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Blockchain Technology for Smart Cities pp 155–180Cite as

An Architecture for e-Health Recommender Systems Based on Similarity of Patients’ Symptoms

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Part of the book series: Blockchain Technologies ((BT))

Abstract

Nowadays, data are generated both by users and other systems deriving new data from the previous ones for supporting decision making. The Electronic Health Records contains from structured data (e.g. hospital id, etc.), semi-structured data (e.g. a Health Level Seven-based records), to unstructured data (e.g. patient’s symptoms). The big challenge with health in smart cities is associated with the prevention, both the business and human health point of view. That is to say, avoid the propagation of certain diseases’ patterns is the best option no just for people, but also from the city’s health and the local economy. Thus, an architecture able to integrate into an Organizational Memory the medical data coming from heterogeneous repositories with the aim of gathering different kinds of symptoms is introduced. The query in the architecture is understood such as an unstructured text (i.e. symptoms) or an electronic health record. In this sense, the architecture is able to reach similar cases from the organizational memory based on a textual similarity analysis for limiting the search space. Next, using the International Classification of Diseases is possible to convert a case to a vector model representation in order to compute metric distances and get other cases order by a level of similarity. Each query answer contains a set of recommendations based on the frequency of diagnoses related to similar cases are given in order to share previous experiences. The processes point of view related to architecture is outlined. Finally, some conclusions and future works are outlined.

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Notes

  1. 1.

    https://www.who.int/classifications/icd/en/.

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Authors and Affiliations

  1. National Technological University, Maestro López and Cruz Roja Argentina, 5000, Córdoba, Argentina

    Valerio Frittelli

  2. National University of La Pampa, C. Gil 353. 1st Floor, 6300, Santa Rosa, Argentina

    Mario José Diván

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  1. Valerio Frittelli
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  2. Mario José Diván
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Correspondence to Mario José Diván .

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Editors and Affiliations

  1. ReSENSE Lab, Department of Electronics Engineering, Hankuk University of Foreign Studies (HUFS), Yongin-si, Korea (Republic of)

    Dhananjay Singh

  2. Department of Electronics Engineering, Indian Institute of Technology (IIT) BHU, Varanasi, India

    Navin Singh Rajput

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Frittelli, V., Diván, M.J. (2020). An Architecture for e-Health Recommender Systems Based on Similarity of Patients’ Symptoms. In: Singh, D., Rajput, N. (eds) Blockchain Technology for Smart Cities. Blockchain Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-15-2205-5_8

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  • DOI: https://doi.org/10.1007/978-981-15-2205-5_8

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