Searching for new patterns in cardiovascular data

Podgorelec, Vili; Kokol, Peter; Štiglic, Milojka Molan

doi:10.1109/cbms.2002.1011363

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Computational intelligence (CI) techniques are potentially powerful tools for classification and prediction and are attracting increasingly more attention in the field of clinical decision making [19], [24], [31]–[38]. The CI techniques include data mining algorithms and techniques like decision tree (DT) [37], [39], neural networks (NN) [19], support vector machine (SVM) [35], [36], [40], adaptive neuro-fuzzy inference system (ANFIS) [34], [41], etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Periventricular Leukomalacia Occurrence in Neonates After Heart Surgery

Jalali

Buckley

Lynch

et al. 2014

IEEE J. Biomed. Health Inform.

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This paper is concerned with predicting the occurrence of Periventricular Leukomalacia (PVL) using vital and blood gas data which are collected over a period of twelve hours after neonatal cardiac surgery. A data mining approach has been employed to generate a set of rules for classification of subjects as healthy or PVL affected. In view of the fact that blood gas and vital data have different sampling rates, in this study we have divided the data into two categories: (i) high resolution (vital), and (ii) low resolution (blood gas), and designed a separate classifier based on each data category. The developed algorithm is composed of several stages; first, a feature pool has been extracted from each data category and the extracted features have been ranked based on the data reliability and their mutual information content with the output. An optimal feature subset with the highest discriminative capability has been formed using simultaneous maximization of the class separability measure and mutual information of a set. Two separate decision trees (DT) have been developed for the classification purpose and more importantly to discover hidden relationships that exist among the data to help us better understand PVL pathophysiology. The DT result shows that high amplitude twenty minute variations and low sample entropy in the vital data and the defined out of range index as well as maximum rate of change in blood gas data are important factors for PVL prediction. Low sample entropy represents lack of variability in hemodynamic measurement, and constant blood pressure with small fluctuations is an important indicator of PVL occurrence. Finally, using the different time frames of data collection, we show that the first six hours of data contain sufficient information for PVL occurrence prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of Periventricular Leukomalacia Occurrence in Neonates After Heart Surgery

Jalali

Buckley

Lynch

et al. 2014

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…Computational intelligence (CI) techniques attract more and more attention from researchers in the biomedical field as a result of their superior performance in comparison with traditional stochastic approaches in prediction, modeling and classification of biomedical signals [10]–[14]. Data mining facilitates data exploration using data analysis methods with sophisticated algorithms in order to discover unknown patterns.…”

Section: Introductionmentioning

confidence: 99%

Application of decision tree in the prediction of periventricular leukomalacia (PVL) occurrence in neonates after heart surgery

Jalali

Licht

Nataraj

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper is concerned with the prediction of the occurrence of Periventricular Leukomalacia (PVL) that occurs in neonates after heart surgery. The data which is collected over a period of 12 hours after the cardiac surgery contains vital measurements as well as blood gas measurements with different resolutions. The decision tree classification technique has been selected as a tool for prediction of the PVL because of its capacity for discovering rules and novel associations in the data. Vital data measured using near-inferred spectroscopy (NIRS) at the sampling rate of 0.25 Hz and blood gas measurement up to 12 times with irregular time intervals for 35 patients collected from Children's Hospital of Philadelphia (CHOP) are used for this study. Vital data contain heart rate (HR), mean arterial pressure (MAP), right atrium pressure (RAP), blood hemoglobin (Hb), hemoglobin oxygen content (HbO2), oxygen saturation (SpO2) and relative cerebral blood flow (rCBF). Features derived from the data include statistical moments (mean, variance, skewness and kurtosis), trend and min and max of the vital data and rate of change, time weighted mean and a custom defined out of range index (ORI) for the blood gas data. A decision tree is developed for the vital data in order to identify the most important vital measurements. In addition, a decision tree is developed for blood gas data to find important factors for the prediction of PVL occurrence. Results show that in blood gas data, maximum rate of change in the concentration of bicarbonate ions in blood (HCO3) and minimum rate of change in the partial pressure of dissolved CO2 in the blood (PaCO2) are the most important factors for prediction of the PVL. Among vital features the kurtosis of HR and Hb are the most important parameters.

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“…Thus, the use of machine learning techniques (Witten & Frank, 2005) to discover valid, novel, interesting and comprehensible patterns could be the key to success in either the business or scientific environment. We find a clear example of this in the medical diagnosis/prognosis domain (Kukar & Groselj, 2000; Breault et al , 2002; Podgorelec et al , 2002), where identifying patterns that help to predict the incidence of any kind of disease may represent the opportunity to react on time to avoid, delay or diminish the consequences of exposure of predispositions.…”

Section: Introductionmentioning

confidence: 99%

Extracting new patterns for cardiovascular disease prognosis

2009

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Cardiovascular diseases constitute one of the main causes of mortality in the world, and machine learning has become a powerful tool for analysing medical data in the last few years. In this paper we present an interdisciplinary work based on an ambulatory blood pressure study and the development of a new classification algorithm named REMED. We focused on the discovery of new patterns for abnormal blood pressure variability as a possible cardiovascular risk factor. We compared our results with other classification algorithms based on Bayesian methods, decision trees, and rule induction techniques. In the comparison, REMED showed similar accuracy to these algorithms but it has the advantage of being superior in its capacity to classify sick people correctly. Therefore, our method could represent an innovative approach that might be useful in medical decision support for cardiovascular disease prognosis.

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Searching for new patterns in cardiovascular data

Cited by 4 publications

References 2 publications

Prediction of Periventricular Leukomalacia Occurrence in Neonates After Heart Surgery

Prediction of Periventricular Leukomalacia Occurrence in Neonates After Heart Surgery

Application of decision tree in the prediction of periventricular leukomalacia (PVL) occurrence in neonates after heart surgery

Extracting new patterns for cardiovascular disease prognosis

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