Expert Opinion Extraction from a Biomedical Database

Samet, Ahmed; Guyet, Thomas; Négrevergne, Benjamin; Dao, Tien Tuan; Hoang, Tuan Nha; Tho, M.C. Ho Ba

doi:10.1007/978-3-319-61581-3_13

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…The possibility of coupling the power of a high-level declaration with an optimized solver allows users to specify how patterns should be and not how they should be computed. There are different existing approaches that blend together the expressiveness and readiness to use of a declarative system within the problem of pattern mining (Järvisalo 2011;Gebser et al 2016;Samet et al 2017;Paramonov et al 2019;Guyet et al 2014;2016). The main objective of this section is to show the growing interest in declarative-based approaches for pattern mining as an alternative to dedicated algorithms, especially when the main focus is to add expressiveness to the standard problem at hand.…”

Section: Declarative-based Approaches For Pattern Miningmentioning

confidence: 99%

“…Frequent patterns are mined following similar encoding principles of Järvisalo (2011), whereas preference-based mining is accomplished by exploiting the asprin system (Brewka et al 2015) which allows expressing combinations of qualitative and quantitative preferences among answer sets. Rare sequential pattern mining is considered in (Samet et al 2017).…”

Section: Declarative-based Approaches For Pattern Miningmentioning

confidence: 99%

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Extended High-Utility Pattern Mining: An Answer Set Programming-Based Framework and Applications

Cauteruccio¹,

Terracina²

2023

Theory and Practice of Logic Programming

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Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High-Utility Pattern Mining; in particular, we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach.

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Section: Declarative-based Approaches For Pattern Miningmentioning

confidence: 99%

Section: Declarative-based Approaches For Pattern Miningmentioning

confidence: 99%

Extended High-Utility Pattern Mining: An Answer Set Programming-Based Framework and Applications

Cauteruccio¹,

Terracina²

2023

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

show abstract

“…The possibility of coupling the power of a high-level declaration with an optimised solver allows users to specify how patterns should be and not how they should be computed. There are different existing approaches that blend together the expressiveness and readiness to use of a declarative system within the problem of pattern mining (Järvisalo 2011;Gebser et al 2016;Samet et al 2017;Paramonov et al 2019;Guyet et al 2014;2016). The main objective of this section is to show the growing interest in declarative-based approaches for pattern mining as an alternative to dedicated algorithms, especially when the main focus is to add expressiveness to the standard problem at hand.…”

Section: Declarative-based Approaches For Pattern Miningmentioning

confidence: 99%

Extended High Utility Pattern Mining: An Answer Set Programming Based Framework and Applications

Cauteruccio¹,

Terracina²

2023

Preprint

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Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High Utility Pattern Mining (HUPM); in particular we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach. Under consideration in Theory and Practice of Logic Programming (TPLP).

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“…However, all the mentioned work can only discover rare association rules built among itemsets, and cannot deal with temporal events and the complex temporal relations between them. Another research direction studies rare sequential patterns [10], [11], [12], [35], [36], [37]. However, rare sequential patterns only consider sequential occurrence between events, and therefore, cannot model other complex relations such as overlapping or containing between temporal events.…”

Section: Related Workmentioning

confidence: 99%

“…However, to find rare temporal patterns, the support has to be set very low, which causes a combinatorial explosion, potentially producing too many patterns that are uninteresting to the user. Existing work proposes solutions to mine rare itemsets [6], [7], [8], [9] and rare sequential patterns [10], [11], [12]. However, they do not consider the temporal aspect of items/events.…”

Section: Introductionmentioning

confidence: 99%

Efficient temporal pattern mining in big time series using mutual information

Pedersen

2021

Proc. VLDB Endow.

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Very large time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in different environments. Significant insights can be gained by mining temporal patterns from these time series. Unlike traditional pattern mining, temporal pattern mining (TPM) adds event time intervals into extracted patterns, making them more expressive at the expense of increased time and space complexities. Existing TPM methods either cannot scale to large datasets, or work only on pre-processed temporal events rather than on time series. This paper presents our Frequent Temporal Pattern Mining from Time Series (FTPMfTS) approach providing: (1) The end-to-end FTPMfTS process taking time series as input and producing frequent temporal patterns as output. (2) The efficient Hierarchical Temporal Pattern Graph Mining (HTPGM) algorithm that uses efficient data structures for fast support and confidence computation, and employs effective pruning techniques for significantly faster mining. (3) An approximate version of HTPGM that uses mutual information, a measure of data correlation, to prune unpromising time series from the search space. (4) An extensive experimental evaluation showing that HTPGM outperforms the baselines in runtime and memory consumption, and can scale to big datasets. The approximate HTPGM is up to two orders of magnitude faster and less memory consuming than the baselines, while retaining high accuracy.

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Expert Opinion Extraction from a Biomedical Database

Cited by 6 publications

References 20 publications

Extended High-Utility Pattern Mining: An Answer Set Programming-Based Framework and Applications

Extended High-Utility Pattern Mining: An Answer Set Programming-Based Framework and Applications

Extended High Utility Pattern Mining: An Answer Set Programming Based Framework and Applications

Efficient temporal pattern mining in big time series using mutual information

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