Readings in medical artificial intelligence: the first decade
Readings in medical artificial intelligence: the first decade
A blackboard architecture for control
Artificial Intelligence
A therapy planning architecture that combines decision theory and artificial intelligence techniques
Computers and Biomedical Research
Implementation of a computerized patient advice system using the HELP clinical information system
Computers and Biomedical Research
Model-based interpretation of time-ordered medical data
Model-based interpretation of time-ordered medical data
Architectures for Intelligence
Architectures for Intelligence
Expert Critiquing Systems
CAUSAL REPRESENTATION OF PATIENT ILLNESS FOR ELECTROLYTE AND ACID-BASE DIAGNOSIS
CAUSAL REPRESENTATION OF PATIENT ILLNESS FOR ELECTROLYTE AND ACID-BASE DIAGNOSIS
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Intelligent monitoring and control
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Input data management in real-time AI systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Model-based monitoring of dynamic systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Integrating diverse reasoning methods in the BB1 blackboard control architecture
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
Incorporating temporal-bounded CBR techniques in real-time agents
Expert Systems with Applications: An International Journal
Qualitative modeling as a paradigm for diagnosis and prediction in critical care environments
Artificial Intelligence in Medicine
Modeling disturbance management in anesthesia: A preliminary report
Artificial Intelligence in Medicine
Guaranteeing real-time response with limited resources
Artificial Intelligence in Medicine
Editorial: Intelligent monitoring and control of dynamic physiological systems
Artificial Intelligence in Medicine
Model-based diagnosis in intensive care monitoring: The YAQ approach
Artificial Intelligence in Medicine
The graphical presentation of decision support information in an intelligent anaesthesia monitor
Artificial Intelligence in Medicine
RT-MOVICAB-IDS: Addressing real-time intrusion detection
Future Generation Computer Systems
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Effective monitoring of device-supported patients in the intensive care unit (ICU) is complex, involving interpretation of many variables, comparative evaluation of many therapy options, and control of many patient-management parameters. Even skilled clinicians make errors that limit the quality of care, harm patients, or cause life-threatening situations. A growing body of research aims to improve ICU monitoring with computer technology. Most of this research falls in two areas: (a) short-term engineering of practical solutions to narrowly defined immediate problems (e.g. smart alarm systems); or (b) basic research on fundamental issues potentially relevant to ICU monitoring (e.g. temporal reasoning). By contrast, our project aims to develop a more comprehensive 'intelligent agent', having a broad range of capabilities, to cooperate on the ICU team. We do not aim to produce a practical system suitable for near-term deployment in the ICU, but rather a 'proof of concept', an experimental system that: (a) demonstrably performs and coordinates a range of intelligent reasoning tasks of use in ICU monitoring; (b) does so reliably in a significant range of medical situations; and (c) arguably will scale up to meet the comprehensive set of practical requirements with an appropriate development effort. We have developed an experimental system called Guardian, which exhibits several of the required capabilities and utilizes an underlying architecture hypothesized to support the full range of required capabilities. In this paper, we describe the Guardian system, its architecture, and its current knowledge base. We describe its performance and summarize the results of preliminary evaluations. Finally, we discuss ongoing and planned research on Guardian.