Selected techniques for data mining in medicine

Lavrač, Nada

doi:10.1016/s0933-3657(98)00062-1

Cited by 273 publications

(139 citation statements)

References 36 publications

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“…In many engineering applications it is a very important property, since it allows the transformation of data (information) into (human) knowledge. This problem has recently been faced for classiÿcation problems by Shanahan [25], Fuessel and Isermann [9], Binaghi [2], Sanchez [23], Ishibuchi [12]; in the prediction of process behavior by Maier [18]; in decision making and data mining by Gorzalczany and Piasta [10,15]; in theoretical developments on fuzzy systems by Klement [14]; in intelligent control and robotics by Stoica [26]; in function approximation, by Nauck and Kruse [19]. Jin [13] addresses interpretability by using similarity measures to check the similarity of each rule; the structure and parameters of the fuzzy rules are optimized and interpretability is improved by ÿne-tuning the fuzzy rules with regularization.…”

Section: Introductionmentioning

confidence: 99%

Interpretability and learning in neuro-fuzzy systems

Paiva

Dourado

2004

Fuzzy Sets and Systems

106

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A methodology for the development of linguistically interpretable fuzzy models from data is presented. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the ÿrst phase, the structure of the model is obtained by means of subtractive clustering, which allows the extraction of a set of relevant rules based on a set of representative input-output data samples. In the second phase, the parameters of the model are tuned via the training of a neural network through backpropagation. In order to attain interpretability goals, the method proposed imposes some constraints on the tuning of the parameters and performs membership function merging. In this way, it will be easy to assign linguistic labels to each of the membership functions obtained, after training. Therefore, the model obtained for the system under analysis will be described by a set of linguistic rules, easily interpretable.

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Section: Introductionmentioning

confidence: 99%

Interpretability and learning in neuro-fuzzy systems

Paiva

Dourado

2004

Fuzzy Sets and Systems

106

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“…Hence, based on these criteria's 829,832 attendances reduce to 364,767 records. Furthermore, due to missing NHS Numbers (29,251) and treatment function codes (8,177), these reduce to 327,339 records. …”

Section: Data Preparationmentioning

confidence: 99%

“…With respect to healthcare applications, [8] and [9] presented a selection of data mining techniques that could be suitable for analysing healthcare data. In another case, clustering methods were used to detect similarities of community centres of a Slovenian region in terms of availability and accessibility of public health care resources [10].…”

Section: Introductionmentioning

confidence: 99%

A Decision Support Tool for Health Service Re-design

et al. 2010

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Many of the outpatient services are currently only available in hospitals, however there are plans to provide some of these services alongside with General Practitioners. Consequently, General Practitioners could soon be based at polyclinics. These changes have caused a number of concerns to Hounslow Primary Care Trust (PCT). For example, which of the outpatient services are to be shifted from the hospital to the polyclinic? What are the current and expected future demands for these services? To tackle some of these concerns, the first phase of this project explores the set of specialties that are frequently visited in a sequence (using sequential association rules). The second phase develops an Excel based spreadsheet tool to compute the current and expected future demands for the selected specialties.From the sequential association rule algorithm, endocrinology and ophthalmology were found to be highly associated (i.e. frequently visited in a sequence), which means that these two specialties could easily be shifted from the hospital environment to the polyclinic. We illustrated the Excel based spreadsheet tool for endocrinology and ophthalmology, however, the model is generic enough to cope with other specialties, provided that the data are available.

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“…It has been demonstrated that machine learning algorithms can analyze data from a collection of patients and can be trained to make predictions on new unseen patients. Machine learning algorithms have been used in a variety of medical applications [25] and have been shown to be specially valuable in data mining scenarios involving large databases and where the domain is poorly understood and therefore difficult to model by humans [26]. Intensive care is one of those domains that can benefit from the use of machine learning techniques [27].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis

Loon

Güiza

Meyfroidt³

et al. 2009

J Med Syst

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This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes according to the time they need to reach a stable state after coronary bypass surgery: less or more than 9 h. On the basis of five physiological variables (heart rate, systolic arterial blood pressure, systolic pulmonary pressure, blood temperature and oxygen saturation), different dynamic features were extracted, namely the means and standard deviations at different moments in time, coefficients of multivariate autoregressive models and cepstral coefficients. These sets of features served subsequently as inputs for a Gaussian process and the prediction results were compared with the case where only admission data was used for the classification. The dynamic features, especially the cepstral coefficients (aROC: 0.749, Brier score: 0.206), resulted in higher performances when compared to static admission data (aROC: 0.547, Brier score: 0.247). The differences in performance are shown to be significant. In all cases, the Gaussian process classifier outperformed to logistic regression.

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Selected techniques for data mining in medicine

Cited by 273 publications

References 36 publications

Interpretability and learning in neuro-fuzzy systems

Interpretability and learning in neuro-fuzzy systems

A Decision Support Tool for Health Service Re-design

Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis

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