Y. Shahar. Dynamic temporal interpretation contexts for temporal abstraction. In Proceedings of the International Workshop on Temporal Representation and Reasoning, pp. 64--71. IEEE Computer Society Press, 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....and diagnosis can be formulated as temporal constraint satisfaction problems, often involving multiple time granularities. In a temporal constraint satisfaction problem, variables are used to represent event occurrences and constraints are used to represent their granular temporal relationships [6, 25, 38, 59, 82, 101, 105, 110]. Finally, shifts in the temporal perspective occur very often in natural language communication, and thus the ability of supporting and relating a variety of temporal models, at di#erent grain sizes, is a relevant feature for the task of natural language understanding [10, 47, 53] Any time ....

Y. Shahar. Dynamic temporal interpretation contexts for temporal abstraction. In Proceedings of the International Workshop on Temporal Representation and Reasoning, pages 64--71. IEEE Computer Society Press, 1996.

....classified as an infinitely persistent property, chickenpox as a finitely persisting but not a recurring property, and flu as a finitely persisting, recurring, property. Persistence derivation is often context sensitive, where contexts can also be dynamically derived (abstracted) from the raw data [147, 148]. For example if it is known that the patient with the headache took aspirin at noon, it can be derived that the persistence of headache lasted up to about 1pm and that there was no headache up to 3pm. This is based on the derivation of the time interval spanning the persistence of the ....

Shahar, Y., "Dynamic temporal interpretation contexts for temporal abstraction." In: Proc. TIME-96, IEEE Computer Society Press, 1996, pp. 64--71.

....pair of time stamps) thus forming a parameter interval. Interpretation contexts are induced by external events, by certain parameter propositions, by the abstraction process s goals, and by certain combinations of these entities, but are not necessarily contemporaneous to the inducing entities [14]. The TA task is thus the following: Given a set of event, parameter, and goal intervals and the domain s TA ontology, produce an interpretation a set of new abstractions that can answer any temporal query about all the abstractions derivable from the transitive closure of the input data and ....

.... mechanism that solves this task assumes that the temporal scope of the interval based interpretation contexts induced by various parameter and event propositions is represented as a set of distance constraints relative to the temporal scope of the proposition inducing the interpretation context [14]. 2. vertical temporal inference: inference from values of contemporaneous input data or abstractions (e.g. results of several blood tests conducted during the same day) into values of higher level concepts (e.g. classification into bone marrow toxicity Grade II) The computational mechanism ....

Shahar Y. Dynamic temporal interpretation contexts for temporal abstraction. Annals of Mathematics and Artificial Intelligence, in press.

.... FAST) and pattern (e.g. QUIESCENT ONSET) The context forming mechanism creates at runtime context intervals, induced by the presence of certain context forming propositions, such as certain external events (not necessarily with the same temporal scope) over which hold interpretation contexts [31]. Context intervals create a relevant frame of reference for interpretation and enable the temporal abstraction mechanisms to focus only on abstractions relevant for particular contexts, thus creating interpretations that are context specific and avoiding unnecessary computations. The ....

Shahar, Y. (1998). Dynamic temporal interpretation contexts for temporal abstraction. Annals of Mathematics and Artificial Intelligence 22 (1-2) 159-192.

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Shahar Y. Dynamic temporal interpretation contexts for temporal abstraction. Annals of Mathematics and Artificial Intelligence. 1998;22:159-92.

.... level and the amount of scientific works in this area motivated two different special issues of the journal ################################### [15, 16] Another indication is that research focusing on time in clinical applications received attention also from the general computer science field [17, 18, 19, 20, 21, 22, 23, 24, 25, 26]. It is interesting to consider the wide variety of applications that need to deal with temporal aspects of clinical data, such as: management of time oriented data stored in medical records of ambulatory or hospitalized patients [27, 28, 29, 11, 12, 30, 31, 32, 33, 34, 35, 36] prediction of ....

....episode) within a standard temporal database is still a difficult task. More is said about that task when we discuss the issues of temporal granularity and uncertainty in Section 5) Relevant to the topic of relative times are several proposals that employ implicit [6, 14, 52] or explicit [24] temporal contexts, which support the representation of relative or context sensitive temporal clinical information or knowledge. 2.1.4. Modeling temporal relationships In modeling temporal relationships, Allen s interval algebra [64] has been widely used in medical informatics [10, 35, 24, 25] ....

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Shahar Y. Dynamic temporal interpretation contexts for temporal abstraction. In [21]: 64-71.

....execution of these plans (e.g. application of clinical guidelines [3] 4. Meaningful time oriented contexts enable generation of context specific abstractions, maintenance of several interpretations of the same data within different contexts, and certain hindsight and foresight inferences [4]. 5. Temporal abstractions are helpful for explanation of recommended actions by an intelligent system. 6. Temporal abstractions are a useful representation for the process and outcome intentions of designers of clinical guidelines, and enable real time and retrospective critiquing and quality ....

Shahar, Y.: Dynamic temporal interpretation contexts for temporal abstraction. Annals of Mathematics and Artificial Intelligence 22(1-2) (1998) 159-192.

....temporal abstraction (KBTA) method [Shahar, 1997] to derive high level concepts, or abstractions, from raw level data. Each derived abstraction must be associated with a context, since the definitions of these abstractions can change depending on the context in which the data were acquired [Shahar, 1998]. Using a knowledge base of domain specific contexts, parameters, events, and patterns, and a database of time stamped raw level data, the Rsum interpreter derives a set Figure 1. The levels of temporal data abstraction. The lowest level of this graph shows raw data indicating the results of ....

Shahar, Y. Dynamic Temporal Interpretation Contexts for temporal abstraction. Annals of Mathematics and Artificial Intelligence, 1998; 22(1-2): pages 159-192.

.... records, support of recommendations by medical decision support systems (Musen et al. 1996) monitoring the execution of therapy plans, representing intentions of therapy plans as temporal patterns to achieve or avoid, thus supporting automated quality assessment of therapy plan application (Shahar et al. 1998), and supporting intelligent visualization and exploration of time oriented clinical data and their multiple level abstractions, enabling interactive data mining (Shahar and Cheng, 1999) Abstractions must be specific to the medical domain and context in which the data were acquired, because only ....

.... external events (e.g. medications) their interrelations (e.g. PART OF relations) and their properties; 4) a context ontology, which includes interpretation contexts (e.g. the temporal context defined by the effect of a drug) and relations (e.g. SUBCONTEXT) among interpretation contexts (Shahar, 1998); 5) an abstraction goal ontology, which includes the user s potential goals for the abstraction process (these can induce contexts e.g. monitoring of diabetes therapy) and their IS A relations; and (6) all relations between inducing propositions and induced contexts (e.g. INDUCED CONTEXTS) ....

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Shahar, Y. (1998). Dynamic temporal interpretation contexts for temporal abstraction, Annals of Mathematics and Artificial Intelligence, 22(1-2):159-192 Shahar Y, Miksch S., and Johnson P. D. (1998). A task-specific ontology for the application and critiquing of time-oriented clinical guidelines, Artificial Intelligence in Medicine, 14: 29-51.

.... and subcontexts, and postexercise contexts) Glucose state state values (i.e. values of the state(state(glucose) abstract parameter) that are measured within different phases (e.g. prelunch and presupper) but within the same day, can be joined by interpolation within the nonconvex context [Shahar, 1996] version of the PREPRANDIAL generalized interpretation context [Shahar, 1996] thus creating an abstraction comprising several Mapping tables Inference tables Inferential properties Maximal gap functions Parameters Glucose Qualitative physical Hypoglycemia symptoms Glucose state ....

.... values of the state(state(glucose) abstract parameter) that are measured within different phases (e.g. prelunch and presupper) but within the same day, can be joined by interpolation within the nonconvex context [Shahar, 1996] version of the PREPRANDIAL generalized interpretation context [Shahar, 1996], thus creating an abstraction comprising several Mapping tables Inference tables Inferential properties Maximal gap functions Parameters Glucose Qualitative physical Hypoglycemia symptoms Glucose state Glucose state DM Glucose state state DM Glucose state DM preprandial ....

Y. Shahar. Dynamic temporal interpretation contexts for temporal abstraction. In Proceedings of the 1996 Third International Workshop on Temporal Representation and Reasoning, pages 64--71, Key West, Florida, 1996.

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Y. Shahar. Dynamic temporal interpretation contexts for temporal abstraction. Annals of Mathematics and Artificial Intelligence, (in press).

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Y. Shahar. Dynamic temporal interpretation contexts for temporal abstraction. In Proceedings of the International Workshop on Temporal Representation and Reasoning, pp. 64--71. IEEE Computer Society Press, 1996.

No context found.

Shahar Y., Dynamic temporal interpretation contexts for temporal abstraction, in [52], 159-192, 1998.

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Shahar Y. Dynamic temporal interpretation contexts for temporal abstraction. Ann Math Artif Intell. 1998;22(1-2): 159-92.

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