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Personalised medicine challenges: quality of data

R. Cruz-Correia,Duarte Ferreira,Gustavo Bacelar,P. Marques,P. Maranhão

Published 2018 in International Journal of Data Science and Analysis

ABSTRACT

Personalised evidence-based-medicine aims to use stored health data to prevent future illnesses. This implies that data should be stored in a readable and understandable form, at least until the death of the person in question. The aim of this paper is to discuss the challenges that arise from the existing pressure to maintain health data in electronic format for many decades. Today clinical databases are filled with heterogeneous data regarding who has collected it, protocols used, detail, precision, and subjectivity. Some data elements are typically more exposed to these problems (e.g. diagnosis) than others (e.g. laboratory results). It is critical that data scientists fully understand how data were collected. Also, it is very important to store context information, protocols used and accuracy/precision information in clinical databases to ensure future understanding of such data.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    International Journal of Data Science and Analysis

  • Publication date

    2018-06-02

  • Fields of study

    Medicine, Computer Science

  • Identifiers
    DOI 10.1007/s41060-018-0127-9
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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