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

Abstract

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.

High-throughput Phenotyping with Temporal Sequences

“…The conception of time relies on how we perceive it, but it can become tenseless when described with a mathematical structure. [11] Temporality of EHR observation data has not been fully exploited by current methods in biomedical research. [3] While interest in applying temporal approaches to EHR data is growing, progress is inhibited by complexities of adequately representing time in EHR observations.…”

“…Applying feature selection methods and inclusion of ontologies have been recommended to generate a small set of predictive and non-spurious temporal patterns. [6,11] Figure 2 illustrates our study design. Adapting the framework from Moskovitch and Shahar (2015), we use a four-phase framework for classification of disease using EHR observations.…”

“…[3] The conception of time, in general, can be domain-specific and complex, entailing significant domain knowledge to accurately understand and harness. [11] The time-stamped EHR observations, in particular, are often acquired asynchronously (i.e., measured at different time instants and sampled irregularly in time), sparse, and include heterogeneous longitudinal data (often both time points and time intervals), thus provide fundamental challenges for directly applying common temporal analysis methods. [11][12][13][14][15][16] For example, the record dates associated with diagnoses codes in EHRs often reflect when the diagnoses were made by clinicians, not the actual onset of the disease.…”

Biosensors and Internet of Things in smart healthcare applications: challenges and opportunities

“…In recent years, temporal abstractions (TA) have been used as a method to derive high level concepts from time stamped data (Stacey & McGregor, 2007). The idea behind TA is to move from a point-based to an interval-based representation of data, which effectively summarizes the data into meaningful parts that are interpretable by the users of the system (Madkour et al, 2016). The evidence arising from the comparison of different cases is fed into decision models to identify and suggest interventions that either prevent the occurrence of risks or reduce their effect on patient health.…”

Decision support system for in-flight emergency events

“…For example, in order to describe some attributes in the characterization of patient condition, the linkage concept Finding site indicates where a disease is located in the body (e.g., the disease pneumonia has Finding site equals to lung structure). Temporal data representation and reasoning are very important in order to extract knowledge from massive data and therefore for constructing the timelines for the medical histories of patients (Madkour et al 2016). Medical knowledge is very concerned by diverse aspects of uncertainty, and hybrid frameworks (including Bayesian probability theory, evidence theory, and fuzzy set theory) are suggested to manage the epistemic uncertainty of medical prognosis and diagnosis (Janghorbani and Moradi 2017).…”