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

Laar, S.A. van (2023)

Doctoral Thesis

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-mining
• Real-world data
• Chemotherapy
• Immunotherapy
• Renal cell carcinoma
• Melanoma
• Breast cancer
• Hepatocellular carcinoma
• Electronic health records
• Covid-19

• All authors: Laar, S.A. van
• Supervisor: Guchelaar, H.J.
• Co-supervisor: Zwaveling, J.; Gombert-Handoko, K.B.
• Committee: Gelderblom, A.J.; Wouters, M.W.J.M.; Erp, P.H. van; Koopman, M.
• Qualification: Doctor (dr.)
• Awarding Institution: Faculty of Medicine, Leiden University Medical Center (LUMC), Leiden University
• Date: 2023-10-12
• ISBN (print): 9789464695618