Tensor based representation and analysis of the electronic healthcare record data

Cyganek, Bogusław; Woźniak, Michał

doi:10.1109/bibm.2015.7359880

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Machine learning techniques are widely applied for medical tasks, see e.g. (Cyganek and Wozniak, 2015), (Grana et al, 2011) and (Froelich et al, 2015). As we formalize the task of automated estimation of UPDRS score as a regression task, when reviewing related works, we focus on regression, which is one of the most prominent fields of machine learning with various applications in medicine, see e.g.…”

Section: Related Workmentioning

confidence: 99%

ParkinsoNET: Estimation of UPDRS Score Using Hubness-Aware Feedforward Neural Networks

Búza

Varga

2016

Applied Artificial Intelligence

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Parkinson's disease is a worldwide frequent neurodegenerative disorder with increasing incidence. Speech disturbance appears during the progression of the disease. UPDRS is a gold standard tool for diagnostic and follow up of the disease. We aim at estimating the UPDRS score based on biomedical voice recordings. In this paper, we study the hubness phenomenon in context of the UPDRS score estimation and propose hubness-aware error correction for feed-forward neural networks in order to increase the accuracy of estimation. We perform experiments on publicly available datasets derived form real voice data and show that the proposed technique systematically increases the accuracy of various feed-forward neural networks.

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Section: Related Workmentioning

confidence: 99%

ParkinsoNET: Estimation of UPDRS Score Using Hubness-Aware Feedforward Neural Networks

Búza

Varga

2016

Applied Artificial Intelligence

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“…Examples include network traffic data (source IP -destination IP -time), movie rating data (user -movie -time), IoT sensor data, and healthcare data. Finding patterns and anomalies in those tensor data is a very important problem with many applications such as building safety monitoring [10], patient health monitoring [5,13,15,22], cyber security [19], terrorist detection [1,2,14], and fake user detection in social networks [4,11]. Tensor decomposition method, a widely-used tool in tensor analysis, has been used for this task.…”

Section: Introductionmentioning

confidence: 99%

CTD: Fast, accurate, and interpretable method for static and dynamic tensor decompositions

2018

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How can we find patterns and anomalies in a tensor, i.e., multi-dimensional array, in an efficient and directly interpretable way? How can we do this in an online environment, where a new tensor arrives at each time step? Finding patterns and anomalies in multi-dimensional data have many important applications, including building safety monitoring, health monitoring, cyber security, terrorist detection, and fake user detection in social networks. Standard tensor decomposition results are not directly interpretable and few methods that propose to increase interpretability need to be made faster, more memory efficient, and more accurate for large and quickly generated data in the online environment. We propose two versions of a fast, accurate, and directly interpretable tensor decomposition method we call CTD that is based on efficient sampling method. First is the static version of CTD, i.e., CTD-S, that provably guarantees up to 11× higher accuracy than that of the state-of-the-art method. Also, CTD-S is made up to 2.3× faster and up to 24× more memory-efficient than the state-of-the-art method by removing redundancy. Second is the dynamic version of CTD, i.e. CTD-D, which is the first interpretable dynamic tensor decomposition method ever proposed. It is also made up to 82× faster than the already fast CTD-S by exploiting factors at previous time step and by reordering operations. With CTD, we demonstrate how the results can be effectively interpreted in online distributed denial of service (DDoS) attack detection and online troll detection.

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Classifying Functional Data from Orthogonal Projections – Model, Properties and Fast Implementation

Skubalska-Rafajłowicz

Rafajłowicz

2021

Computational Science – ICCS 2021

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Tensor based representation and analysis of the electronic healthcare record data

Cited by 4 publications

References 19 publications

ParkinsoNET: Estimation of UPDRS Score Using Hubness-Aware Feedforward Neural Networks

ParkinsoNET: Estimation of UPDRS Score Using Hubness-Aware Feedforward Neural Networks

CTD: Fast, accurate, and interpretable method for static and dynamic tensor decompositions

Classifying Functional Data from Orthogonal Projections – Model, Properties and Fast Implementation

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