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... Show moreThe 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. Show less
Purpose Chemotherapy-induced febrile neutropenia (FN) is a life-threatening and chemotherapy dose-limiting adverse event. FN can be prevented with granulocyte-colony stimulating factors (G-CSFs).... Show morePurpose Chemotherapy-induced febrile neutropenia (FN) is a life-threatening and chemotherapy dose-limiting adverse event. FN can be prevented with granulocyte-colony stimulating factors (G-CSFs). Guidelines recommend primary G-CSF use for patients receiving either high (> 20%) FN risk (HR) chemotherapy, or intermediate (10-20%) FN risk (IR) chemotherapy if the overall risk with additional patient-related risk factors exceeds 20%. In this study, we applied an EHR text-mining tool for real-world G-CSF treatment evaluation in breast cancer patients. Methods Breast cancer patients receiving IR or HR chemotherapy treatments between January 2015 and February 2021 at LUMC, the Netherlands, were included. We retrospectively collected data from EHR with a text-mining tool and assessed G-CSF use, risk factors, and the FN and neutropenia (grades 3-4) and incidence. Results A total of 190 female patients were included, who received 77 HR and 113 IR treatments. In 88.3% of the HR regimens, G-CSF was administered; 7.3% of these patients developed FN vs. 33.3% without G-CSF. Although most IR regimen patients had >= 2 risk factors, only 4% received G-CSF, of which none developed neutropenia. However, without G-CSF, 11.9% developed FN and 31.2% severe neutropenia. Conclusions Our text-mining study shows high G-CSF use among HR regimen patients, and low use among IR regimen patients, although most had >= 2 risk factors. Therefore, current practice is not completely in accordance with the guidelines. This shows the need for increased awareness and clarity regarding risk factors. Also, text-mining can effectively be implemented for the evaluation of patient care. Show less
Dijk, W.B. van; Fiolet, A.T.L.; Schuit, E.; Sammani, A.; Groenhof, T.K.J.; Graaf, R. van der; ... ; Mosterd, A. 2021
Objective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of... Show moreObjective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial.Study Design and Setting: In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel.Results: Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4-8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7-92.8%).Conclusion: Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information. (C) 2020 The Authors. Published by Elsevier Inc. Show less
Text-mining is a challenging field of research initially meant for reading large text collections with a computer. Text-mining is useful in summarizing text, searching for the informative documents... Show moreText-mining is a challenging field of research initially meant for reading large text collections with a computer. Text-mining is useful in summarizing text, searching for the informative documents, and most important to do knowledge discovery. Knowledge discovery is the main subject of this thesis. The hypothesis that knowledge discovery is possible started with the work done by Swanson. He made, as a first finding, links between Raynaud__s disease and fish oil using intermediate medical terms to relate them to each other. This principle was formalized in the AB- C concept. A and C are not directly related to each other but via an intermediate concept B that needs to be discovered. Tex data can be extended by adding other non textual data such as microarray experiments. Then we are in the field of data-mining. The final goal is to do all kinds of discoveries with computer (in silico) using data sources in order to assist biology research to save time and discover more. Show less
