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342 results on '"Dekker, Andre"'

1. Mind Your Data: Privacy and Legal Matters in eHealth

Author

Zegers, Catharina M L, Witteveen, Annemieke, Schulte, Mieke H J, Henrich, Julia F, Vermeij, Anouk, Klever, Brigit, and Dekker, Andre

Subjects
Medicine
Abstract

The health care sector can benefit considerably from developments in digital technology. Consequently, eHealth applications are rapidly increasing in number and sophistication. For successful development and implementation of eHealth, it is paramount to guarantee the privacy and safety of patients and their collected data. At the same time, anonymized data that are collected through eHealth could be used in the development of innovative and personalized diagnostic, prognostic, and treatment tools. To address the needs of researchers, health care providers, and eHealth developers for more information and practical tools to handle privacy and legal matters in eHealth, the Dutch national Digital Society Research Programme organized the “Mind Your Data: Privacy and Legal Matters in eHealth” conference. In this paper, we share the key take home messages from the conference based on the following five tradeoffs: (1) privacy versus independence, (2) informed consent versus convenience, (3) clinical research versus clinical routine data, (4) responsibility and standardization, and (5) privacy versus solidarity.

Published
2021
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7. VertiBayes: Learning Bayesian network parameters from vertically partitioned data with missing values

Author

van Daalen, Florian, Ippel, Lianne, Dekker, Andre, and Bermejo, Inigo

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

Federated learning makes it possible to train a machine learning model on decentralized data. Bayesian networks are probabilistic graphical models that have been widely used in artificial intelligence applications. Their popularity stems from the fact they can be built by combining existing expert knowledge with data and are highly interpretable, which makes them useful for decision support, e.g. in healthcare. While some research has been published on the federated learning of Bayesian networks, publications on Bayesian networks in a vertically partitioned or heterogeneous data setting (where different variables are located in different datasets) are limited, and suffer from important omissions, such as the handling of missing data. In this article, we propose a novel method called VertiBayes to train Bayesian networks (structure and parameters) on vertically partitioned data, which can handle missing values as well as an arbitrary number of parties. For structure learning we adapted the widely used K2 algorithm with a privacy-preserving scalar product protocol. For parameter learning, we use a two-step approach: first, we learn an intermediate model using maximum likelihood by treating missing values as a special value and then we train a model on synthetic data generated by the intermediate model using the EM algorithm. The privacy guarantees of our approach are equivalent to the ones provided by the privacy preserving scalar product protocol used. We experimentally show our approach produces models comparable to those learnt using traditional algorithms and we estimate the increase in complexity in terms of samples, network size, and complexity. Finally, we propose two alternative approaches to estimate the performance of the model using vertically partitioned data and we show in experiments that they lead to reasonably accurate estimates., Comment: An updated verson of this paper has been accepted for publication in Springer Complex & Intelligent Systems. The final version can be found here: https://link.springer.com/article/10.1007/s40747-024-01424-0

Published
2022
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11. Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: A Netherlands consortium of dementia cohorts case study

Author

Mateus, Pedro, Moonen, Justine, Beran, Magdalena, Jaarsma, Eva, van der Landen, Sophie M., Heuvelink, Joost, Birhanu, Mahlet, Harms, Alexander G.J., Bron, Esther, Wolters, Frank J., Cats, Davy, Mei, Hailiang, Oomens, Julie, Jansen, Willemijn, Schram, Miranda T., Dekker, Andre, and Bermejo, Inigo

Published
2024
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18. Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer

Author

Mayo, Charles S., Feng, Mary U., Brock, Kristy K., Kudner, Randi, Balter, Peter, Buchsbaum, Jeffrey C., Caissie, Amanda, Covington, Elizabeth, Daugherty, Emily C., Dekker, Andre L., Fuller, Clifton D., Hallstrom, Anneka L., Hong, David S., Hong, Julian C., Kamran, Sophia C., Katsoulakis, Eva, Kildea, John, Krauze, Andra V., Kruse, Jon J., McNutt, Tod, Mierzwa, Michelle, Moreno, Amy, Palta, Jatinder R., Popple, Richard, Purdie, Thomas G., Richardson, Susan, Sharp, Gregory C., Satomi, Shiraishi, Tarbox, Lawrence R., Venkatesan, Aradhana M., Witztum, Alon, Woods, Kelly E., Yao, Yuan, Farahani, Keyvan, Aneja, Sanjay, Gabriel, Peter E., Hadjiiski, Lubomire, Ruan, Dan, Siewerdsen, Jeffrey H., Bratt, Steven, Casagni, Michelle, Chen, Su, Christodouleas, John C., DiDonato, Anthony, Hayman, James, Kapoor, Rishhab, Kravitz, Saul, Sebastian, Sharon, Von Siebenthal, Martin, Bosch, Walter, Hurkmans, Coen, Yom, Sue S., and Xiao, Ying

Published
2023
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19. Defining the role of real-world data in cancer clinical research: The position of the European Organisation for Research and Treatment of Cancer

Author

Saesen, Robbe, Van Hemelrijck, Mieke, Bogaerts, Jan, Booth, Christopher M., Cornelissen, Jan J., Dekker, Andre, Eisenhauer, Elizabeth A., Freitas, André, Gronchi, Alessandro, Hernán, Miguel A., Hulstaert, Frank, Ost, Piet, Szturz, Petr, Verkooijen, Helena M., Weller, Michael, Wilson, Roger, Lacombe, Denis, and van der Graaf, Winette T.

Published
2023
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23. Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization

Author

Varghese, Amal Joseph, Gouthamchand, Varsha, Sasidharan, Balu Krishna, Wee, Leonard, Sidhique, Sharief K, Rao, Julia Priyadarshini, Dekker, Andre, Hoebers, Frank, Devakumar, Devadhas, Irodi, Aparna, Balasingh, Timothy Peace, Godson, Henry Finlay, Joel, T, Mathew, Manu, Gunasingam Isiah, Rajesh, Pavamani, Simon Pradeep, and Thomas, Hannah Mary T

Published
2023
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25. Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors

Author

Tohidinezhad, Fariba, Bontempi, Dennis, Zhang, Zhen, Dingemans, Anne-Marie, Aerts, Joachim, Bootsma, Gerben, Vansteenkiste, Johan, Hashemi, Sayed, Smit, Egbert, Gietema, Hester, Aerts, Hugo JWL., Dekker, Andre, Hendriks, Lizza E.L., Traverso, Alberto, and De Ruysscher, Dirk

Published
2023
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28. Bayesian network structure for predicting local tumor recurrence in rectal cancer patients treated with neoadjuvant chemoradiation followed by surgery

Author

Osong, Biche, Masciocchi, Carlotta, Damiani, Andrea, Bermejo, Inigo, Meldolesi, Elisa, Chiloiro, Giuditta, Berbee, Maaike, Lee, Seok Ho, Dekker, Andre, Valentini, Vincenzo, Gerard, Jean-Pierre, Rödel, Claus, Bujko, Krzysztof, van de Velde, Cornelis, Folkesson, Joakim, Sainato, Aldo, Glynne-Jones, Robert, Ngan, Samuel, Brændengen, Morten, Sebag-Montefiore, David, and van Soest, Johan

Published
2022
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29. Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study

Author

Theophanous, Stelios, Lønne, Per-Ivar, Choudhury, Ananya, Berbee, Maaike, Dekker, Andre, Dennis, Kristopher, Dewdney, Alice, Gambacorta, Maria Antonietta, Gilbert, Alexandra, Guren, Marianne Grønlie, Holloway, Lois, Jadon, Rashmi, Kochhar, Rohit, Mohamed, Ahmed Allam, Muirhead, Rebecca, Parés, Oriol, Raszewski, Lukasz, Roy, Rajarshi, Scarsbrook, Andrew, Sebag-Montefiore, David, Spezi, Emiliano, Spindler, Karen-Lise Garm, van Triest, Baukelien, Vassiliou, Vassilios, Malinen, Eirik, Wee, Leonard, and Appelt, Ane L.

Published
2022
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39. Development and validation of radiomic signature for predicting overall survival in advanced-stage cervical cancer.

Author

Jha, Ashish Kumar, Mithun, Sneha, Sherkhane, Umeshkumar B., Jaiswar, Vinay, Shah, Sneha, Purandare, Nilendu, Prabhash, Kumar, Maheshwari, Amita, Gupta, Sudeep, Wee, Leonard, Rangarajan, V., and Dekker, Andre

Subjects
RANDOM forest algorithms, SQUAMOUS cell carcinoma, CERVIX uteri tumors, PREDICTION models, DATA analysis, RESEARCH funding, RADIOMICS, LOGISTIC regression analysis, DIGITAL signatures, CHEMORADIOTHERAPY, POSITRON emission tomography computed tomography, RETROSPECTIVE studies, DESCRIPTIVE statistics, RADIOISOTOPE brachytherapy, SUPPORT vector machines, STATISTICS, DICOM (Computer network protocol), COMPARATIVE studies, MACHINE learning, DATA analysis software, CONFIDENCE intervals, OVERALL survival, ALGORITHMS
Abstract

Background: The role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide, contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance. Purpose: The purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features. Materials and Methods: Pretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital, were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC), were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation. Results: The average prediction accuracy was found to be 0.65 (95% CI: 0.60– 0.70), 0.72 (95% CI: 0.63–0.81), and 0.77 (95% CI: 0.72–0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62–0.76), 0.79 (95% CI: 0.72–0.86), 0.71 (95% CI: 0.62–0.80), and 0.72 (95% CI: 0.66–0.78) for LR, RF, SVC and GBC models developed on three datasets respectively. Conclusion: Our study shows the promising predictive performance of a robust radiomic signature to predict 5-year overall survival in cervical cancer patients. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Getting real about synthetic data ethics: Are AI ethics principles a good starting point for synthetic data ethics?

Author

Shanley, Danielle, Hogenboom, Joshi, Lysen, Flora, Wee, Leonard, Lobo Gomes, Aiara, Dekker, Andre, and Meacham, Darian

Abstract

Synthetic data promises to be a viable alternative when data collection and data sharing may not be feasible or cost effective, but it raises distinct ethical issue that merit serious consideration. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Colorectal cancer health and care quality indicators in a federated setting using the Personal Health Train.

Author

Choudhury, Ananya, Janssen, Esther, Bongers, Bart C., van Meeteren, Nico L. U., Dekker, Andre, and van Soest, Johan

Subjects
MEDICAL quality control, COLORECTAL cancer, PERSONAL training, MEDICAL personnel, CANCER treatment, HOSPITALS, CANCER hospitals
Abstract

Objective: Hospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric 'Proof of Principle' study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources. Materials and methods: Two quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data. Results: ETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted. Discussion and conclusion: PHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Characterisation and classification of oligometastatic disease: a European Society for Radiotherapy and Oncology and European Organisation for Research and Treatment of Cancer consensus recommendation

Author

Guckenberger, Matthias, Lievens, Yolande, Bouma, Angelique B, Collette, Laurence, Dekker, Andre, deSouza, Nandita M, Dingemans, Anne-Marie C, Fournier, Beatrice, Hurkmans, Coen, Lecouvet, Frédéric E, Meattini, Icro, Méndez Romero, Alejandra, Ricardi, Umberto, Russell, Nicola S, Schanne, Daniel H, Scorsetti, Marta, Tombal, Bertrand, Verellen, Dirk, Verfaillie, Christine, and Ost, Piet

Published
2020
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