Simulation of Trauma Incidents

Mathematical modeling and simulation with medical applications has gained much interest in the last few years, mainly due to the widespread availability of low-cost technology and computational power. This paper presents an integrated platform for the in-silico simulation of trauma incidents, based on a suite of interacting mathematical models. The models cover the generation of a scenario for an incident, a model of physiological evolution of the affected individuals, including the possible effect of the treatment, and a model of evolution in time of the required medical resources. The problem of optimal resource allocation is also investigated. Model parameters have been identified according to the expertise of medical doctors and by reviewing some related literature. The models have been implemented and exposed as web services, while some software clients have been built for the purpose of testing. Due to its extendability, our integrated platform highlights the potential of model-based simulation in different health-related fields, such as emergency medicine and personal health systems. Modifications of the models are already being used in the context of two funded projects, aiming at evaluating the response of health systems to major incidents with and without model-based decision support.

[1]  Jianghai Hu,et al.  Stochastic Hybrid Systems , 2013 .

[2]  Guy L. Steele,et al.  The Java Language Specification , 1996 .

[3]  R. Chambers,et al.  The role of mathematical modeling in medical research: "research without patients?". , 2000, The Ochsner journal.

[4]  Roy C. P. Kerckhoffs,et al.  Current progress in patient-specific modeling , 2010, Briefings Bioinform..

[5]  Gabor D Kelen,et al.  Refining Surge Capacity: Conventional, Contingency, and Crisis Capacity , 2009, Disaster Medicine and Public Health Preparedness.

[6]  Grisha Spasov,et al.  Architectural models for realization of web-based personal health systems , 2009, CompSysTech '09.

[7]  Andrea Malizia,et al.  Modelling and simulation for Major Incidents , 2015, 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).

[8]  Omar Bouamra,et al.  Outcome prediction modelling for trauma patients: a German perspective , 2014, Critical Care.

[9]  Gordon Clapworthy,et al.  Digital Human Modelling: A Global Vision and a European Perspective , 2007, HCI.

[10]  M. Marschollek,et al.  Health-enabling technologies for pervasive health care: on services and ICT architecture paradigms , 2008, Informatics for health & social care.

[11]  K A Schulman,et al.  Mathematical Models in Decision Analysis , 1997, Infection Control & Hospital Epidemiology.

[12]  Cornelia M van Duijn,et al.  Genome-based prediction of common diseases: advances and prospects. , 2008, Human molecular genetics.

[13]  Johan G. Blickman,et al.  Advanced Trauma Life Support®. ABCDE from a radiological point of view , 2007, Emergency Radiology.

[14]  G. Ginsburg,et al.  The path to personalized medicine. , 2002, Current opinion in chemical biology.

[15]  Martin Hill,et al.  Disaster Medicine: Using Modeling and Simulation to Determine Medical Requirements for Responding to Natural and Man-Made Disasters , 2010 .

[16]  W. Haddon,et al.  The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. , 1974, The Journal of trauma.

[17]  R. Sanfelice,et al.  Hybrid dynamical systems , 2009, IEEE Control Systems.

[18]  D B Chalfin Decision analysis in critical care medicine. , 1999, Critical care clinics.

[19]  Pasquale Palumbo,et al.  Theoretical Biology and Medical Modelling Open Access a Discrete Single Delay Model for the Intra-venous Glucose Tolerance Test , 2022 .