Design of a flexible platform for execution of medical decision support agents in the intensive care unit

This paper addresses the design of a generic and scalable platform for the execution of medical decision support agents in the intensive care unit (ICU). As will be motivated, medical decision support agents can impose high computational load and in practical setups a large amount of such agents are typically running in parallel. Future ICU systems will rely on extensive medical decision support. However, in current systems only one workstation is typically dedicated for the execution of medical decision support agents. Therefore, we propose an architecture based on middleware technology to allow for easy distribution of the agents along multiple workstations. The architecture allows for easy integration with a general ICU data flow management architecture.

[1]  Baruch Awerbuch,et al.  An Opportunity Cost Approach for Job Assignment in a Scalable Computing Cluster , 2000, IEEE Trans. Parallel Distributed Syst..

[2]  M. Levy,et al.  2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference , 2003, Intensive care medicine.

[3]  Dan C. Marinescu,et al.  A computational framework for the 3D structure determination of viruses with unknown symmetry , 2003, J. Parallel Distributed Comput..

[4]  Jordi Sabater-Mir,et al.  A multi-agent system approach for monitoring the prescription of restricted use antibiotics , 2003, Artif. Intell. Medicine.

[5]  D. Mannino,et al.  The epidemiology of sepsis in the United States from 1979 through 2000. , 2003, The New England journal of medicine.

[6]  E. Ivers,et al.  Early Goal-Directed Therapy in the Treatment of Severe Sepsis and Septic Shock , 2001 .

[7]  Bruno Volckaert,et al.  A generic middleware-based platform for scalable cluster computing , 2002, Future Gener. Comput. Syst..

[8]  Rajkumar Buyya,et al.  High Performance Cluster Computing , 1999 .

[9]  Marian Bubak,et al.  An integrative approach to high-performance biomedical problem solving environments on the Grid , 2004, Parallel Comput..

[10]  Jens Gregor,et al.  Simulation of emission tomography using grid middleware for distributed computing , 2004, Comput. Methods Programs Biomed..

[11]  Amnon Barak,et al.  The MOSIX multicomputer operating system for high performance cluster computing , 1998, Future Gener. Comput. Syst..

[12]  Antonio Moreno,et al.  Software agents in health care , 2003, Artif. Intell. Medicine.

[13]  Bruno Volckaert,et al.  Application-Specific Hints in Reconfigurable Grid Scheduling Algorithms , 2004, International Conference on Computational Science.

[14]  J Decruyenaere,et al.  On the design of a generic and scalable multilayer software architecture for data flow management in the intensive care unit. , 2003, Methods of information in medicine.

[15]  Amnon Barak,et al.  The MOSIX Distributed Operating System: Load Balancing for UNIX , 1993 .

[16]  G. Clermont,et al.  Predicting hospital mortality for patients in the intensive care unit: A comparison of artificial neural networks with logistic regression models , 2001, Critical care medicine.

[17]  Simson L. Garfinkel,et al.  PGP: Pretty Good Privacy , 1994 .

[18]  C. Hanson,et al.  Artificial intelligence applications in the intensive care unit , 2001, Critical care medicine.

[19]  David Lee,et al.  The Telescience Portal for advanced tomography applications , 2003, J. Parallel Distributed Comput..

[20]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[21]  Thomas L. Sterling,et al.  BEOWULF: A Parallel Workstation for Scientific Computation , 1995, ICPP.

[22]  Alan H. Morris,et al.  Efficacy of computerized decision support for mechanical ventilation: results of a prospective multi-center randomized trial , 1999, AMIA.

[23]  Jesse B. Hall,et al.  Principles of Critical Care , 1992 .

[24]  Bruno Volckaert,et al.  Design of a middleware-based cluster management platform with task management and migration , 2002, Proceedings. IEEE International Conference on Cluster Computing.

[25]  R. Dybowski,et al.  Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm , 1996, The Lancet.

[26]  William Stallings,et al.  Cryptography and Network Security: Principles and Practice , 1998 .

[27]  Bruno Volckaert,et al.  Grid computing: the next network challenge! , 2004 .

[28]  Jürgen Paetz Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions , 2003, Artif. Intell. Medicine.

[29]  Rajkumar Buyya,et al.  High Performance Cluster Computing: Programming and Applications , 1999 .

[30]  T D East,et al.  Computerized decision support for mechanical ventilation of trauma induced ARDS: results of a randomized clinical trial. , 2001, The Journal of trauma.

[31]  Rainer Schmidt,et al.  Medical multiparametric time course prognoses applied to kidney function assessments , 1999, Int. J. Medical Informatics.

[32]  Baruch Awerbuch,et al.  An Opportunity Cost Approach for Job Assignment and Reassignment in a Scalable Computing Cluster , 2002 .

[33]  Bruno Volckaert,et al.  Evaluation of Grid Scheduling Strategies Through a Network-Aware Grid Simulator , 2003, PDPTA.

[34]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[35]  Filip De Turck,et al.  Design of a generic platform for efficient and scalable cluster computing based on middleware technology , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.