Marco Spruit is an associate professor in the Natural Language Processing group within the Information and Computing Sciences department of the Faculty of Science at Utrecht University in the Netherlands. As principle investigator in the department's Applied Data Science Lab, his research primarily focuses on Self-Service Data Science.

Please refer to my Research Theme page for more information, or my Research Projects page for active projects in this theme.

Top 5 Projects

  1. COVIDA: Computing Visits Data for Dutch Natural Language Processing in Mental Healthcare. EUR 230K (2019-2021). Two-year postdoc, parttime scientific programmer, and deployment infrastructure. Financer(s): Alliance Fund UU-UMCU-TU/e. Applicant(s): Spruit,M., Scheepers,F., & Kaymak,U. et al. Remark: Grant total: 492K EUR.
  2. OPERAMOPtimising thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly. EUR 250K (2015-2020). PhD and postdoc position for the STRIP Assistant (STRIPA) prescriptive analytics platform. Financer: PHC 17-2014. Remark: #634238; grant total: 6.6M EUR.
  3. SMESEC: Protecting Small and Medium-sized Enterprises digital technology through an innovative cyber-SECurity framework. EUR 280K (2017-2020). Two PhD positions for cybersecurity maturity modelling. Financer: H2020-DS-2016-2017. Remark: #740787; grant total: 5.6M EUR.
  4. SAF21: Social Aspects in 21st Century Fisheries. EUR 200K (2015–2018). PhD Project on Natural Language Processing (NLP) applications for a Ph.D student stationed at Manchester Metropolitan University (MMU). Financer: MSCA-ITN-2014-ETN. Remark: #642080; grant total: 2.7M EUR.
  5. STRIMP: Implementatie van de STRIP Assistent ter verbetering van de STRIP medicatiebeoordeling. EUR 110K (2018-2019). Postdoc to integrate the STRIP Assistant within Dutch daily primary care. Financer: ZonMW/Goed Gebruik Geneesmiddelen – Stimulering Toepassing In de Praktijk (GGG – STIP Ronde 3). Remark: Grant total: 350K EUR.

Top 5 Publications

  1. Spruit,M., & Lytras,M. (2018). Applied Data Science in Patient-centric Healthcare: Adaptive Analytic Systems for Empowering Physicians and Patients. Telematics and Informatics, 35(4), 643–653. [IF: 3.714] [pdf] [online]
  2. Menger,V., Scheepers,F., & Spruit,M. (2018). Comparing Deep Learning and Classical Machine Learning Approaches for Predicting Inpatient Violence Incidents from Clinical Text. Applied Sciences, 8(6), Data Analytics in Smart Healthcare, 981. [IF: 2.217] [pdf]
  3. Menger,V., Scheepers,F., Wijk,L. van, & Spruit,M. (2018). DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text. Telematics and Informatics, 35(4), 727–736. [IF: 3.714] [pdf] [online]
  4. Syed,S., Borit,M., & Spruit,M. (2018). Narrow lenses for capturing the complexity of fisheries: A topic analysis of fisheries science from 1990 to 2016. Fish and Fisheries, 1–19. [IF: 6.665] [pdf] [online]
  5. Tawfik,N., & Spruit,M. (2019). UU_TAILS at 2019 MEDIQA Challenge: Learning Textual Entailment in the Medical Domain. Proceedings of the BioNLP 2019 workshop (pp. 493–499). BioNLP 2019, August 1, 2019, Florence, Italy: Association for Computational Linguistics (ACL). [pdf]


Subpages (2): Projects & Team Team