Text mining real-world data to evaluate systemic anti-cancer therapy

Author(s):
Laar, S.A. van
Published
2023
Publication type
Doctoral Thesis

Abstract

The aim of this thesis was to investigate if a text-mining tool is suitable for collecting real-world data from electronic health records to evaluate cancer treatments in clinical practice. By investigating a range of use cases including treatments of patients with renal cell carcinoma, hepatocellular carcinoma, melanoma, breast cancer, and COVID-19, it showed that the text-mining tool is a suitable method of data needed for the evaluation of treatment patterns, effectiveness, safety, prognostic factors, and guideline adherence. The discussion showed that enhancing the data quality and actively using real-world data for treatment evaluation regarding treatment policies are some of the next steps.

Text-miningReal-world dataChemotherapyImmunotherapyRenal cell carcinomaMelanomaBreast cancerHepatocellular carcinomaElectronic health recordsCovid-19
Defense date
2023-10-12
Language
English
Supervisor(s)
Guchelaar, H.J.
Advisor(s)
Zwaveling, J.; Gombert-Handoko, K.B.
Committee member(s)
Gelderblom, A.J.; Wouters, M.W.J.M.; Erp, P.H. van; Koopman, M.
Qualification
Ph.D.
Degree granting institution
Faculty of Medicine, Leiden University Medical Center (LUMC), Leiden University, Leiden
hdl
1887/3643700
ISBN(s)
9789464695618