Projects per year
Personal profile
Academic background
Tom Bakkes received his bachelor’s degree in Electrical Engineering in 2016. He continued his studies in Electrical Engineering for his master’s degree and specialized in the signals processing systems group. This is where he got interested in using the techniques he had learned in a Care & Cure setting. For his graduation project he joined the Biomedical Diagnostics lab where he worked on using machine learning for predicting the outcome of in-vitro fertilization treatments. Following that he received his master’s degree in 2018 and has now continued working in the Biomedical Diagnostics lab as a PhD candidate focusing on clinical decision support systems in perioperative care.
Research profile
Tom Bakkes is a PhD candidate in the Biomedical Diagnostics lab at the Eindhoven University of Technology. Here he works on using statistics and machine learning to build clinical decision support systems for the perioperative care. Thereby his current studies focus on automatic detection of patient-ventilation asynchrony, and early detection of deterioration in post-operative patients. For the latter study he works in close collaboration with clinical experts from the Catharina hospital in Eindhoven.
Quote
I work on clinical decision support systems in the perioperative care that will allow clinicians to achieve a more accurate and faster insight on their patients.
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Projects
- 2 Active
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TKI-LSH 2019B015 CaRe-ON: CardiOvascular Research Opting for New Applications: continuous cardiac risk and lifestyle profiling
Bergmans, J. W. M., Papini, G., van der Hagen, D., van der Hagen, D. & Bakkes, T. H. G. F.
1/04/20 → 29/02/24
Project: Research direct
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PPS TKI Philips MEDICAID: MedTech Solutions for Earlier Detection of Cardiovascular Disease
Bergmans, J. W. M., Sammali, F., Bester, M., van de Laar, L., van Gilst, M. M., Mischi, M., Wulterkens, B., van der Hagen, D., Rabotti, C., Vullings, R., Schiphorst, L., Bakkes, T. H. G. F. & Turco, S.
2/01/18 → 30/09/23
Project: Research direct
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Automated detection and classification of patient–ventilator asynchrony by means of machine learning and simulated data
Bakkes, T., van Diepen, A., De Bie, A., Montenij, L., Mojoli, F., Bouwman, A., Mischi, M., Woerlee, P. & Turco, S., Mar 2023, In: Computer Methods and Programs in Biomedicine. 230, 10 p., 107333.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile12 Downloads (Pure) -
Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilation
van Diepen, A., Bakkes, T. H. G. F., de Bie, A. J. R., Turco, S., Bouwman, R. A., Woerlee, P. H. & Mischi, M., Feb 2023, In: Heliyon. 9, 2, 14 p., e13610.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile10 Downloads (Pure) -
A model-based approach to generating annotated pressure support waveforms
van Diepen, A., Bakkes, T. H. G. F., de Bie, A., Turco, S., Bouwman, R. A., Woerlee, P. H. & Mischi, M., Dec 2022, In: Journal of Clinical Monitoring and Computing. 36, 6, p. 1739-1752 14 p.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile2 Citations (Scopus)28 Downloads (Pure) -
A Model-based Approach to Generating Annotated Pressure Support Waveforms
van Diepen, A., Bakkes, T. H. G. F., de Bie, A. J. R., Turco, S., Bouwman, R. A., Woerlee, P. H. & Mischi, M., 9 Dec 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Institute of Electrical and Electronics Engineers, p. 4188-4191 4 p. 9630166Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
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A Model-Based Approach to Synthetic Data Set Generation for Patient-Ventilator Waveforms for Machine Learning and Educational Use
Diepen, A. V., Bakkes, T. H. G. F., de Bie, A. J. R., Turco, S., Bouwman, R. A., Woerlee, P. H. & Mischi, M., 29 Mar 2021, In: arXiv. 2021, 19 p., 2103.15684.Research output: Contribution to journal › Article › Academic
Open AccessFile45 Downloads (Pure)
Thesis
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Machine learning for classification of uterine activity during IVF cycles
Author: Bakkes, T., 13 Dec 2018Supervisor: Mischi, M. (Supervisor 1), Sammali, F. (Supervisor 2) & Schoot, B. (Supervisor 2)
Student thesis: Master