Tom Bakkes
Department / Institute
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.
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.
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.
Recent Publications
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Eveline H.J. Mestrom,Tom H.G.F. Bakkes,Nassim Ourahou,Hendrikus H.M. Korsten,Paulo de Andrade Serra,Leon J. Montenij,Massimo Mischi,Simona Turco,R. Arthur Bouwman
Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors
PLoS ONE (2023) -
pariatur
Automated detection and classification of patient–ventilator asynchrony by means of machine learning and simulated data
Computer Methods and Programs in Biomedicine (2023) -
Tom Bakkes,Anouk van Diepen,Ashley De Bie,Leon Montenij,Francesco Mojoli,Arthur Bouwman,Massimo Mischi,Pierre Woerlee,Simona Turco
Automated detection and classification of patient–ventilator asynchrony by means of machine learning and simulated data
Computer Methods and Programs in Biomedicine (2023) -
est
Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilation
Heliyon (2023) -
Anouk van Diepen,T.H.G.F. Bakkes,A.J.R. de Bie,S. Turco,R.A. Bouwman,P.H. Woerlee,M. Mischi
Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilation
Heliyon (2023)
Ancillary Activities
No ancillary activities