Artificial Intelligence and Data Mining Techniques in Medicine &amp;#150; Success Stories

Shadabi, Fariba; Sharma, Dharmendra

doi:10.1109/bmei.2008.170

Cited by 6 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this information is not easy to analyse and process and requires the application of special-purpose tools. As illustrated by the success stories described by Shadabi and Sharma [1], data mining techniques have a huge potential for analysing such large volumes of stored medical data in order to discover knowledge. Generally, the extraction of useful, tacit and previously unknown knowledge from large data volumes is a process called Knowledge Discovery in Databases (KDD), which ranges from the understanding and preparation of the data to the interpretation and use of the discovered knowledge (results of the KDD process).…”

Section: Introductionmentioning

confidence: 99%

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Aljawarneh

Anguera

Atwood

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Aljawarneh

Anguera

Atwood

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…Data mining is an interesting option in this respect. As illustrated by the success stories described by Shadabi and Sharma [5] , data mining techniques have a huge potential for analysing such large volumes of stored medical data in order to discover knowledge. Generally, the extraction of useful, tacit and previously unknown knowledge from large data volumes is what is known as knowledge discovery in databases (KDD).…”

Section: Introductionmentioning

confidence: 99%

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

Anguera

Barreiro

Lara

et al. 2016

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

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.1Knowledge 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 the application of different knowledge discovery techniques for the purposes of classification within the above domains. The accuracy of this application for the two classes considered in each case is 99.86% and 98.11% for epilepsy diagnosis in the electroencephalography (EEG) domain and 99.4% and 99.1% for early-age sports talent classification in the stabilometry domain. The KDD techniques achieve better results than other traditional neural network-based classification techniques.

show abstract