Analytic Systems in Applied Data Science

Research theme of Marco Spruit

Bio in 95 words

Marco Spruit is a tenured researcher at the Information and Computing Sciences department of Utrecht University in the Netherlands. As principle investigator in the department's Applied Data Science LabMarco’s research centres around Analytic Systems in Applied Data Science with special attention to Health Analytic Systems. Marco serves on the editorial boards of the international journals on Decision Analytics, Business Intelligence Research, Semantic Web and Information Systems, Autonomic Computing, and Computer Information Systems. Before 2007 Marco worked in industry as a software developer for fourteen years in the fields of Business Intelligence and Text Analytics.

Analytic Systems

The research theme Analytics Systems investigates utility determinants of analytic systems in daily practices from a people-process-technology perspective using an action research approach. It considers metrics such as effectiveness, efficiency and usability to determine a system’s societal impact. One novel aspect is its specific aim to collect these measurements from daily practices instead of from merely computational experiments. This, however, requires a significant software prototype engineering effort up front before an analytic system’s utility can actually be determined. A strategic secondary objective of this research is to improve transparancy in decision making processes throughout application domains.

This theme relates research fields such as evidence-adaptive Decision support systems which deploy Data analytics derived actionable information, often depending on Text analytics based preprocessing as up to 90% of all data sources are known to be either semi-structured or unstructured. Information infrastructure technologies provide a proven overarching framework for data storage architectures, from datawarehouses to data lakes. Finally, as the aim is to determine the utility of analytic systems in daily practices, I include Persuasive technologies to ensure continuous alignment of people, process, and technology perspectives.

Top 5 Publications

  1. Spruit,M., & Jagesar,R. (2016). Power to the People! Meta-algorithmic modelling in applied data science. In Fred,A. et al. (Ed.), Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 400–406). KDIR 2016, November 11-13, 2016, Porto, Portugal: ScitePress. [pdf] [online]
  2. Spruit,M., Vroon,R., & Batenburg,R. (2014). Towards healthcare business intelligence in long-term care: an explorative case study in the Netherlands. Computers in Human Behavior, 30, 698–707. [ISI impact factor: 2.293] [pdf] [online]
  3. 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: 2.503] [pdf] [online]
  4. Pachidi,S., Spruit,M., & Weerd,I. van der (2014). Understanding Users' Behavior with Software Operation Data Mining. Computers in Human Behavior, 30, Special Issue: ICTs for Human Capital, 583–594. [ISI impact factor: 2.293] [pdf] [online]
  5. Menger,V., Scheepers,F., Wijk,L. van, & Spruit,M. (In press). DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text. Telematics and Informatics. [ISI impact factor: 3.398] [online]

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. Type: Competitive funding (Horizon2020).
  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. Type: Competitive funding (Horizon2020).
  3. TAF21: Text Analytics for 21st Century Fisheries (2015 – 2018). PhD Project for Early Stage Researcher (ESR) 7 at Manchester Metropolitan University (MMU). Type: Competitive funding (Horizon2020).
  4. PRAISE: Psychiatry Research Analytics InfraStructurE (2015-2019). PhD Project in the Big Data Psychiatry programme on an interorganisational integration architecture & interactive analytics platform. Type: Research collaboration.
  5. CESCA: CEll SCreening Architecture & Analytics (2011 – 2017). PhD Project on an online platform for high content screening and cloud-based data analysis services for drug target discovery and validation, leads discovery and optimization, and the assessment of cellular toxicity. Type: Research collaboration.
Subpages (2): Projects Team