AI systems in clinical practice

Acute care systems
Knowledge-based system for the management of mechanical ventilation in Intensive Care Units (ICUs)

developed by clinical domains keywords
The French National Institute for Health and Medical Research (INSERM) with the Physiology and ICU Departments at the Hôpital Henri Mondor, Créteil, France Mmanagement of mechanical ventilation in Intensive Care Units (ICUs) Knowledge-based system, decision support system
location commissioned status
Hôpital Henri Mondor, Créteil, France; Servei Medicina Intensiva, Hospital Sant Pau, Barcelona. 1992 Decommissioned 2001

See also: Automedon, an enhancement of NéoGanesh.

NéoGanesh is a closed-loop knowledge-based system used for ventilator management in Intensive Care Units. NéoGanesh integrates a distributed model of medical reasoning and an explicit representation of time. The system is based on the representation of physicians expertise. It interprets clinical data in real-time and controls the mechanical assistance provided, in Pressure Support Ventilation mode, to a patient who suffers from a lung disease. NéoGanesh develops a therapeutic strategy to gradually re-educate the respiratory muscles of the patient, and evaluates his capacity to breathe without mechanical assistance. NéoGanesh runs on a microcomputer placed at the patient's bedside, controls a Veolar ventilator (Hamilton Suitzerland) and does not interfere with the usual management of the patients. Our representation paradigm is based on object-orientation and forward chaining production rules. NéoGanesh is implemented in Smalltalk-80. A clinical evaluation of NéoGanesh was performed at Henri Mondor Hospital (Créteil, France). The use of NéoGanesh improves the quality of the patient's ventilation and the prediction of weaning.

Dojat M, Pachet F, Guessoum Z et al. NéoGanesh: a working system for the automated control of assisted ventilation in ICUs. Artif Intell Med. 1997 Oct;11(2):97-117.

[PubMed]   [Science Direct]

" Automating the control of therapy administered to a patient requires systems which integrate the knowledge of experienced physicians. This paper describes NeoGanesh, a knowledge-based system which controls, in closed-loop, the mechanical assistance provided to patients hospitalized in intensive care units. We report on how new advances in knowledge representation techniques have been used to model medical expertise. The clinical evaluation shows that such a system relieves the medical staff of routine tasks, improves patient care, and efficiently supports medical decisions regarding weaning. To be able to work in closed-loop and to be tested in real medical situations, NeoGanesh deals with a voluntarily limited problem. However, embedded in a powerful distributed environment, it is intended to support future extensions and refinements and to support reuse of knowledge bases. "

Dojat M. and Pachet F., An extendable knowledge-based system for the control of mechanical ventilation, in Proc. 14th IEEE-EMBS, Paris, pp. 920-921, 1992.

Dojat M. and Pachet F., Representation of a medical expertise using the Smalltalk environment: putting a prototype to work, in TOOLS 7, G. Heeg, B. Magnusson and B. Meyer, Ed., New York: Prentice Hall, pp. 379-389, 1992.

Dojat M, Brochard L, Lemaire F and Harf A. A knowledge-based system for assisted ventilation of patients in intensive care units, International Journal of Clinical Monitoring and Computing. 1992;9:239-250.

Dojat M and Pachet F. Effective domain-dependent reuse in medical knowledge bases, Comput. Biomed. Res. 1995;28:403-432.

Dojat M, Harf A, Touchard D, Laforest M, Lemaire F and Brochard L. Evaluation of a knowledge-based system providing ventilatory management and decision for extubation, Am. J. Respir. Crit. Care Med. 1996;153:997-1004.

Dojat M and Sayettat C. A realistic model for temporal reasoning in real-time patient monitoring, A.A.I. 1996; 10:121-143.

Dojat M, Harf A, Touchard D, Laforest M, Lemaire F and Brochard L. Evaluation of a knowledge-based system providing ventilatory management and decision for extubation. In: J. H. Van Bemmel and A. T. McCray, eds., Yearbook of Medical Informatics 97. Schattauer, 1997:589-596.

Dojat M, Ramaux N and Fontaine D. Scenario Recognition for Temporal Reasoning in Medical Domains, Artificial Intelligence in Medicine. 1998;14:139-155.

Dojat M, Harf A, Touchard D, Lemaire F and Brochard L. Clinical Evaluation of a Computer-Controlled Pressure Support Mode, Am. J. Respir. Crit. Care Med. 2000;161:1161-66.

Dojat M, Miksch S and Hunter J. Knowledge-Based Information Management in Intensive Care and Anaesthesia, Artificial Intelligence in Medicine. 2000;19:185-187.

Dojat M and Brochard L. Knowledge-based systems for automatic ventilatory management. In: G. Iotti, eds., Respiratory Care Clinics of North America. Closed-Loop Control Mechanical Ventilation. W.B. Saunders Co., Philadelphia, 2001:379-396.

contact links

Michel DOJAT, INSERM, Grenoble, France.

 bullet  Automedon, a significant enhancement of the NéoGanesh system
Michel DOJAT

Archive of AI systems in clinical practice previously administered by Enrico Coiera. Used with permission. Maintained and extended since 2001 by OpenClinical.

Entry on archive: September 29 1995
Last updated: March 20 2003
Search this site


Privacy policy User agreement Copyright Feedback

Last modified:
© Copyright OpenClinical 2002-2011