Pilot Study: Agent-Based Exploration of Complex Data in a Hospital Environment

We present the results of a pilot study created to explore a subset of complex data, utilizing an agent-based model simulation tool. These results center on data taken from hospital admission records, tracking patient attributes and how they relate to patient outcomes. The focus of this work is to highlight three design principles: 1) using an iterative process between the modeling of a system and the grounding of that simulation with real-world data; 2) a focus on agent primitives, emphasizing bottom-up emergence of effects, rather than top-down control; and 3) integration of various "theories" of patient care and hospital effectiveness as a method for experimentation with Complex Adaptive System-based data mining.

[1]  Mirsad Hadzikadic,et al.  Learning to Predict: INC2.5 , 1997, IEEE Trans. Knowl. Data Eng..

[2]  M. Mitchell Waldrop,et al.  Complexity : the emerging science and the edge of order and chaos , 1992 .

[3]  Mirsad Hadzikadic,et al.  Towards a General Tool for Studying Threshold Effects Across Diverse Domains , 2009, Advances in Information and Intelligent Systems.

[4]  Simon A. Levin,et al.  Complex adaptive systems: Exploring the known, the unknown and the unknowable , 2002 .

[5]  John H. Holland,et al.  Emergence. , 1997, Philosophica.

[6]  Mirsad Hadzikadic,et al.  An Agent-Based Model of Solid Tumor Progression , 2009, BICoB.

[7]  William Ribarsky,et al.  Advances in Information and Intelligent Systems , 2009, Advances in Information and Intelligent Systems.

[8]  M. Schreckenberg.  Modeling Complex Systems , 2004 .

[9]  Mark A. Bedau,et al.  Emergence : contemporary readings in philosophy and science , 2008 .

[10]  Steven Johnson,et al.  Emergence: The Connected Lives of Ants, Brains, Cities, and Software , 2001 .

[11]  Min Sun,et al.  A Computer Simulation Laboratory for Social Theories , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.