Reliable Probabilistic Prediction for Medical Decision Support

Papadopoulos, Harris

doi:10.1007/978-3-642-23960-1_32

Cited by 15 publications

(10 citation statements)

References 16 publications

(18 reference statements)

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“…Since then VPs have been developed based on k-Nearest Neighbours [4], Nearest Centroid [3] and Neural Networks [9]. Furthermore, a VP based on a binary SVM has been developed in [17], and has been compared with Platt's method in the batch setting.…”

Section: Venn Predictionmentioning

confidence: 99%

Reliable Probability Estimates Based on Support Vector Machines for Large Multiclass Datasets

Lambrou

Papadopoulos

Nouretdinov

et al. 2012

IFIP Advances in Information and Communication Technology

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Abstract. Venn Predictors (VPs) are machine learning algorithms that can provide well calibrated multiprobability outputs for their predictions. The only drawback of Venn Predictors is their computational inefficiency, especially in the case of large datasets. In this work, we propose an Inductive Venn Predictor (IVP) which overcomes the computational inefficiency problem of the original Venn Prediction framework. Each VP is defined by a taxonomy which separates the data into categories. We develop an IVP with a taxonomy derived from a multiclass Support Vector Machine (SVM), and we compare our method with other probabilistic methods for SVMs, namely Platt's method, SVM Binning, and SVM with Isotonic Regression. We show that these methods do not always provide well calibrated outputs, while our IVP will always guarantee this property under the i.i.d. assumption.

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Section: Venn Predictionmentioning

confidence: 99%

Reliable Probability Estimates Based on Support Vector Machines for Large Multiclass Datasets

Lambrou

Papadopoulos

Nouretdinov

et al. 2012

IFIP Advances in Information and Communication Technology

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“…The Venn Prediction framework was also introduced in [1] where the interested reader can find a thorough description. Since then, VPs have been developed based on k-Nearest Neighbours [17], Nearest Centroid [18] and Neural Networks [19], [20]. Furthermore, a VP based on a binary SVM has been developed in [21], where it has been compared with Platt's method in the batch setting.…”

Section: Introductionmentioning

confidence: 99%

Calibrated probabilistic predictions for biomedical applications

Lambrou

Papadopoulos

Gammerman

2012

2012 IEEE 12th International Conference on Bioinformatics &Amp; Bioengineering (BIBE)

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Venn Prediction (VP) is a machine learning framework that can be used to develop methods that provide wellcalibrated probabilistic outputs. Unlike other probabilistic methods, the VP framework guarantees validity under the assumption that the data are independently and identically distributed (i.i.d.). Well-calibrated probabilistic outputs are of great importance, especially in biomedical applications. In this work, we develop a new Venn Predictor based on the Sequential Minimal Optimisation (SMO) algorithm and we examine its application to two real-world biomedical problems. We demonstrate in our results that our method can provide calibrated probabilistic outputs for predictions without any loss of accuracy. Moreover, we compare the outputs of our method with the probability outputs of SMO with logistic regression.

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“…Practically any known machine learning algorithm can be used as an underlying algorithm in this framework (such as Neural Network in [11] and SVM in [12,13]). In this work we used logistic regression as an underlying algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multiprobabilistic prediction in early medical diagnoses

Nouretdinov

Devetyarov

Vovk

et al. 2013

Ann Math Artif Intell

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This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. Is allows to output a valid probability interval. The methodology is designed for mass spectrometry data. For demonstrative purposes, we applied this methodology to MALDI-TOF data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer and breast cancer. The experiments showed that probability intervals are narrow, that is, the output of the multiprobability predictor is similar to a single probability distribution. In addition, probability intervals produced for heart disease and ovarian cancer data were more accurate than the output of corresponding probability predictor. When Venn machines were forced to make point predictions, the accuracy of such predictions is for the most data better than the accuracy of the underlying algorithm that outputs single probability distribution of a label. Application of this methodology to MALDI-TOF data sets empirically demonstrates the validity. The accuracy of the proposed method on ovarian cancer data rises from 66.7%11 months in advance of the moment of diagnosis to up to 90.2 % at the moment of diagnosis. The same approach has been applied to heart disease data without time dependency, although the achieved accuracy was not as high (up to 69.9 %). The methodology allowed us to confirm mass spectrometry peaks previously identified as carrying statistically significant information for discrimination between controls and cases.

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Reliable Probabilistic Prediction for Medical Decision Support

Cited by 15 publications

References 16 publications

Reliable Probability Estimates Based on Support Vector Machines for Large Multiclass Datasets

Reliable Probability Estimates Based on Support Vector Machines for Large Multiclass Datasets

Calibrated probabilistic predictions for biomedical applications

Multiprobabilistic prediction in early medical diagnoses

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