Temporal abstraction and temporal Bayesian networks in clinical domains: A survey

Orphanou, Kalia; Stassopoulou, Athena; Keravnou, Elpida

doi:10.1016/j.artmed.2013.12.007

Cited by 54 publications

(18 citation statements)

References 64 publications

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“…Attempts have been made to address both of these for time series classication. Some studies extract a xed number of values from the time series, such as the length, the average value, or the inclination between the rst and last values [11,12]. Meanwhile, other studies focus on shrinking the whole time series into a single point by taking the weighted sum of all values, where the time stamp of each value is used for assigning the corresponding weights [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Learning from heterogeneous temporal data in electronic health records

Zhao

Papapetrou

Asker

et al. 2017

Journal of Biomedical Informatics

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Electronic health records contain large amounts of longitudinal data that are valuable for biomedical informatics research. The application of machine learning is a promising alternative to manual analysis of such data. However, the complex structure of the data, which includes clinical events that are unevenly distributed over time, poses a challenge for standard learning algorithms. Some approaches to modeling temporal data rely on extracting single values from time series; however, this leads to the loss of potentially valuable sequential information. How to better account for the temporality of clinical data, hence, remains an important research question. In this study, novel representations of temporal data in electronic health records are explored. These representations retain the sequential information, and are directly compatible with standard machine learning algorithms. The explored methods are based on symbolic sequence representations of time series data, which are utilized in a number of different ways. An empirical investigation, using 19 datasets comprising clinical measurements observed over time from a real database of electronic health records, shows that using a distance measure to random subsequences leads to substantial improvements in predictive performance compared to using the original sequences or clustering the sequences. Evidence is moreover provided on the quality of the symbolic sequence representation by comparing it to sequences that are generated using domain knowledge by clinical experts. The proposed method creates representations that better account for the temporality of clinical events, which is often key to prediction tasks in the biomedical domain.

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Section: Introductionmentioning

confidence: 99%

Learning from heterogeneous temporal data in electronic health records

Zhao

Papapetrou

Asker

et al. 2017

Journal of Biomedical Informatics

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“…Temporal abstraction (TA) is a well known data analysis technique that is frequently applied in clinical domains to analyze complex multivariate clinical histories [129] [130], i.e. the series of relevant clinical events occurring to a patient (e.g.…”

Section: Temporal Processing In the Clinical Domainmentioning

confidence: 99%

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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“…DBNs have been applied in many clinical domains for various tasks such as prognosis and diagnosis. A detailed survey on TA and DBN applied to medicine and the benefits of their potential integration can be found in our recent work in [7]. The paper is organized as follows: In Sect.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment for Primary Coronary Heart Disease Event Using Dynamic Bayesian Networks

Orphanou

Stassopoulou

Keravnou

2015

Artificial Intelligence in Medicine

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Abstract. Coronary heart disease (CHD) is the leading cause of mortality worldwide. Primary prevention of CHD denotes limiting a first CHD event in individuals who have not been formally diagnosed with the disease. This paper demonstrates how the integration of a Dynamic Bayesian network (DBN) and temporal abstractions (TAs) can be used for assessing the risk of a primary CHD event. More specifically, we introduce basic TAs into the DBN nodes and apply the extended model to a longitudinal CHD dataset for risk assesment. The obtained results demonstrate the effectiveness of our proposed approach.

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Temporal abstraction and temporal Bayesian networks in clinical domains: A survey

Cited by 54 publications

References 64 publications

Learning from heterogeneous temporal data in electronic health records

Learning from heterogeneous temporal data in electronic health records

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

Risk Assessment for Primary Coronary Heart Disease Event Using Dynamic Bayesian Networks

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