Open AccessBig Data and machine learning in medicine: the main problems
Author(s) -
Svetlana P. Shchelykalina,
К. В. Киселев,
Т. В. Зарубина
Publication year - 2018
Publication title -
kalpa publications in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 2515-1762
DOI - 10.29007/vvr8
Subject(s) - big data , data science , computer science , comparability , health informatics , interoperability , informatics , health care , world wide web , data mining , mathematics , engineering , combinatorics , electrical engineering , economics , economic growth
Big data and deep learning technologies play an important role in the modern scientific world. The tendency to work with huge data sets is now conquering the medical area. In this article, based on the experience of the Department of medical cybernetics and informatics of the RNRMU Medical and biological Faculty, we explain the main issues that re- searchers deal with in collection and processing of medical data. We explain that problems may relate to data sources issues, semantic interoperability, data relevance, multidimensionality, completeness, and comparability. Modern digital health records and their services like EHR nowadays cannot provide necessary “Big Data” information. The healthcare system makes it impossible to collect relevant big data sets in a short period. Further issues are certain irresponsibility of doctors and patients; their truthfulness about facts happened in reality and the difference between these facts and what is written in a medical record. This often leads to incorrect and incomplete data sets in medical information systems. We conclude by stating that “Big Data” in medicine today cannot be “Big” as in other scientific areas. Re- searchers should try to collect relevant, truthful, and complete information in observable amount and time and perform their studies.