Particularities of data mining in medicine: lessons learned from patient medical time series data analysis
| Autor: | J.W. Atwood, Juan A. Lara, Aurea Anguera, Shadi Aljawarneh, David Lizcano |
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| Rok vydání: | 2019 |
| Předmět: |
Computer Networks and Communications
Computer science Process (engineering) KDD lcsh:TK7800-8360 02 engineering and technology Stabilometry computer.software_genre Physiological signals lcsh:Telecommunication Domain (software engineering) Task (project management) 03 medical and health sciences 0302 clinical medicine Knowledge extraction lcsh:TK5101-6720 Medical data mining 0202 electrical engineering electronic engineering information engineering EEG Data mining Time series data analysis Sensors lcsh:Electronics Lessons learned Computer Science Applications Signal Processing 020201 artificial intelligence & image processing computer 030217 neurology & neurosurgery |
| Zdroj: | udiMundus: Repositorio Institucional de la Universidad a Distancia de Madrid Universidad a Distancia de Madrid (UDIMA) EURASIP Journal on Wireless Communications and Networking, Vol 2019, Iss 1, Pp 1-29 (2019) udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid instname |
| Popis: | Nowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects. |
| Databáze: | OpenAIRE |
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