JTSA: An open source framework for time series abstractions

Sacchi, Lucia; Capozzi, Davide; Bellazzi, Riccardo; Larizza, Cristiana

doi:10.1016/j.cmpb.2015.05.006

Cited by 12 publications

(8 citation statements)

References 27 publications

(39 reference statements)

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“…As described in our previous work [29], AID-GM is integrated with the Java Time Series Abstractor (JTSA) library [33], a framework recently developed at the Department of Electrical, Computer and Biomedical Engineering of the University of Pavia, Italy, by some of the authors of this paper. JTSA extracts qualitative patterns from time series of measurements using Temporal Abstractions (TA) [34][35][36][37][38].…”

Section: Analytics Modulementioning

confidence: 99%

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Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform

Salvi

Bosoni

Tibollo

et al. 2019

Sensors

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Diabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient’s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject’s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.

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Section: Analytics Modulementioning

confidence: 99%

“…The algorithms for pattern detection were tested and validated on simulated data sets in our previous works [33,39,40].…”

Section: Analytics Modulementioning

confidence: 99%

Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform

Salvi

Bosoni

Tibollo

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Temporal patterns detection can be particularly useful for a variety of medical analyses, such as data exploration and summarization, temporal reasoning, evaluation of the response to specific treatments, anomaly detection, and prediction of clinical outcomes. Temporal patterns can be extracted in different ways and several methodologies have been proposed in the literature to achieve this goal [131]. Sacchi et al [131] proposed JTSA (Java Time Series Abstractor), a Java-based framework providing a library of algorithms for Temporal Abstraction detection, which can be easily extended and integrated into other applications.…”

Section: Temporal Processing In the Clinical Domainmentioning

confidence: 99%

“…Temporal patterns can be extracted in different ways and several methodologies have been proposed in the literature to achieve this goal [131]. Sacchi et al [131] proposed JTSA (Java Time Series Abstractor), a Java-based framework providing a library of algorithms for Temporal Abstraction detection, which can be easily extended and integrated into other applications. JTSA can be used both as a standalone tool for data summarization and as a module to be embedded into a complex architecture to select specific phenotypes based on TAs in a large dataset.…”

Section: Temporal Processing In the Clinical Domainmentioning

confidence: 99%

“…The ASTPminer allows to search for frequent temporal patterns by considering a pattern to be a temporal arrangement between a set of event types that satisfies some similarity criteria through different occurrences. Despite the relevance of TA as a methodology for temporal reasoning, only few efforts have been made to create a framework that is general, easy to use and simple to integrate into other applications [131]. Conceptual graphs (CGs) are used to represent clinical guidelines because they support visual reasoning with a logical background, making them a potentially valuable representation for guidelines [133].…”

Section: Temporal Processing In the Clinical Domainmentioning

confidence: 99%

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Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain

Madkour

Benhaddou

Tao

2016

Computer Methods and Programs in Biomedicine

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Background and Objective We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic Health Records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives. Methods This review surveys the methods used in three important area: modeling and representing of time, Medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations. Results the main findings of this review is revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations. Conclusions Extracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.

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NONCADO: A System to Prevent Falls by Encouraging Healthy Habits in Elderly People

Salvi

Panzarasa

Bagarotti

et al. 2019

Artificial Intelligence in Medicine

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JTSA: An open source framework for time series abstractions

Cited by 12 publications

References 27 publications

Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform

Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain

NONCADO: A System to Prevent Falls by Encouraging Healthy Habits in Elderly People

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