For the latest publications of this PI visit Marsh Konigs – Amsterdam UMC
Konigs, M. (Marsh) m.konigs@amsterdamumc.nl
| Research line | Neuroscientic outcome measurement |
|---|---|
| Department | General Pediatrics |
| Research Institute(s) | Amsterdam Reproduction and Development |
| Description | This research line aims to determine the impact of disease and treatment on the brain of children and young adults. The work focuses on the development and application of neuroscientific outcome measurements in typically developing and clinical groups, assessing neurocognitive functioning in children and adults (e.g. the in-house developed ‘Emma Toolbox for Neurocognitive Functioning’), advanced analysis of neurocognitive test performance (e.g. neurocognitive networks), structural and functional brain networks (e.g. DTI resting-state fMRI), and early neurocognitive functioning in babies and infants (e.g. eye-tracking). |
| Seniors | • Jaap Oosterlaan, PI • Cece Kooper, Postdoc |
| Projects | |
| The HEADLINE Study | Marloes Hoppen investigates the acute impact of heading in amateur football using blood biomarkers. |
| The PEPR Study | Cece Kooper determines neurodevelopmental outcome of traumatic brain injury in a research network of hospitals using neurocognitive testing, EEG and advanced multimodal MRI. |
| The ONSET Study | Noa Ijdo tracks early neurocognitive development in infants using with sickle cell disease using eyetracking (Noa Ijdo) |
| The MATURE Study | Dook Koch investigates the long term outcome of meningitis among young adults infected in childhood using neurocognitive testing and advanced multimodal MRI. |
| The INPACT Study | Sophie Lijdsman investigates the impact of chronic kidney disease and associated treatments on the brain of children and young adults using neurocognitive testing, EEG and diffusion tensor imaging. |
| Research line | Clinical outcome prediction for precision medicine |
|---|---|
| Department | General pediatrics |
| Research Institute(s) | Amsterdam Reproduction and Development |
| Description | This research line is aimed at better prediction of patient outcome, contributing to the transition towards precision medicine by improving clinical outcome prediction. Together with prof. dr. Mark Hoogendoorn & dr. Frank Bennis, we investigate the added value of machine learning models for clinical outcome prediction. We are also developing a machine learning pipeline optimized for high-dimensional data. |
| Seniors | • Jaap Oosterlaan, PI • Frank Bennis, UD |
| Projects | |
| The Extubation Success Prediction study | Frank Bennis develops a machine learning model using routine clinical data and device data that provides online predictions of extubation success for children on mechanical ventilation at the Pediatric Intensive Care Unit. |
| The BPD prediction Study | Frank Bennis developed a model that predicts the development of BPD (at 36 weeks) using routinely collected clinical data and continuous vital signs collected in the first 7 days after birth. The prediction model consists of an auto-encoder with long short-term memory model, embedded in a neural network. The model is currently implemented in EPIC for automatic outcome prediction and real-world model performance monitoring. |
| The PEPR Study | Cece Kooper determines the added value of machine learning models to predict outcome based premorbid data, hospital data and multimodal MRI data assessing structural and functional brain connectivity. |
| The ONSET Study | Noa Ijdo determines predictors of early adverse neurocognitive development in infants using with sickle cell disease |
| The MATURE Study | Dook Koch determines predictors of very long-term term outcome of childhood meningitis in young adulthood. |
| Research line | Data-driven health care innovation | |
|---|---|---|
| Department | General Pediatrics | |
| Research Institute(s) | Amsterdam Reproduction and Development | |
| Description | This research line aims to innovate health care using structured clinical data, by integrating care, evaluation and scientific research using structured care paths. This work is focused on the development of structured multidisciplinary clinical (follow-up) programs that make use of structured electronic clinical registration and integrate with other clinical data sources (e.g. medical devices, patient reported outcome measurements). Consequently, the structured clinical data flows into rich and ever-accumulating databases that are re-used for care evaluation and scientific research aimed at data-driven care innovation. The Emma Children's Hospital Follow Me program is the blueprint for this work. | |
| Seniors | • Jaap Oosterlaan, PI • Ruud van der Veen, post-doc | |
| Projects | ||
| Follow Me | Follow Me is an ambitious program that develops and implements structured outpatient care for all tertiary care patients at the Emma Children’s Hospital of Amsterdam UMC with the ambition to improve clinical follow-up, support data-driven health care evaluation, facilitate clinical research to improve clinical care, and educate the future generation of health care professionals. Follow Me care paths are implemented at the departments of Pediatric Surgery (Joep Derikx), Pediatric Intensive Care Unit (Marieke Otten), Pediatric Cardiology (Stijn Haas, Jane Koster), Neonatal Intensive Care (Menne van Boven) and Endocrinology (Lieve Willemsen). | |
| The Royal Dutch Football Association’s Concussion Clinic | The Royal Duthc Football Association's Concussion Clinic, supervised by Marloes Hoppen, is a combined clinical and research outpatient clinic for patients with persisting symptoms after sport-related concussion. The structured care path contributes to continuously expanding database for care evaluation and scientific research. The cohort involves more than 500 patients in 2025, representing the largest cohort of patients with persisting symptoms after sport-related concussion in Europe. Research based on this cohort is part of the Dutch Clinical Guidelines for care in this patient group. | |
| The Health Intelligence Program at the Daan Theeuwes Center for Intensive Neurorehabilitation | Ruud van der Veen aims to provide continuous care evaluation for young adults with severe brain injury. As part of this program, interprofessional data collection has been structured in a Measurement Feedback System. This clinical data is routinely measured as part of the primary care process, captured in an electronic patient data platform, directly fed back to the interdisciplinary team by individual patient dashboards and used as a basis for clinical decision-making by the interdisciplinary team meeting in cycles of 6 weeks. The structured data also flows into continuously expanding databases with longitudinal data tracking interdisciplinary recovery progress, used for a periodic cycle of care evaluation and scientific research aimed at the transition towards precision medicine. |
Last edited: 05-03-2026