Abstract: KNAVE-II is a system for visualization and exploration of large amounts of time-oriented clinical data and of multiple levels of clinically meaningful abstractions derivable from these data. KNAVE-II uses a distributed temporal-abstraction architecture that integrates a set of knowledge services, each interacting with a domain-specific knowledge source, a set of data-access services, each interacting with a clinical data source, and a computational service for deriving knowledge-based abstractions of the data. Care providers are often overwhelmed by the amount of time-oriented data associated with patient records, such as those of chronic patients. Providing visualization of the data and of their clinically meaningful interpretati...
Physicians and medical decision-support applications, such as for diagnosis, therapy, monitoring, qu...
The detection of temporal relationships among time-ordered patient data is an important, but difficu...
This paper presents emerging trends in the area of temporal abstraction and data mining, as applied ...
Interpretation and exploration of longitudinal clinical data is a major part of diagnosis, therapy, ...
We describe a domain-independent framework (KNAVE) specific to the task of interpretation, summariza...
ions Yuval Shahar 1 and Cleve Cheng 1 1 Stanford Medical Informatics, Stanford University Schoo...
We describe a general method for abstracting higher-level, interval-based concepts from time-stamped...
The main goal of this work is to propose a framework for the visual specification and query of consi...
Objective: Intelligent clinical data analysis systems require precise qualitative descriptions of da...
This dissertation details a framework for providing knowledge-based temporal reasoning and data anal...
Abstract — Physicians and medical decision-support applications, such as for diagnosis, therapy, mon...
AbstractA new domain-independent knowledge-based inference structure is presented, specific to the t...
The effective and efficient use of information systems in health care organizations and services are...
Clinical reports often include descriptions of events in the patient’s medical history, as well as e...
Temporal abstraction (TA) provides the means to instil domain knowledge into data analysis processes...
Physicians and medical decision-support applications, such as for diagnosis, therapy, monitoring, qu...
The detection of temporal relationships among time-ordered patient data is an important, but difficu...
This paper presents emerging trends in the area of temporal abstraction and data mining, as applied ...
Interpretation and exploration of longitudinal clinical data is a major part of diagnosis, therapy, ...
We describe a domain-independent framework (KNAVE) specific to the task of interpretation, summariza...
ions Yuval Shahar 1 and Cleve Cheng 1 1 Stanford Medical Informatics, Stanford University Schoo...
We describe a general method for abstracting higher-level, interval-based concepts from time-stamped...
The main goal of this work is to propose a framework for the visual specification and query of consi...
Objective: Intelligent clinical data analysis systems require precise qualitative descriptions of da...
This dissertation details a framework for providing knowledge-based temporal reasoning and data anal...
Abstract — Physicians and medical decision-support applications, such as for diagnosis, therapy, mon...
AbstractA new domain-independent knowledge-based inference structure is presented, specific to the t...
The effective and efficient use of information systems in health care organizations and services are...
Clinical reports often include descriptions of events in the patient’s medical history, as well as e...
Temporal abstraction (TA) provides the means to instil domain knowledge into data analysis processes...
Physicians and medical decision-support applications, such as for diagnosis, therapy, monitoring, qu...
The detection of temporal relationships among time-ordered patient data is an important, but difficu...
This paper presents emerging trends in the area of temporal abstraction and data mining, as applied ...