dr.ir. W. d'Hollosy (Wendy)

Wendy works in the field of health informatics, with as main topics data science, personalized eHealth technologies, clinical decision supports systems, and the application of interoperability and machine learning to optimize the referral and treatment of patients in healthcare. Currently, this research is mainly focused on patients with chronic musculoskeletal pain.

She was involved at RRD (www.rrd.nl) as researcher in the H2020 Back-UP project (http://backup-project.eu/), which aims to create a predictive model to support effective and efficient treatment of neck and lower back pain. Currently, she leads the Pioneers in HealthCare (PIHC) PReferral project on the optimization of existing care pathways and referrals of patients with chronic musculoskeletal pain in 1st, 2nd and 3rd care based on artificial intelligence.

Wendy works 1 day a week at the University of Twente in the biomedical signals and system group (https://www.utwente.nl/en/eemcs/bss) in the Personalized eHealth Technology Program (https://www.utwente.nl/en/techmed/research/research-programmes/ehealth/) and 4 days a week as senior data scientist at Univé (www.unive.nl), a cooperative nonprofit insurance company in the Netherlands.

Medicine & Life Sciences

# Delivery Of Health Care
# Low Back Pain
# Machine Learning
# Musculoskeletal Pain
# Primary Health Care
# Referral And Consultation
# Technology

Engineering & Materials Science

# Machine Learning

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Biomedical Signals and Systems (BSS)

Hietbrink, E. A. G., Middelweerd, A., Empelen, P. V., Preuhs, K. , Konijnendijk, A. A. J. , Nijeweme-d’Hollosy, W. O., Schrijver, L. K., Laverman, G. D. , & Vollenbroek-Hutten, M. M. R. (2023). A Digital Lifestyle Coach (E-Supporter 1.0) to Support People With Type 2 Diabetes: Participatory Development Study. JMIR human factors, 10, [e40017]. https://doi.org/10.2196/40017

Verma, D., Jansen, D., Bach, K. , Poel, M., Mork, P. J. , & d'Hollosy, W. (2022). Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes. BMC medical informatics and decision making, 22, [227]. https://doi.org/10.1186/s12911-022-01973-9

Vaseur, R. , d'Hollosy, W. , & Tabak, M. (2022). Technology-supported shared decision making in the treatment and management of chronic conditions: a systematic review protocol. Abstract from 9th Dutch Bio-Medical Engineering Conference, BME 2022 (Canceled), Netherlands. https://www.bme2022.nl/bme-2022-programme/

Slatman, E. S., Mossink, A., Jansen, D. J., Broeks, J., van der Lugt, P., Prosman, G-J. , & d'Hollosy, W. (2022). Factors used by general practitioners for referring patients with chronic musculoskeletal pain: a qualitative study. BMC family practice, 23, [126]. https://doi.org/10.1186/s12875-022-01743-6

Hoppenbrouwer, X., Rollinson, A. U., Dunsmuir, D., Ansermino, J. M., Dumont, G. , d'Hollosy, W. , Veltink, P. H. , & Garde Martinez, A. (2021). Night to night variability of pulse oximetry features in children at home and at the hospital. Physiological measurement, 42(10), [104003]. https://doi.org/10.1088/1361-6579/ac278e

d'Hollosy, W., Bekmann, A., Jansen, D. J., & Prosman, G-J. (2021). The usage of patient-reported data in machine learning to predict pain rehabilitation: possible or not?. Abstract from BritSpine2021.

d'Hollosy, W., Jansen, D. J. , & Poel, M. (2021). Appropriate healthcare referrals of patients with chronic musculoskeletal pain? We can do better: a multimodal artificial intelligence approach. Abstract from 8th Dutch Bio-Medical Engineering Conference, BME 2021, Virtual Conference.

d'Hollosy, W. , Konijnendijk, A., Bessembinder, R., Leeferink, F., & Broeks, J. (2021). Towards optimized personalized referral advice and patient satisfaction in chronic musculoskeletal pain. Abstract from 8th Dutch Bio-Medical Engineering Conference, BME 2021, Virtual Conference.

Oude Nijeweme - d'Hollosy, W. , van Velsen, L. , Poel, M. , Groothuis-Oudshoorn, C., Soer, R., Stegeman, P. , & Hermens, H. (2020). Applying machine learning on patient-reported data to model the selection of appropriate treatments for low back pain: A Pilot Study. In F. Cabitza, A. Fred, & H. Gamboa (Eds.), Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) (Vol. 5: HEALTHINF, pp. 117-124). SCITEPRESS. https://doi.org/10.5220/0008962101170124

Oude Nijeweme - d'Hollosy, W., Schrijver, L. K. , & Vollenbroek-Hutten, M. M. R. (2019). E-Supporter: Personalized Technology Supported Coaching of Patients with Chronic Diseases. Poster session presented at 7th Dutch Bio-Medical Engineering Conference, BME 2019, Egmond aan zee, Netherlands.

Stegeman, P., Speijer, L. G. N., Hamelink, J. L. C., Sterken, M. W., Soer, R., Wolff, A. P., Schiphorst Preuper, H. R., Reneman, M. F. , & Oude Nijeweme - d'Hollosy, W. (2018). INTERRATER RELIABILITY OF TRIAGING PATIENTS WITH LOW BACK PAIN TO INTERVENTION BASED ON SELF-REPORTS ONLY. Paper presented at SBPR 2018, Groningen, Netherlands.

Cabrita, A. M. , Oude Nijeweme - d'Hollosy, W. , & Jansen-Kosterink, S. (2018). Back-UP: Personalised Prognostic Models To Improve Well-Being And return To Work After Neck and Low Back Pain. 26. Paper presented at SBPR 2018, Groningen, Netherlands.

Oude Nijeweme - d'Hollosy, W. , Poel, M. , van Velsen, L. S. , Oudshoorn, C. , Hermens, H. J., Stegeman, P., Wolff, A. P., Reneman, M. F., & Soer, R. (2018). Personalized advice on chronic low back pain interventions: a machine learning approach. 25. Paper presented at SBPR 2018, Groningen, Netherlands.

Oude Nijeweme-d'Hollosy, W., Notenboom, T. , & Banos, O. (2018). A Study on the Perceptions of Autistic Adolescents towards Mainstream Emotion Recognition Technologies. Proceedings (MDPI), 2(19), [1200]. https://doi.org/10.3390/proceedings2191200

Oude Nijeweme - d'Hollosy, W. , van Velsen, L. S., Henket, A. , & Hermens, H. J. (2018). An Interoperable eHealth Reference Architecture for Primary Care. In 2018 IEEE Symposium on Computers and Communications (ISCC) IEEE. https://doi.org/10.1109/ISCC.2018.8538576

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University of Twente
Drienerlolaan 5
7522 NB Enschede
The Netherlands

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University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Biomedical Signals and Systems (BSS)

University of Twente Drienerlolaan 5
7522 NB Enschede

+31 (0)53 489 9111
info@utwente.nl
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