Table 1.

Theoretical and methodological studies in processing temporal information in medicine

Theories and methodologies	Related work
Category 1: applying theories and models in temporal reasoning in AI
Probabilistic approaches [66]	Associated with the tasks of interpreting or forecasting with time- stamped clinical data whose values are affected by different sources of uncertainty. Many studies in time [60, 67–71] have applied Bayesian probabilistic networks.
Event calculus [22]	Applied to assess the evolution of a patient’s status [72]. It also has been adopted as the temporal ontology in a temporal abstraction approach for clinical time-oriented databases [73].
Markov decision process [74]	Applied to solve decision problems in which the optimal choice has to be revised periodically in accordance to the evolution of the patient’s conditions [75].
Allen’s interval [29]	Applied to the tasks of temporal abstraction and query process to determine qualitative temporal relationships between medical events [24, 30, 32, 33, 76].
Temporal constraint network [34]	Used to facilitate patient-monitoring and problem-detection [36], manage medical resources [38], and treat temporal constraints in clinical guidelines [37, 39, 77–80]. It has also been applied to model temporal information in clinical discharge summaries [57].
Case-based reasoning [81]	Used to determine similarities in patient treatments mainly with respect to their pattern of time and dosage [82]. Schmidt [83] proposed a method for prognosis of temporal courses, which combines temporal abstractions with case-based reasoning.
Category 2: developing frameworks that meet needs from clinical applications
Temporal Database Management	Involves tasks of storing, processing and retrieving time-oriented information [84, 85]. Various modeling approaches have been studied. Wiederhold proposed a Time Oriented Databank (TOD) [86] which created a cubic view of time-stamped clinical data. Kahn et al [24, 33] described a query language called TQuery. Das [87–89] proposed a method for temporal integration called temporal mediation which addresses the problem of temporal model heterogeneity. Other approaches involve relational models [90, 91], entity-relationship models [92, 93] or object-oriented models [94–96].
Temporal Abstraction	Relates to the task of creating interval-based concepts (abstractions) from time-stamped raw data [30]. Many studies have been conducted [5, 30, 58, 73, 82, 83, 97–107], specifically focusing on clinical domains such as chronic diseases, growth and development problems, and intensive neonatal or adult care unit settings, where clinical data are either cumulative or arrive rapidly. Shahar [5, 30, 98] developed the RESUME system, which utilizes specific explicit domain ontologies and contains several mechanisms that deal with five temporal-abstraction subtasks.
Temporal Data Visualization	Involves tasks of collecting, navigating and visualizing time-oriented information. Some early work used raw clinical data. For example, Cousins [108] developed a system called a time line with a set of operators and a user interface that allows time lines to be manipulated. Later on, systems appeared with a more flexible zoom in and zoom out interface [109, 110]. Shahar et al [102, 111–115] developed methods and systems for visualization of domain specific temporal abstractions. Other methods [116–118] in this field include 2D and 3D visualization, and various statistical and graphical methodologies.
Category 3: resolving issues such as temporal granularity and uncertainty
Temporal Granularity	Involves tasks of representing and storing time points and time spans with different and mixed granularities, converting a temporal primitive from one granular level to another, and handling granularity mismatches between two sets of data [2, 4, 31, 57, 60, 61, 87, 88, 119–125].
Temporal Uncertainty	Relates to tasks of handling vague and uncertain temporal information which is due to imprecise or unreliable data and knowledge [1, 31, 57, 126–129], e.g. fuzzy sets theory was used to model imprecise temporal information within a diagnostic framework [128].