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14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and Signal Processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Lella, Luigi Gentile, Luana Pristipino, Christian Toni, Danilo ASUR Marche Via Oberdan 2 Ancona Italy Sapienza Univ Dept Human Neurosci Rome Italy ASL1 Roma San Filippo Neri Hosp Rome Italy

ISBN: (纸本)9789897584909

Stroke patients discharge planning is a complex task that could be carried out by the use of a suitable decision support system. Such a platform should be based on unsupervised machine learning algorithms to reach the best results. More specifically, in this kind of prediction task clustering learning algorithms seem to perform better than the other unsupervised models. These algorithms are able to independently subdivide the treated clinical cases into groups, and they can serve to discover interesting correlations among the clinical variables taken into account and to improve the prediction accuracy of the treatment outcome. This work aims to compare the prediction accuracy of a particular clustering learning algorithm, the Growing Neural Gas, with the prediction accuracy of other supervised and unsupervised algorithms used in stroke patients discharge planning. This machine learning model is also able to accurately identify the input space topology. In other words it is characterized by the ability to independently select a subset of attributes to be taken into consideration in order to correctly perform any predictive task.

关键词： pattern recognition and machine learning Big Data in Healthcare Data Mining and Data Analysis Decision Support Systems

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On similarity

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2022年 599卷

作者： Costa, Luciano da F. Univ Sao Paulo Sao Carlos Inst Phys DFCM Av Trab Sao Carlense400 BR-13566590 Sao Carlos SP Brazil

The objective quantification of similarity between two mathematical or physical structures, from scalars to graphs, constitutes a central issue in the physical sciences and technology. In the present work, we develop a principled and systematic approach that adopts the Kronecker delta function of two scalar real values as the prototypical reference for fully strict similarity quantification. We then consider other approaches, namely the cosine similarity, correlation, Sorensen-Dice, and Jaccard indices, and show that they provide successively more strict similarity quantifications. Multiset-based generalizations of these indices to take into account real values are then adopted in order to progressively extend the indices to multisets, vectors, and functions in real spaces. Several important results are reported, including the multiset formulation of similarity indices, as well as the formal derivation of the Jaccard index from the Kronecker delta function. When generalized to real functions, the described similarity indices become respective functionals, which can then be employed to obtain operations analogous to convolution and correlation. Complete application examples involving the recognition of patterns through template matching between two ID functions as well as the identification of multiples instances of objects in 2D scalar fields (images) in presence of noise are also reported which well-illustrate the potential of the proposed concepts and methods. The characterization of the eigenmodes of successive convolutions are also addressed, with interesting results substantiating the enhanced potential of the coincidence index for accurate and stable similarity quantification. (C) 2022 Elsevier B.V. All rights reserved.

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Ensemble quantile classifier

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2020年第0期144卷 106849-000页

作者： Lai, Yuanhao McLeod, Ian Western Univ London ON Canada

Both the median-based classifier and the quantile-based classifier are useful for discriminating high-dimensional data with heavy-tailed or skewed inputs. But these methods are restricted as they assign equal weight to each variable in an unregularized way. The ensemble quantile classifier is a more flexible regularized classifier that provides better performance with high-dimensional data, asymmetric data or when there are many irrelevant extraneous inputs. The improved performance is demonstrated by a simulation study as well as an application to text categorization. It is proven that the estimated parameters of the ensemble quantile classifier consistently estimate the minimal population loss under suitable general model assumptions. It is also shown that the ensemble quantile classifier is Bayes optimal under suitable assumptions with asymmetric Laplace distribution inputs. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Binary classification Extraneous noise variables High-dimensional discriminant analysis pattern recognition and machine learning Sparsity Text mining

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Resolving Differences of Opinion between Medical Experts: A Case Study with the IS-DELPHI System 13

Resolving Differences of Opinion between Medical Experts: A ...

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13th International Joint Conference on Biomedical Engineering Systems and Technologies

作者： Sleeman, Derek Kostadinov, Kiril Moss, Laura Sim, Malcolm Univ Aberdeen Comp Sci Dept Aberdeen AB24 3FX Scotland Univ Glasgow Sch Med Acad Unit Anaesthesia Pain & Crit Care Glasgow G31 2ER Lanark Scotland

ISBN: (纸本)9789897583988

Knowledge intensive clinical systems, as well as machine learning algorithms, have become more widely used over the last decade or so. These systems often need access to sizable labelled datasets which could be more useful if their instances are accurately labelled / annotated. A variety of approaches, including statistical ones, have been used to label instances. In this paper, we discuss the use of domain experts, in this case clinicians, to perform this task. Here we recognize that even highly rated domain experts can have differences of opinion on certain instances;we discuss a system inspired by the Delphi approaches which helps experts resolve their differences of opinion on classification tasks. The focus of this paper is the IS-DELPHI tool which we have implemented to address the labelling issue;we report its use in a medical domain in a study involving 12 Intensive Care Unit clinicians. The several pairs of experts initially disagreed on the classification of 11 instances but as a result of using IS-DELPHI all those disagreements were resolved. From participant feedback (questionnaires), we have concluded that the medical experts understood the task and were comfortable with the functionality provided by IS-DELPHI. We plan to further enhance the system's capabilities and usability, and then use IS-DELPHI, which is a domain independent tool, in a number of further medical domains.

关键词： Expertize Capture Clinical Decision Support Systems Inconsistencies Negotiations DELPHI-approach Refinement Cognitive Informatics Medical Informatics pattern recognition and machine learning

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Predictive AI Models for the Personalized Medicine 12

Predictive AI Models for the Personalized Medicine

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12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 12th International Conference on Health Informatics (HEALTHINF)

作者： Lella, Luigi Licata, Ignazio Minati, Gianfranco Pristipino, Christian De Belvis, Antonio Pastorino, Roberta Reg Hlth Agcy Marche ASUR Marche AN Italy Inst Sci Methodol ISEM Rome PA Italy Italian Assoc Syst Res AIRS Milan MI Italy Italian Assoc Syst Med & Healthcare ASSIMSS Rome Italy Univ Cattolica Sacro Cuore Sect Hyg Inst Publ Hlth Rome Italy

ISBN: (纸本)9789897583537

Innovative information systems which enable personalized medicine are presented. The designed decision support systems are expected to infer with an excellent level of accuracy the outcome of a therapeutic intervention through the analysis of biometric, genetic and environmental data. They are also capable to motivate their predictions according to a dynamic knowledge base, which is kept updated with new analysed cases. These systems can be used by researchers to identify useful correlations between biometric, genetic and environmental data with potential risks and benefits of certain therapeutic choices. They can also be used by the patients to choose the most appropriate therapeutic intervention according to their needs and expectations. In other words the presented decision support tools can realize the vision of the predictive, preventive, personalized and participatory (P4) medicine pursued by the systemic medicine.

关键词： e-Health e-Health Applications pattern recognition and machine learning Decision Support Systems

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On measuring confidence levels using multiple views of feature set for useful unlabeled data selection

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NEUROCOMPUTING 2016年第Part3期173卷 1589-1601页

作者： Thanh-Binh Le Kim, Sang-Woon Myongji Univ Dept Comp Engn Yongin 17058 South Korea

This paper concerns the use of multiple views of a feature set to select a small amount of useful unlabeled data. In the semi-supervised learning (SSL) approach, using a selection strategy, strongly discriminative examples are first selected from unlabeled data and then, together with labeled data, utilized for training a (supervised) classifier or used for re-training the ensemble classifier. In this scenario, the selection strategy plays an important role in improving classification performance. This paper investigates a new selection strategy for a case in which the data are composed of different multiple views: first, multiple views of the data are derived independently;second, each of the views are used to measure corresponding confidence levels with which examples to be selected are evaluated;third, all the confidence levels measured from the multiple views are used as a weighted average to derive the target confidence;this select-and-train process is repeated for a pre-defined number of iterations. The experimental results, obtained using semi-supervised support vector machines for synthetic and real-life benchmark data, demonstrate that the proposed mechanism can compensate for the shortcomings of traditional strategies. In particular, the results demonstrate that when the data is appropriately decomposed into multiple views, this strategy can achieve further improved results in terms of the classification accuracy. (C) 2015 Elsevier B.V. All rights reserved.

关键词： pattern recognition and machine learning Semi-supervised learning Multi-views learning Feature decomposition

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Indoor Localization via Discriminatively Regularized Least Square Classification

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INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2011年第2期18卷 57-72页

作者： Ouyang, Robin Wong, Albert Woo, Kam Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China

In this paper, we address the received signal strength (RSS)-based indoor localization problem in a wireless local area network (WLAN) environment and formulate it as a multi-class classification problem using survey locations as classes. We present a discriminatively regularized least square classifier (DRLSC)-based localization algorithm that is aimed at making use of the class label information to better distinguish the RSS samples taken from different locations after proper transformation. Besides DRLSC, two other regularized least square classifiers (RLSCs) are also presented for comparison. We show that these RLSCs can be expressed in a unified problem formulation with a closed-form solution and convenient assessment of the convexity of the problem. We then extend the linear RLSCs to their nonlinear counterparts via the kernel trick. Moreover, we address the missing value problem, utilize clustering to reduce the training and online complexity, and introduce kernel alignment for fast kernel parameter tuning. Experimental results show that, compared with other methods, the kernel DRLSC-based algorithm achieves superior performance for indoor localization when only a small fraction of the data samples are used.

关键词： Indoor localization Received signal strength (RSS) Wireless local area network (WLAN) pattern recognition and machine learning Discriminatively regularized least square classifier (DRLSC)

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