NéoGanesh: a working system for the automated control of assisted ventilation in ICUs
Introduction
There is a growing need for computerized systems able to assist clinical staff in decision making, especially in medical environments such as operating rooms or intensive care units (ICUs), where the flow of information is abundant, false positive alarms are common and life-threatening situations should be prevented. These intelligent patient monitoring systems must reason about complex situations under real-time constraints such as resource limitations and must guarantee a timely response. Building such systems is a challenging goal for the emerging research area of real-time artificial intelligence (AI) [31]and more specifically `adaptive intelligent systems' [22]. This paper describes an intelligent patient monitoring system for the automatic control of mechanical ventilation.
For a number of reasons, including the complexity of medical reasoning, interference from noise, considerations of liability, and social and cultural factors, most intelligent patient monitoring systems are open-loop systems with respect to planning and control. Sepia [38]for monitoring patients hospitalized in hemato-oncology departments is a good example of such a system. However, there are specific well defined medical problems, in particular planning drug therapy [11], where closed-loop systems can be proposed. Such closed-loop systems can further improve the management of patient care because they operate continuously on a daily basis.
Computers have been used in clinical practice for traditional tasks such as database management, data acquisition and physiological signal processing. Sophisticated systems which might provide advice in the choice of therapy and assistance in diagnosis are not yet widely used. Several such systems are described in the literature, but only a few of them are routinely used in clinical practice. `Intelligent' decision support systems will help the physician to make better clinical decisions but they have yet to be tested at the patients bedside.
Five years ago in collaboration with the ICU at the Henri Mondor Hospital (Créteil, France), we started a project to explore the feasibility of and the clinical interest in a system providing automated control of mechanical ventilation and the decision for extubation. We decided that such an intelligent system should be able to: (1) handle in real-time a huge mass of information about the patients state; (2) diagnose observed situations; (3) predict the evolution of the patients state; (4) construct action plans with prompt reaction in emergency cases; and (5) execute planned actions.
In this paper we report on how new advances in knowledge representation techniques, in particular the association of the object-oriented paradigm with production rules, temporal reasoning and distribution of knowledge, have been used to design the system. We report on the clinical results obtained with our prototype, called NéoGanesh, which has been used to ventilate a large number of patients in our ICU. To be able to work in closed-loop and to be tested in real medical situations, NéoGanesh deals with a voluntarily limited problem. However, embedded in a powerful distributed environment, it is intended to support future extensions and refinements and to allow reuse of the knowledge bases developed so far.
Our paper is structured as follows. Section 2defines the medical problem, details the different levels of control for the management of mechanical ventilation and briefly outlines several systems that address a similar problem. Section 3describes the main characteristics of the NéoGanesh system. Section 4and Section 5respectively detail the distributed architecture of NéoGanesh and how medical expertise is modeled. Section 6is devoted to the clinical results. Finally, we conclude on the interest of our approach and discuss future extensions of NéoGanesh.
Section snippets
The control of mechanical ventilation
The mechanical assistance provided for a patient with respiratory insufficiency must be well adapted to his or her physiological needs. The clinician must assess the respiratory comfort of the patient and the time-course of his or her ventilation and must set the ventilator parameters accordingly. A second task of the clinician is to reduce mechanical assistance gradually until the patient is able to breathe alone. This task is known as the weaning process.
During weaning from mechanical
The NéoGanesh system
NéoGanesh is based on the knowledge of ventilation management acquired by the clinical staff of the ICU at the Henri Mondor Hospital (Créteil, France). In contrast to other systems 23, 37, NéoGanesh deals with a voluntarily limited problem: only one mode of ventilation is managed by the system, i.e. pressure support ventilation (PSV) used to ventilate patients who can have spontaneous respiratory activity. These limitations allowed us to design a closed-loop system that controls the ventilator
A distributed architecture
Distributed artificial intelligence and especially multi-agent architectures provide a powerful paradigm for the modeling and the development of complex systems. It is based on the decomposition of systems into several interacting and autonomous entities. Recent applications show the growing interest in this paradigm in the medical domain 21, 27. According to the definition of Shoham [40]“an agent refers to an entity that functions continuously and autonomously in an environment in which other
Knowledge representation in NéoGanesh
Interpreting data over time is an essential task of diagnostic and control processes. The time course of a process, determined from the evolution of a set of representative parameters, is central to predicting its future behavior and to choosing actions over time to influence it. Since the early work in AI, many formal studies have been conducted about change and time representation 10, 46. However, the complexity of temporal constraint propagation algorithms have limited the integration of a
Evaluation
In NéoGanesh, both synchronous communication and asynchronous communication between agents are possible, although the latter has not been tested yet in a clinical environment. Preliminary results [21]showed that the response time (the elapsed time between the perception of an event and an action on the ventilator) and the reaction time (the elapsed time between the perception of an event by the DataProcessor agent and its processing by the Classifier agent) are shorter with the introduction of
Conclusion
NéoGanesh is based on current AI technologies: sophisticated knowledge representation and temporal reasoning in a distributed architecture. We have chosen an environment which combines actors, objects, and production rules. We fully exploit the well known mechanisms of object-oriented programming and by using the inheritance mechanism our system can be easily extended.
Preliminary clinical studies performed at the Henri Mondor Hospital have demonstrated that patients show less signs of
Acknowledgements
This work has been partially financially supported by Hamilton AG. We wish to thank Professor Jean-François Perrot for his stimulating advice and constant encouragement that significantly influenced this work.
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