A Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics

  • Danilo DessìEmail author
  • Diego Reforgiato Recupero
  • Gianni Fenu
  • Sergio Consoli
Part of the Intelligent Systems Reference Library book series (ISRL, volume 149)


During the last decades, a huge amount of data have been collected in clinical databases in the form of medical reports, laboratory results, treatment plans, etc., representing patients health status. Hence, digital information available for patient-oriented decision making has increased drastically but it is often not mined and analyzed in depth since: (i) medical documents are often unstructured and therefore difficult to analyze automatically, (ii) doctors traditionally rely on their experience to recognize an illness, give a diagnosis, and prescribe medications. However doctors experience can be limited by the cases they are treated so far and medication errors can occur frequently. In addition, it is generally hard and time-consuming inferring information for comparing unstructured data and evaluating similarities between heterogeneous resources. Technologies as Data Mining, Natural Language Processing, and Machine Learning can provide possibilities to explore and exploit potential knowledge from diagnosis history records and help doctors to prescribe medication correctly to decrease medication error effectively. In this paper, we design and implement a medical recommender system that is able to cluster a collection of medical reports on features detected by IBM Watson and Framester, two emerging tools from, respectively, Cognitive Computing and Frame Semantics, and then, giving a medical report from a specific patient as input, to recommend similar other medical reports from patients who had analogues symptoms. Experiments and results have proved the quality of the resulting clustering and recommendations, and the key role that these innovative services can play on the biomedical sector. The proposed system is able to classify new medical cases thus supporting physicians to take more correct and reliable actions about specific diagnosis and cares.


Health recommender systems Data mining Cognitive computation Personal health records Clustering Knowledge inference Personalized medicine Relevance computation Biomedical text-mining 



Danilo Dessì gratefully acknowledges Sardinia Regional Government for the financial support of his PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014–2020—Axis III Education and training, Thematic goal 10, Priority of investment 10ii, Specific goal 10.5).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Danilo Dessì
    • 1
    Email author
  • Diego Reforgiato Recupero
    • 1
  • Gianni Fenu
    • 1
  • Sergio Consoli
    • 2
  1. 1.Mathematics and Computer Science DepartmentUniversity of CagliariCagliariItaly
  2. 2.Philips Research, Data Science DepartmentEindhovenThe Netherlands

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