Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry

Publication date: Available online 18 May 2016 Source:Computational and Structural Biotechnology Journal Author(s): A. Anguera, J.M. Barreiro, J.A. Lara, D. Lizcano One of the major challenges in the medical domain today is how to exploit the huge amount of data that this field generates. To do this, approaches are required that are capable of discovering knowledge that is useful for decision making in the medical field. Time series are data types that are common in the medical domain and require specialized analysis techniques and tools, especially if the information of interest to specialists is concentrated within particular time series regions, known as events. This research followed the steps specified by the so-called knowledge discovery in databases (KDD) process to discover knowledge from medical time series derived from stabilometric (396 series) and electroencephalographic (200) patient electronic health records (EHR). The view offered in the paper is based on the experience gathered as part of the VIIP project. 1 1 This work was partially supported by the Spanish Ministry of Education and Science as part of the 2004–2007 National R&D&I Plan through the VIIP Project (DEP2005–00,232-C03). Knowledge discovery in medical time series has a number of difficulties and implications that are highlighted by illustrating the application of several techniques that cover the entire KDD process through two case studies. This paper illustrates ...
Source: Computational and Structural Biotechnology Journal - Category: Biotechnology Source Type: research