data

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