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An Enhanced Genetic Algorithm With Clustering for Optimizing Mobile Stroke Unit Deployment
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-0403-5353
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-8209-0921
Department of Neurology, Lund, Sweden.
2024 (English)In: 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE): Nov. 27 2024 to Nov. 29 2024Kragujevac, Serbia, Institute of Electrical and Electronics Engineers (IEEE), 2024Chapter in book (Refereed)
Abstract [en]

Mobile stroke units (MSUs), which are specialized ambulances equipped with a brain imaging device and staffed with trained healthcare personnel, have the potential to provide rapid on-site diagnosis and treatment for stroke patients. However, efficient access to prehospital stroke care requires optimizing the placement of MSUs. The MSU allocation problem has been previously solved using a traditional genetic algorithm that utilizes random starting solutions. The use of random starting solutions can, however, cause the algorithm to converge slowly. This can be especially problematic if the initial solutions are significantly far from the global optimum. To address this problem, we propose an enhanced genetic algorithm with clustering (EGAC), which is a time-efficient method to solve the MSU allocation problem by identifying the optimal locations of MSUs in a geographic region. By leveraging clustering, the EGAC provides diverse and comprehensive coverage, avoiding the pitfalls of starting with closely located and potentially less optimal solutions, thereby effectively steering and accelerating its convergence towards the optimal MSU placements. Our experimental results show that the EGAC significantly outperforms the traditional genetic algorithm, without cluster-based starting solutions, by achieving remarkably faster convergence toward the optimal solution for different number of MSUs to allocate. We validate the performance of the EGAC through qualitative and quantitative analyses.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024.
Series
IEEE International Symposium on Bioinformatics and Bioengineering, ISSN 2159-5410, E-ISSN 2471-7819
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:mau:diva-73678DOI: 10.1109/BIBE63649.2024.10820448Scopus ID: 2-s2.0-85217167249ISBN: 979-8-3315-1862-2 (electronic)ISBN: 979-8-3315-1863-9 (print)OAI: oai:DiVA.org:mau-73678DiVA, id: diva2:1935688
Available from: 2025-02-07 Created: 2025-02-07 Last updated: 2025-02-18Bibliographically approved
In thesis
1. Artificial intelligence for enhanced prehospital stroke care: focus on efficient mobile stroke unit allocation and travel time estimation
Open this publication in new window or tab >>Artificial intelligence for enhanced prehospital stroke care: focus on efficient mobile stroke unit allocation and travel time estimation
2025 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Stroke remains one of the leading causes of death and disability globally, underscoring the importance of timely and effective prehospital care to improve patient outcomes. This thesis aims to use artificial intelligence’s power to enhance prehospital stroke care. To accomplish this, we study challenges in prehospital stroke care by focusing on three interrelated research challenges: Mobile stroke unit (MSU) allocation, ambulance travel time estimation, and improving travel time calculations within emergency medical service (EMS) simulation. We develop and analyze different optimization and machine learning (ML) methods to achieve improved analysis and planning of prehospital stroke care. In particular, we propose methods to solve the MSU allocation problem, which aims to identify the optimal locations for a fixed number of MSUs at the existing ambulance station locations within a geographic region. Moreover, we develop a machine learning-based regression method for ambulance travel time estimation. Next, we apply our pre-trained ML-based regression method to improve ambulance travel time estimation within an EMS simulation framework.

For the MSU allocation problem, we first propose a mathematical model, which we apply to identify the optimal MSU locations in the Blekinge and Kronoberg counties of Sweden. The experimental findings show both the correctness of the suggested model and the benefits of placing MSUs in the considered regions. Second, we propose a Genetic algorithm (GA) method with an efficient encoding scheme for the input data, representing the number of MSUs and potential sites. Additionally, we develop custom selection, crossover, and mutation operators tailored to the specific characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method’s flexibility and adaptability through a series of experiments across multiple settings. Third, we propose the enhanced genetic algorithm with clustering (EGAC). By leveraging clustering, the EGAC provides diverse and comprehensive coverage, avoiding the pitfalls of starting with closely located and potentially less optimal solutions, thereby effectively steering and accelerating its convergence towards the optimal MSU placements. Our experimental results show that the EGAC significantly outperforms the traditional genetic algorithm, which does not make use of cluster-based starting solutions, by achieving remarkably faster convergence towards the optimal solution for different numbers of MSUs to allocate. We illustrate the performance of the EGAC through qualitative and quantitative analyses.

For ambulance travel time estimation, we propose an ML-based regression method for estimating ambulance travel times. Ambulance travel time estimations play a pivotal role in ensuring timely and efficient emergency medical care by predicting the time needed by an ambulance to reach a specific location. Overlooking factors such as local traffic situations, day of the week, hour of the day, or the weather may create a risk of inaccurately estimating the ambulance travel times, which might lead to longer emergency response times, potentially impacting patient outcomes. We propose a machine learning approach to accurately estimate ambulance travel times, particularly using regression models and real-world spatiotemporal data from the Skåne region, Sweden. Our method includes data preprocessing and feature engineering, with a focus on variables significantly correlated with travel time. Through a comprehensive exploratory data analysis, we highlight the main characteristics, patterns, and underlying trends of the considered ambulance data set. We present an extensive empirical analysis comparing the performance of different machine learning models across different ambulance travel trips and feature sets, revealing insights into the importance of each feature in improving the estimation accuracy.

Another focus of this thesis is the use of our ML-based regression method to improve the ambulance travel time estimation within EMS simulation. To illustrate the effectiveness of the proposed regression modeling, we utilize a modeling construction framework to construct an EMS simulation model for stroke patients and applied it in a scenario study covering Skåne County, Sweden. The result of the simulation shows differences in am- bulance driving times when using the ML-based module compared to existing routing engines designed for passenger cars. The observed differences emphasize the impacts of integrating ML-based estimations into EMS simulations.

Place, publisher, year, edition, pages
Malmö: Malmö University Press, 2025. p. 35
Series
Studies in Computer Science ; 33
Keywords
Artificial intelligence, optimization, machine learning, mobile stroke unit, clustering, fast convergence, genetic algorithm, ambulance allocation, simulation, emergency medical service, healthcare, travel time estimation
National Category
Communication Systems
Identifiers
urn:nbn:se:mau:diva-73660 (URN)10.24834/isbn.9789178775835 (DOI)978-91-7877-582-8 (ISBN)978-91-7877-583-5 (ISBN)
Presentation
2025-02-28, C0315 Niagara, Malmö, 13:00 (English)
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Available from: 2025-02-09 Created: 2025-02-07 Last updated: 2025-02-11Bibliographically approved

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Abid, Muhammad AdilHolmgren, JohanLorig, Fabian

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