Personal profile

Biography

Goran Nenadic is a Professor in the Department of Computer Science, University of Manchester and a Turing Fellow at the Alan Turing Institute. His research interests are focused on natural language processing, text mining and semi-automated curation of knowledge from unstructured textual data. Goran has been working in this area since 1993. Current research projects focus on large-scale extraction and curation of biomedical information and clinical/epidemiological findings, by comibing rule-based and data-driven approaches. He is also interested in processing healthcare social media.

Goran leads the UK healthcare text analytics network (Healtex) and was the founding Editor-in-Chief of Journal of Biomedical Semantics.

Qualifications

  • 2003. PhD in Computer Science (School of Sciences, University of Salford)
  • 1997. MSc in Computer Science (Faculty of Mathematics, University of Belgrade)
  • 1993. BSc in Mathematics and Computer Science (Faculty of Mathematics, University of Belgrade)

Research interests

Dr Goran Nenadic has been working in the area of natural language processing and text mining since 1993. His research interests include automatic terminology extraction, classification and management, compound word recognition and digital corpora encoding. He also investigates terminology-driven mining of scientific literature (in particular for life sciences) by extracting terms and establishing associations and links among them. He is interested in text analytics and sentiment analysis from online resources, in particular in the healthcare domain.  He leads the UK healthcare text analytics network (http://www.healtex.org).

His current research focus is on large-scale extraction and linking of clinical/epidemiological findings from electronic health records (EHRs), healthcare social media and biomedical literature. His team has worked on mining narratives from EHRs (e.g. for clinical outcomes in cancer patients, or medication prescription extraction), making sense of patient-generated data from social media (e.g. benefits and harms of medical treatments) and providing context to clinical decision support systems (e.g. for treatment planning in brain injuries). He has led a number of joint projects with healthcare service providers (e.g. semi-automated large-scale anonymisation of clinical narratives; identification of mental health symptoms in social media; process mapping of occupational therapy reports), as well as with industrial partners (e.g. dynamic clinical documentation management, information extraction from clinical trial reports). Since 2008, his team has been actively involved in international challenges in clinical text mining. 

My group

Opportunities

I currently supervise a number of postgraduate research students, working on different aspects of text mining For more details, please visit http://gnteam.cs.manchester.ac.uk/

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 5 - Gender Equality
  • SDG 7 - Affordable and Clean Energy
  • SDG 16 - Peace, Justice and Strong Institutions

External positions

Turing Fellow, Alan Turing Institute

1 Sept 2017 → …

Areas of expertise

  • QA75 Electronic computers. Computer science
  • natural language processing

Research Beacons, Institutes and Platforms

  • Institute for Data Science and AI
  • Digital Futures
  • Christabel Pankhurst Institute

Keywords

  • Biomedical Text Mining
  • Clinical Text mining
  • Natural Language Processing
  • Text Analytics
  • Text Mining

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