The Experts below are selected from a list of 11376 Experts worldwide ranked by ideXlab platform
Yuval Shahar - One of the best experts on this subject based on the ideXlab platform.
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Classification of multivariate time series via Temporal Abstraction and time intervals mining
Knowledge and Information Systems, 2015Co-Authors: Robert Moskovitch, Yuval ShaharAbstract:Classification of multivariate time series data, often including both time points and intervals at variable frequencies, is a challenging task. We introduce the KarmaLegoSification (KLS) framework for classification of multivariate time series analysis, which implements three phases: (1) application of a Temporal Abstraction process that transforms a series of raw time-stamped data points into a series of symbolic time intervals; (2) mining these symbolic time intervals to discover frequent time-interval-related patterns (TIRPs), using Allen’s Temporal relations; and (3) using the TIRPs as features to induce a classifier. To efficiently detect multiple TIRPs (features) in a single entity to be classified, we introduce a new algorithm, SingleKarmaLego, which can be shown to be superior for that purpose over a Sequential TIRPs Detection algorithm. We evaluated the KLS framework on datasets in the domains of diabetes, intensive care, and infectious hepatitis, assessing the effects of the various settings of the KLS framework. Discretization using Symbolic Aggregate approXimation (SAX) led to better performance than using the equal-width discretization (EWD); knowledge-based cut-off definitions when available were superior to both. Using three abstract Temporal relations was superior to using the seven core Temporal relations. Using an epsilon value larger than zero tended to result in a slightly better accuracy when using the SAX discretization method, but resulted in a reduced accuracy when using EWD, and overall, does not seem beneficial. No feature selection method we tried proved useful. Regarding feature (TIRP) representation, mean duration performed better than horizontal support, which in turn performed better than the default Binary (existence) representation method.
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A distributed architecture for efficient parallelization and computation of knowledge-based Temporal Abstractions
Journal of Intelligent Information Systems, 2011Co-Authors: Asaf Shabtai, Yuval Shahar, Yuval EloviciAbstract:Today, data storage capabilities as well as computational power are rapidly increasing. On the one hand, this improvement makes it possible to generate and store a great amount of Temporal (time-oriented) data for future query, analysis and discovery of new knowledge. On the other hand, systems and experts are encountering new problems in processing this increased amount of data. The rapid growth in stored time-oriented data necessitates the development of new methods for handling, processing, and interpreting large amounts of Temporal data. One approach is to use an automatic summarization process based on predefined knowledge, such the Knowledge-Based Temporal-Abstraction (KBTA) method. This method enables one to summarize and reduce the amount of raw data by creating higher level interpretations based on predefined domain knowledge. Unfortunately, the task of Temporal Abstraction is inherently computationally expensive, especially when an enormous volume of multivariate data has to be handled and when complex patterns need to be considered. In this research, we address the scalability problem of a Temporal-Abstraction task that involves processing significantly large amounts of raw data. We propose a new computational framework, the Distributed KBTA (DKBTA), which efficiently distributes the Abstraction process among several parallel computational nodes, in order to achieve an acceptable computation time. The DKBTA framework distributes the Temporal-Abstraction process along one or more computational axes, each of which enables parallelization of one or more Temporal-Abstraction tasks into which the main Temporal-Abstraction task is decomposed, such as by different subject groups, concepts types, or Abstraction types. We have implemented the DKBTA framework and have evaluated it in a preliminary fashion in the medical and the information security domains, with encouraging results. In our small-scale evaluation, only distribution along the subjects axis and sometimes along the concept-type axis seemed to consistently enhance performance, and only for computations involving individual subjects and not functions of sets of subjects; but this observation might depend on the number of processing units. Additionally, since the communication between the processing units was based on the TCP protocol, we could not observe any speedup even when using two processing units on the same machine. In our further evaluations we plan to use a shared memory architecture in order to exchange data between processing units.
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Abstraction of Time-Oriented Clinical Data
Temporal Information Systems in Medicine, 2010Co-Authors: Carlo Combi, Elpida Keravnou-papailiou, Yuval ShaharAbstract:The chapter aims to give a comprehensive and critical review of current approaches to the common task of Abstraction of time-oriented data in medicine, or Temporal Abstraction. Temporal-data Abstraction constitutes a central requirement that presently receives much and justifiable attention. The role of this process is especially crucial in the context of time-oriented clinical monitoring, therapy planning, and exploration of clinical databases. General theories of time typically used in artificial intelligence do not fully address the requirements for Temporal Abstraction in medical reasoning (see Chapter 2).
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medical Temporal knowledge discovery via Temporal Abstraction
American Medical Informatics Association Annual Symposium, 2009Co-Authors: Robert Moskovitch, Yuval ShaharAbstract:Medical knowledge includes frequently occurring Temporal patterns in longitudinal patient records. These patterns are not easily detectable by human clinicians. Current knowledge could be extended by automated Temporal data mining. However, multivariate time-oriented data are often present at various levels of Abstraction and at multiple Temporal granularities, requiring a transformation into a more abstract, yet uniform dimension suitable for mining. Temporal Abstraction (of both the time and value dimensions) can transform multiple types of point-based data into a meaningful, time-interval-based data representation, in which significant, interval-based Temporal patterns can be discovered. We introduce a modular, fast time-interval mining method, KarmaLego, which exploits the transitivity inherent in Temporal relations. We demonstrate the usefulness of KarmaLego in finding meaningful Temporal patterns within a set of records of diabetic patients; several patterns seem to have a different frequency depending on gender. We also suggest additional uses of the discovered patterns for Temporal clustering of the mined population and for classifying multivariate time series.
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AMIA - Medical Temporal-knowledge discovery via Temporal Abstraction.
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2009Co-Authors: Robert Moskovitch, Yuval ShaharAbstract:Medical knowledge includes frequently occurring Temporal patterns in longitudinal patient records. These patterns are not easily detectable by human clinicians. Current knowledge could be extended by automated Temporal data mining. However, multivariate time-oriented data are often present at various levels of Abstraction and at multiple Temporal granularities, requiring a transformation into a more abstract, yet uniform dimension suitable for mining. Temporal Abstraction (of both the time and value dimensions) can transform multiple types of point-based data into a meaningful, time-interval-based data representation, in which significant, interval-based Temporal patterns can be discovered. We introduce a modular, fast time-interval mining method, KarmaLego, which exploits the transitivity inherent in Temporal relations. We demonstrate the usefulness of KarmaLego in finding meaningful Temporal patterns within a set of records of diabetic patients; several patterns seem to have a different frequency depending on gender. We also suggest additional uses of the discovered patterns for Temporal clustering of the mined population and for classifying multivariate time series.
Mark A. Musen - One of the best experts on this subject based on the ideXlab platform.
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AMIA - Representation of Temporal indeterminacy in clinical databases.
Proceedings. AMIA Symposium, 2000Co-Authors: Martin J. O'connor, Mark A. MusenAbstract:Temporal indeterminancy is common in clinical medicine because the time of many clinical events is frequently not precisely known. Decision support systems that reason with clinical data may need to deal with this indeterminancy. This indeterminacy support must have a sound foundational model so that other system components may take advantage of it. In particular, it should operate in concert with Temporal Abstraction, a feature that is crucial in several clinical decision support systems that our group has developed. We have implemented a Temporal query system called Tzolkin that provides extensive support for the Temporal indeterminancies found in clinical medicine, and have integrated this support with our Temporal Abstraction mechanism. The resulting system provides a simple, yet powerful approach for dealing with Temporal indeterminancy and Temporal Abstraction.
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Semi-automated entry of clinical Temporal-Abstraction knowledge.
Journal of the American Medical Informatics Association : JAMIA, 1999Co-Authors: Yuval Shahar, Lawrence V. Basso, Herbert Kaizer, Darrell M. Wilson, Hai Chen, Daniel P. Stites, Mark A. MusenAbstract:RESUME Protege ´, a general framework and set of tools for the construction of knowledge-based systems. The usability of the KA tool was evaluated by three expert physicians and three knowledge engineers in three domains—the monitoring of children's growth, the care of patients with diabetes, and protocol-based care in oncology and in experimental therapy for AIDS. The study evaluated the usability of the KA tool for the entry of previously elicited knowledge. Measurements: The authors recorded the time required to understand the methodology and the KA tool and to enter the knowledge; they examined the subjects' qualitative comments; and they compared the output Abstractions with benchmark Abstractions computed from the same data and a version of the same knowledge entered manually by
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Knowledge-based Temporal Abstraction in clinical domains
Artificial Intelligence in Medicine, 1996Co-Authors: Yuval Shahar, Mark A. MusenAbstract:We have defined a knowledge-based framework for the creation of abstract, interval-based concepts from time-stamped clinical data, the knowledge-based Temporal-Abstraction (KBTA) method. The KBTA method decomposes its task into five subtasks; for each subtask we propose a formal solving mechanism. Our framework emphasizes explicit representation of knowledge required for Abstraction of time-oriented clinical data, and facilitates its acquisition, maintenance, reuse and sharing. The RESUME system implements the KBTA method. We tested RESUME in several clinical-monitoring domains, including the domain of monitoring patients who have insulin-dependent diabetes. We acquired from a diabetes-therapy expert diabetes-therapy Temporal-Abstraction knowledge. Two diabetes-therapy experts (including the first one) created Temporal Abstractions from about 800 points of diabetic-patients' data. RESUME generated about 80% of the Abstractions agreed by both experts; about 97% of the generated Abstractions were valid. We discuss the advantages and limitations of the current architecture.
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Knowledge acquisition for Temporal Abstraction.
Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium, 1996Co-Authors: A. Stein, Mark A. Musen, Yuval ShaharAbstract:Temporal Abstraction is the task of detecting relevant patterns in data over time. The knowledge-based Temporal-Abstraction method uses knowledge about a clinical domain's contexts, external events, and parameters to create meaningful interval-based Abstractions from raw time-stamped clinical data. In this paper, we describe the acquisition and maintenance of domain-specific Temporal-Abstraction knowledge. Using the PROTEGE-II framework, we have designed a graphical tool for acquiring Temporal knowledge directly from expert physicians, maintaining the knowledge in a sharable form, and converting the knowledge into a suitable format for use by an appropriate problem-solving method. In initial tests, the tool offered significant gains in our ability to rapidly acquire Temporal knowledge and to use that knowledge to perform automated Temporal reasoning.
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Knowledge-based Temporal Abstraction in diabetes therapy.
Medinfo. MEDINFO, 1995Co-Authors: Yuval Shahar, Amar K. Das, Fredric B. Kraemer, Lawrence V. Basso, Mark A. MusenAbstract:We suggest a general framework for solving the task of creating abstract, interval-based concepts from time-stamped clinical data. We refer to this problem-solving framework as the knowledge-based Temporal-Abstraction (KBTA) method. The KBTA method emphasizes explicit representation, acquisition, maintenance, reuse, and the sharing of knowledge required for Abstraction of time-oriented clinical data. We describe the subtasks into which the KBTA method decomposes its task, the problem-solving mechanisms that solve these subtasks, and the knowledge necessary for instantiating these mechanisms in a particular clinical domain. We have implemented the KBTA method in the RESUME system and have applied it to the task of monitoring the care of insulin-dependent diabetics.
Daniel Capurro - One of the best experts on this subject based on the ideXlab platform.
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Business Process Management Workshops - Characterization of Drug Use Patterns Using Process Mining and Temporal Abstraction Digital Phenotyping
Business Process Management Workshops, 2019Co-Authors: Eric Rojas, Daniel CapurroAbstract:Understanding and identifying executed patterns, activities and processes for patients of different characteristics provides medical experts a deep understanding of which tasks are critical in the provided care, and may help identify ways to improve them. However, extracting these events and data for patients with complex clinical phenotypes is not a trivial task. This paper provides an approach to identifying specific patient cohorts based on complex digital phenotypes as a starting point to apply process mining tools and techniques and identify patterns or process models. Using Temporal Abstraction-based digital phenotyping and pattern matching, we identified a cohort of patients with sepsis from the MIMIC II database, and then apply process mining techniques to discover medication use patterns. In the case study we present, the use of Temporal Abstraction digital phenotyping helped us discover a relevant patient cohort, aiding in the extraction of the data required to generate drug use patterns for medications of different types such as vasopressors, vasodilators and systemic antibacterial antibiotics. For sepsis patients, combining the use of Temporal Abstraction digital phenotyping and process mining tools and techniques, was proven to help extract accurate cohorts of patients for health care process mining.
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characterization of drug use patterns using process mining and Temporal Abstraction digital phenotyping
Lecture Notes in Business Information Processing, 2019Co-Authors: Eric Rojas, Daniel CapurroAbstract:© 2019, Springer Nature Switzerland AG. Understanding and identifying executed patterns, activities and processes for patients of different characteristics provides medical experts a deep understanding of which tasks are critical in the provided care, and may help identify ways to improve them. However, extracting these events and data for patients with complex clinical phenotypes is not a trivial task. This paper provides an approach to identifying specific patient cohorts based on complex digital phenotypes as a starting point to apply process mining tools and techniques and identify patterns or process models. Using Temporal Abstraction-based digital phenotyping and pattern matching, we identified a cohort of patients with sepsis from the MIMIC II database, and then apply process mining techniques to discover medication use patterns. In the case study we present, the use of Temporal Abstraction digital phenotyping helped us discover a relevant patient cohort, aiding in the extraction of the data required to generate drug use patterns for medications of different types such as vasopressors, vasodilators and systemic antibacterial antibiotics. For sepsis patients, combining the use of Temporal Abstraction digital phenotyping and process mining tools and techniques, was proven to help extract accurate cohorts of patients for health care process mining.
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characterization of drug use patterns using process mining and Temporal Abstraction digital phenotyping
Business Process Management, 2018Co-Authors: Eric Rojas, Daniel CapurroAbstract:Understanding and identifying executed patterns, activities and processes for patients of different characteristics provides medical experts a deep understanding of which tasks are critical in the provided care, and may help identify ways to improve them. However, extracting these events and data for patients with complex clinical phenotypes is not a trivial task. This paper provides an approach to identifying specific patient cohorts based on complex digital phenotypes as a starting point to apply process mining tools and techniques and identify patterns or process models. Using Temporal Abstraction-based digital phenotyping and pattern matching, we identified a cohort of patients with sepsis from the MIMIC II database, and then apply process mining techniques to discover medication use patterns. In the case study we present, the use of Temporal Abstraction digital phenotyping helped us discover a relevant patient cohort, aiding in the extraction of the data required to generate drug use patterns for medications of different types such as vasopressors, vasodilators and systemic antibacterial antibiotics. For sepsis patients, combining the use of Temporal Abstraction digital phenotyping and process mining tools and techniques, was proven to help extract accurate cohorts of patients for health care process mining.
Yuval Elovici - One of the best experts on this subject based on the ideXlab platform.
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A distributed architecture for efficient parallelization and computation of knowledge-based Temporal Abstractions
Journal of Intelligent Information Systems, 2011Co-Authors: Asaf Shabtai, Yuval Shahar, Yuval EloviciAbstract:Today, data storage capabilities as well as computational power are rapidly increasing. On the one hand, this improvement makes it possible to generate and store a great amount of Temporal (time-oriented) data for future query, analysis and discovery of new knowledge. On the other hand, systems and experts are encountering new problems in processing this increased amount of data. The rapid growth in stored time-oriented data necessitates the development of new methods for handling, processing, and interpreting large amounts of Temporal data. One approach is to use an automatic summarization process based on predefined knowledge, such the Knowledge-Based Temporal-Abstraction (KBTA) method. This method enables one to summarize and reduce the amount of raw data by creating higher level interpretations based on predefined domain knowledge. Unfortunately, the task of Temporal Abstraction is inherently computationally expensive, especially when an enormous volume of multivariate data has to be handled and when complex patterns need to be considered. In this research, we address the scalability problem of a Temporal-Abstraction task that involves processing significantly large amounts of raw data. We propose a new computational framework, the Distributed KBTA (DKBTA), which efficiently distributes the Abstraction process among several parallel computational nodes, in order to achieve an acceptable computation time. The DKBTA framework distributes the Temporal-Abstraction process along one or more computational axes, each of which enables parallelization of one or more Temporal-Abstraction tasks into which the main Temporal-Abstraction task is decomposed, such as by different subject groups, concepts types, or Abstraction types. We have implemented the DKBTA framework and have evaluated it in a preliminary fashion in the medical and the information security domains, with encouraging results. In our small-scale evaluation, only distribution along the subjects axis and sometimes along the concept-type axis seemed to consistently enhance performance, and only for computations involving individual subjects and not functions of sets of subjects; but this observation might depend on the number of processing units. Additionally, since the communication between the processing units was based on the TCP protocol, we could not observe any speedup even when using two processing units on the same machine. In our further evaluations we plan to use a shared memory architecture in order to exchange data between processing units.
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intrusion detection for mobile devices using the knowledge based Temporal Abstraction method
Journal of Systems and Software, 2010Co-Authors: Asaf Shabtai, Uri Kanonov, Yuval EloviciAbstract:In this paper, a new approach for detecting previously unencountered malware targeting mobile device is proposed. In the proposed approach, time-stamped security data is continuously monitored within the target mobile device (i.e., smartphones, PDAs) and then processed by the knowledge-based Temporal Abstraction (KBTA) methodology. Using KBTA, continuously measured data (e.g., the number of sent SMSs) and events (e.g., software installation) are integrated with a mobile device security domain knowledge-base (i.e., an ontology for abstracting meaningful patterns from raw, time-oriented security data), to create higher level, time-oriented concepts and patterns, also known as Temporal Abstractions. Automatically-generated Temporal Abstractions are then monitored to detect suspicious Temporal patterns and to issue an alert. These patterns are compatible with a set of predefined classes of malware as defined by a security expert (or the owner) employing a set of time and value constraints. The goal is to identify malicious behavior that other defensive technologies (e.g., antivirus or firewall) failed to detect. Since the Abstraction derivation process is complex, the KBTA method was adapted for mobile devices that are limited in resources (i.e., CPU, memory, battery). To evaluate the proposed modified KBTA method a lightweight host-based intrusion detection system (HIDS), combined with central management capabilities for Android-based mobile phones, was developed. Evaluation results demonstrated the effectiveness of the new approach in detecting malicious applications on mobile devices (detection rate above 94% in most scenarios) and the feasibility of running such a system on mobile devices (CPU consumption was 3% on average).
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evaluation of a Temporal Abstraction knowledge acquisition tool in the network security domain
International Conference on Knowledge Capture, 2007Co-Authors: Asaf Shabtai, Yuval Shahar, Maor Atlas, Yuval EloviciAbstract:In this paper we describe the design and evaluation of the Temporal Knowledge Master, a graphical knowledge-acquisition (KA) tool used for entering the knowledge re-quired by any implementation of the Knowledge-Based Temporal Abstraction (KBTA) method. The KBTA method provides mechanisms that perform derivation of context-specific, interval-based abstract interpretations (also known as Temporal Abstractions) from raw time-stamped data, by using a domain-specific knowledge-base. The study evalu-ated the functionality and usability of the KA tool in the computer-network security domain.
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K-CAP - Evaluation of a Temporal-Abstraction knowledge acquisition tool in the network security domain
Proceedings of the 4th international conference on Knowledge capture - K-CAP '07, 2007Co-Authors: Asaf Shabtai, Yuval Shahar, Maor Atlas, Yuval EloviciAbstract:In this paper we describe the design and evaluation of the Temporal Knowledge Master, a graphical knowledge-acquisition (KA) tool used for entering the knowledge re-quired by any implementation of the Knowledge-Based Temporal Abstraction (KBTA) method. The KBTA method provides mechanisms that perform derivation of context-specific, interval-based abstract interpretations (also known as Temporal Abstractions) from raw time-stamped data, by using a domain-specific knowledge-base. The study evalu-ated the functionality and usability of the KA tool in the computer-network security domain.
Lucia Sacchi - One of the best experts on this subject based on the ideXlab platform.
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a Temporal Abstraction framework for classifying clinical Temporal data
American Medical Informatics Association Annual Symposium, 2009Co-Authors: Iyad Batal, Lucia Sacchi, Riccardo Bellazzi, Milos HauskrechtAbstract:The increasing availability of complex Temporal clinical records collected today has prompted the development of new methods that extend classical machine learning and data mining approaches to time series data. In this work, we develop a new framework for classifying the patient’s time-series data based on Temporal Abstractions. The proposed STF-Mine algorithm automatically mines discriminative Temporal Abstraction patterns from the data and uses them to learn a classification model. We apply our approach to predict HPF4 test orders from electronic patient health records. This test is often prescribed when the patient is at the risk of Heparin induced thrombocytopenia (HIT). Our results demonstrate the benefit of our approach in learning accurate time series classifiers, a key step in the development of intelligent clinical monitoring systems.
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AMIA - A Temporal Abstraction framework for classifying clinical Temporal data.
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2009Co-Authors: Iyad Batal, Lucia Sacchi, Riccardo Bellazzi, Milos HauskrechtAbstract:The increasing availability of complex Temporal clinical records collected today has prompted the development of new methods that extend classical machine learning and data mining approaches to time series data. In this work, we develop a new framework for classifying the patient's time-series data based on Temporal Abstractions. The proposed STF-Mine algorithm automatically mines discriminative Temporal Abstraction patterns from the data and uses them to learn a classification model. We apply our approach to predict HPF4 test orders from electronic patient health records. This test is often prescribed when the patient is at the risk of Heparin induced thrombocytopenia (HIT). Our results demonstrate the benefit of our approach in learning accurate time series classifiers, a key step in the development of intelligent clinical monitoring systems.
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FLAIRS Conference - Multivariate Time Series Classification with Temporal Abstractions
2009Co-Authors: Iyad Batal, Lucia Sacchi, Riccardo Bellazzi, Milos HauskrechtAbstract:The increase in the number of complex Temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data. This work focuses on methods for multivariate time-series classification. Time series classification is a challenging problem mostly because the number of Temporal features that describe the data and are potentially useful for classification is enormous. We study and develop a Temporal Abstraction framework for generating multivariate time series features suitable for classification tasks. We propose the STF-Mine algorithm that automatically mines discriminative Temporal Abstraction patterns from the time series data and uses them to learn a classification model. Our experimental evaluations, carried out on both synthetic and real world medical data, demonstrate the benefit of our approach in learning accurate classifiers for time-series datasets.
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Temporal Abstraction for feature extraction: a comparative case study in prediction from intensive care monitoring data.
Artificial intelligence in medicine, 2007Co-Authors: Marion Verduijn, Lucia Sacchi, Niels Peek, Riccardo Bellazzi, Evert De Jonge, Bas A De MolAbstract:Summary Objectives To compare two Temporal Abstraction procedures for the extraction of meta features from monitoring data. Feature extraction prior to predictive modeling is a common strategy in prediction from Temporal data. A fundamental dilemma in this strategy, however, is the extent to which the extraction should be guided by domain knowledge, and to which extent it should be guided by the available data. The two Temporal Abstraction procedures compared in this case study differ in this respect. Methods and material The first Temporal Abstraction procedure derives symbolic descriptions from the data that are predefined using existing concepts from the medical language. In the second procedure, a large space of numerical meta features is searched through to discover relevant features from the data. These procedures were applied to a prediction problem from intensive care monitoring data. The predictive value of the resulting meta features were compared, and based on each type of features, a class probability tree model was developed. Results The numerical meta features extracted by the second procedure were found to be more informative than the symbolic meta features of the first procedure in the case study, and a superior predictive performance was observed for the associated tree model. Conclusion The findings indicate that for prediction from monitoring data, induction of numerical meta features from data is preferable to extraction of symbolic meta features using existing clinical concepts.
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comparison of two Temporal Abstraction procedures a case study in prediction from monitoring data
American Medical Informatics Association Annual Symposium, 2005Co-Authors: Marion Verduijn, Arianna Dagliati, Lucia Sacchi, Niels Peek, Riccardo Bellazzi, Evert De Jonge, Bas A De MolAbstract:This paper presents an empirical comparison of two Temporal Abstraction procedures, that were applied to derive predictive features for a prediction problem in intensive care medicine. The first procedure employs knowledge from practitioners to derive qualitative patterns of state changes; the second procedure searches through a large number of data summaries to discover those that have predictive value. The derived features were used to predict whether postsurgical patients would need mechanical ventilation longer then 24h. The data-driven Temporal Abstraction procedure was found to provide more informative predictors, resulting in better predictions.
