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Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data

Volume 7947 of the series Lecture Notes in Computer Science pp 13-24

Combining HCI, Natural Language Processing, and Knowledge Discovery - Potential of IBM Content Analytics as an Assistive Technology in the Biomedical Field

  • Andreas HolzingerAffiliated withLancaster UniversityInstitute for Medical Informatics, Statistics & Documentation, Research Unit HCI4MED, Medical University Graz
  • , Christof StockerAffiliated withLancaster UniversityInstitute for Medical Informatics, Statistics & Documentation, Research Unit HCI4MED, Medical University Graz
  • , Bernhard OfnerAffiliated withLancaster UniversityInstitute for Medical Informatics, Statistics & Documentation, Research Unit HCI4MED, Medical University Graz
  • , Gottfried ProhaskaAffiliated withLancaster UniversityIBM Austria
  • , Alberto BrabenetzAffiliated withLancaster UniversityIBM Austria
  • , Rainer Hofmann-WellenhofAffiliated withCarnegie Mellon UniversityDepartment for Dermatology, LKH-University Hospital Graz

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Abstract

Medical professionals are confronted with a flood of big data most of it containing unstructured information. Such unstructured information is the subset of information, where the information itself describes parts of what constitutes as significant within it, or in other words - structure and information are not completely separable. The best example for such unstructured information is text. For many years, text mining has been an essential area of medical informatics. Although text can easily be created by medical professionals, the support of automatic analyses for knowledge discovery is extremely difficult. We follow the definition that knowledge consists of a set of hypotheses, and knowledge discovery is the process of finding or generating new hypotheses by medical professionals with the aim of getting insight into the data. In this paper we present some lessons learned of ICA for dermatological knowledge discovery, for the first time. We follow the HCI-KDD approach, i.e. with the human expert in the loop matching the best of two worlds: human intelligence with computational intelligence.

Keywords

Knowledge discovery data mining human-computer interaction medical informatics Unstructured Information Management Content Analytics