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.

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. She also led 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. Currently, she is involved in the RE-SAMPLE project that aims to develop AI-powered care for patients with COPD and other chronic illnesses (https://www.re-sample.eu/)

Wendy works 1 day a week at the University of Twente in the Biomedical Signals and System (BSS) group (https://www.utwente.nl/en/eemcs/bss) in the personalized eHealth team and 4 days a week as internal auditor 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)

Vaseur, R. M. E., te Braake, E. , Beinema, T. , Oude Nijeweme-d'Hollosy, W. , & Tabak, M. (2024). Technology-supported shared decision-making in chronic conditions: a systematic review of randomized controlled trials. Patient education and counseling, 124, Article 108267. https://doi.org/10.1016/j.pec.2024.108267

Hietbrink, E. A. G. , Oude Nijeweme-d’Hollosy, W., Middelweerd, A. , Konijnendijk, A. A. J., Schrijver, L. K., ten Voorde, A. S., Fokkema, E. M. S., Laverman, G. D. , & Vollenbroek-Hutten, M. M. R. (2023). A Digital Coach (E-Supporter 1.0) to Support Physical Activity and a Healthy Diet in People With Type 2 Diabetes: Acceptability and Limited Efficacy Testing. JMIR Formative Research, 7, Article e45294. https://doi.org/10.2196/45294

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, Article e40017. https://doi.org/10.2196/40017

Hietbrink, E. A. G. , Nijeweme-d’Hollosy, W. O., Middelweerd, A. , Konijnendijk, A. A. J., Schrijver, L. K., ten Voorde, A. S., Fokkema, E. M. S., Laverman, G. D. , & Vollenbroek-Hutten, M. M. R. (2022). A Digital Coach (E-Supporter 1.0) to Support Physical Activity and a Healthy Diet in People With Type 2 Diabetes: Acceptability and Limited Efficacy Testing. JMIR Publications. https://doi.org/10.2196/preprints.45294

Vaseur, R., te Braake, E. , Beinema, T. , d'Hollosy, W. , & Tabak, M. (2022). Technology-supported shared decision-making in chronic conditions: preliminary results of a systematic review. European respiratory journal, 60. https://doi.org/10.1183/13993003.congress-2022.2987

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, Article 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, Article 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), Article 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.

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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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