Marco Spruit is Principle Investigator in the Applied Data Science Lab at the Information and Computing Sciences department of Utrecht University in the Netherlands. His research focuses on MODEL-DRIVEN ANALYTIC SYSTEMS for Self-Service Data Science: a novel research domain for a novel problem domain.

The emerging importance of self-servicing data science brings many new opportunities, but also new research challenges. First, we should empower domain professionals and citizens to maximise the societal impact of data science technologies. Second, we should evaluate our Analytic Systems in daily practice, where they are needed: Society is our Lab! Third, we should beforehand incorporate all relevant knowledge into our analytic systems to improve their potential and afterwards curate our resulting findings as meta-algorithmic models for improved reusability and reproducibilty.

Marco serves on the editorial boards of the international journals on Semantic Web and Information Systems, Computer Information Systems, and Big Data and Analytics in Healthcare, among others. Before 2007 Marco worked in industry as a Big Text Analytics & Systems engineer for fourteen years.

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

Theme

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: 3.398] [pdf] [url] [AHM]
  2. 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: 3.398] [pdf] [url] [AHNI]
  3. 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: 9.013] [pdf] [online[N]
  4. Spruit,M., Heeringa,W., & Nerbonne,J. (2009). Associations among linguistic levels. Lingua, 119(11), The forests behind the trees, 1624–1642. [ISI impact factor: 0.578] [pdf] [url] [N]
  5. Meulendijk,M., Spruit,M., Drenth-van Maanen,C., Numans,M., Brinkkemper,S., Jansen,P., & Knol,W (2015). Computerized decision support improves medication review effectiveness: an experiment evaluating the STRIP Assistant’s usability. Drugs & Aging, 32(6), 495–503. [ISI impact factor: 2.769] [pdf] [url] [AHI]

Top 5 Projects

  1. OPERAMOPtimising thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly (2015-2020). PhD and postdoc position to further develop the STRIP Assistant (STRIPA) prescriptive analytics platform as the intervention instrument in a multi-language RCT (Horizon2020; 6.6M EUR). [AHIN]
  2. SMESEC: Protecting Small and Medium-sized Enterprises digital technology through an innovative cyber-SECurity framework (2017-2020). PhD position and PI time to further develop the ISFAM family of information security maturity models (Horizon2020; 5.6M EUR). [AHI]
  3. SAF21: Text Analytics for 21st Century Fisheries (2015 – 2018). PhD Project for Early Stage Researcher (ESR) 7 at Manchester Metropolitan University (MMU) (Horizon2020; 2.7M EUR). [AN]
  4. STRIMP: Implementation of the STRIP Assistant to improve the STRIP medication review (2017 – 2019). Scientific programmer for two years to integrate the STRIP Assistant within Dutch daily primary care (ZonMW; 350K EUR). [AHI]
  5. OPTICA: Optimising PharmacoTherapy In the multimorbid elderly in Primary CAre: a cluster randomised controlled trial (2017-2019). Parttime postdoc position to prepare an RCT to implement STRIPA 2.0 in daily GP practices in Switzerland (SNF; 475K EUR). [AHI]


Subpages (2): Projects Team