Ph.D. Student: Mª del Carmen Luque
Advisors: Jose M. Luna, Sebastián Ventura
Defended on: September 2020
Keywords: text mining, multi-label learning, classification, named entity recognition
Digital version: PDF


The main objective of this thesis is to develop a system based on Text Mining capable of transforming the textual clinical information in Knowledge to support the health professional in making decisions that allow the early detection of a disease.

The partial objectives are the following:

  • Design and develop a system that allows the extraction of named entities in the field of Medicine. The application will allow the detection of the following named medical entities: diagnosis, pharmacology, procedures, findings/symptoms and anatomical location.
  • Develop a system capable of identifying risk factors or clinical alerts based on the named medical entities detected in the clinical reports.
  • Develop a system that allows the automatic detection of negations in clinical reports.
  • Design and implement a system that allows automatically inferring and categorizing clinical reports generating standardized diagnostic codes based on the paradigm of multi-label classification.


The development of this thesis is being supported by:

  • Spanish Ministry of Science and Competitiveness, project TIN-2014-55252-P.
  • Spanish Ministry of Science and Competitiveness, project TIN-2017-83445-P.


  1. C. Luque, J.M. Luna, M. Luque, S. Ventura. An advanced review on text mining in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9, n. 3, p. e1302. 2019. DOI: 10.1002/widm.1302.
  2. C. Luque, J.M. Luna, S. Ventura. A semantically enriched text mining system for clinical decision support. Computational Intelligence, pp. 1-26. 2020. DOI: https://doi.org/10.1111/coin.12322.
  1. C. Luque, J.M. Luna, S. Ventura. MiNerDoc: a Semantically Enriched Text Mining System to Transform Clinical Text into Knowledge. In IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 702-707. 2019. DOI: 10.1109/CBMS.2019.00142.

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