Network-Agnostic Computational Approaches for Modelling and Validating Evolving Complex Systems
Time: Wed 2025-02-19 13.00
Location: T2 (Jacobssonsalen), Hälsovägen 11C, Huddinge
Video link: https://kth-se.zoom.us/j/66116245953
Language: English
Subject area: Technology and Health
Doctoral student: Arsineh Boodaghian Asl , Hälsoinformatik och logistik
Opponent: Professor Hajime Mizuyama, Aoyama Gakuin University
Supervisor: Professor Sebastiaan Meijer, Hälsoinformatik och logistik; Associate Professor Jayanth Raghothama, Hälsoinformatik och logistik; Associate Professor Adam S. Darwich, Hälsoinformatik och logistik
Abstract
The dynamic and evolving nature of complex systems influences the behaviour of individual parts and the performance of the whole system, which hinders the flexibility in modelling and applicability of the modelling approaches for various systems. Modelling approaches which are developed for other types of systems have limitations in capturing the behaviour of health systems. This can stem from the dependencies of models on the system structure, type and data structure. From the network perspective, health systems can have various representations, which are defined by each network topology, type and parameters. This makes it challenging to utilize an approach developed to analyse and model systems of other aspects and domains.
The degree of flexibility of an approach depends on its scalability to be able to model a system that grows, its generality to model systems of various types, its adaptability to internal changes, and its flexibility to topology without the need for change. Additionally, flexibility should enable the approach adjustment with new functions and features to facilitate and analyse other aspects of a system easily. Network representation of complex health systems facilitates the application of network algorithms and network simulation methods to enhance the modelling approaches. This requires the adjustment of these algorithms and methods, which can be achieved by tuning, merging, modifying, and gaming procedures. Such adjustments increase the efficiency of a model to analyse different aspects of a system and overcome limitations.
This thesis offers various network-agnostic computational approaches for modelling and validating complex health systems. For this, three different case studies are provided to explore which represent the complex societal, hospital and organisational systems, respectively. The proposed modelling approaches for these systems aim to facilitate the quantification of vertices and edges, classify the vertices’ behaviours, promote verification and validation, evaluate the systems' performances, and enhance the link prediction for a more accurate representation of the systems.
The key contribution of this thesis is to offer approaches which can facilitate scalability, generality, adaptability, topological flexibility, and adjustability. This is achieved by adjusting the network algorithms and simulation methods based on the purpose of the modelling. Hence, 1) a gaming simulation approach is proposed to facilitate the quantification of the edges in a complex societal system, 2) ranking algorithm is merged with path analysis to quantify the vertices and identify the vertices persistency in a complex societal system, 3) ranking algorithm is merged with system dynamic simulation to facilitate the quantification of the vertices as the complex societal system evolve and change, 4) an agent-based network simulation is implemented along with network algorithms to identify the bottlenecks using flow and structural hole algorithms and evaluation the performances of the wards using percolation and perturbation algorithm in a complex hospital system, 5) a modification of flow algorithm is proposed to model the dynamic nonlinear flow of the patients in a complex hospital system, 6) a link prediction approach which merges the path analysis and non\_randomness algorithm to identify the missing links in a complex organizational system, and finally 7) a multi-network simulation to evaluate the performances of the organizations in parallel.
The thesis provides two different classifications of the proposed approaches. The first classification indicates how each approach contributes to modelling the system based on the underlying system scale, type, dynamic, topology and model adjustability, and the second classification indicates the proper application of network algorithms and network simulation methods based on the underlying health system type and the purpose of the analysis.
Each approach provides a holistic view of the systems through matrix or network representation to inform about their states and a view of the vertices’ dynamic behaviours to evaluate their performances.