A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia
Anticipating the timing and location of future emergency calls is crucial for making informed decisions about vehicle location and relocation, ultimately reducing response times and enhancing service quality. A predictive model for EMS (Emergency Medical Services) events is proposed to address this...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025002841 |
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| author | Dionicio Neira-Rodado Juan Camilo Paz-Roa John Willmer Escobar Miguel Ángel Ortiz-Barrios |
| author_facet | Dionicio Neira-Rodado Juan Camilo Paz-Roa John Willmer Escobar Miguel Ángel Ortiz-Barrios |
| author_sort | Dionicio Neira-Rodado |
| collection | DOAJ |
| description | Anticipating the timing and location of future emergency calls is crucial for making informed decisions about vehicle location and relocation, ultimately reducing response times and enhancing service quality. A predictive model for EMS (Emergency Medical Services) events is proposed to address this need. The proposed spatiotemporal approach integrates machine learning, signal analysis, and statistical features, capturing geographical, temporal, and event-specific factors. The model identifies patterns associated with the occurrence or absence of emergency calls, using clustering techniques for demand spatial splitting and then training an XGBoost model on the multivariate time series. The model uses signal analysis to extract valuable insights from time-series data, enhancing understanding of temporal patterns, while statistical features enhance predictive capabilities. Principal Component Analysis (PCA) enhances convergence and integrates diverse time series features. As a result, this novel integrated approach improves the estimation of spatiotemporal probabilities of emergency events, effectively addressing data sparsity challenges. This framework adapts effectively, predicting EMS zones and guiding system configuration. The model outperforms a Random Forest trained solely on time-series data, boosting accuracy by up to 26.9 % in Barranquilla's case study zones, with a mean improvement of 16.4 %. Accuracy improvement makes the model helpful in assisting city authorities in ambulance location/relocation and dispatching decisions. |
| format | Article |
| id | doaj-art-cd005b31153840f7a2250c0a51207f0b |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-cd005b31153840f7a2250c0a51207f0b2025-02-02T05:28:31ZengElsevierHeliyon2405-84402025-01-01112e41904A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, ColombiaDionicio Neira-Rodado0Juan Camilo Paz-Roa1John Willmer Escobar2Miguel Ángel Ortiz-Barrios3School of Industrial Engineering, Universidad del Valle, Cali, 760008, Colombia; Department of Productivity and Innovation, Universidad de la Costa, Barranquilla, 080002, ColombiaDepartment of Civil and Industrial Engineering, Pontificia Universidad Javeriana Cali, Cali, 760008, ColombiaSchool of Industrial Engineering, Universidad del Valle, Cali, 760008, Colombia; Corresponding author.Department of Productivity and Innovation, Universidad de la Costa, Barranquilla, 080002, ColombiaAnticipating the timing and location of future emergency calls is crucial for making informed decisions about vehicle location and relocation, ultimately reducing response times and enhancing service quality. A predictive model for EMS (Emergency Medical Services) events is proposed to address this need. The proposed spatiotemporal approach integrates machine learning, signal analysis, and statistical features, capturing geographical, temporal, and event-specific factors. The model identifies patterns associated with the occurrence or absence of emergency calls, using clustering techniques for demand spatial splitting and then training an XGBoost model on the multivariate time series. The model uses signal analysis to extract valuable insights from time-series data, enhancing understanding of temporal patterns, while statistical features enhance predictive capabilities. Principal Component Analysis (PCA) enhances convergence and integrates diverse time series features. As a result, this novel integrated approach improves the estimation of spatiotemporal probabilities of emergency events, effectively addressing data sparsity challenges. This framework adapts effectively, predicting EMS zones and guiding system configuration. The model outperforms a Random Forest trained solely on time-series data, boosting accuracy by up to 26.9 % in Barranquilla's case study zones, with a mean improvement of 16.4 %. Accuracy improvement makes the model helpful in assisting city authorities in ambulance location/relocation and dispatching decisions.http://www.sciencedirect.com/science/article/pii/S2405844025002841PCA (principal component analysis)ClusteringXGboostSignal processingStatistical featuresSpatiotemporal classification |
| spellingShingle | Dionicio Neira-Rodado Juan Camilo Paz-Roa John Willmer Escobar Miguel Ángel Ortiz-Barrios A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia Heliyon PCA (principal component analysis) Clustering XGboost Signal processing Statistical features Spatiotemporal classification |
| title | A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia |
| title_full | A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia |
| title_fullStr | A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia |
| title_full_unstemmed | A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia |
| title_short | A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia |
| title_sort | novel machine learning approach for spatiotemporal prediction of ems events a case study from barranquilla colombia |
| topic | PCA (principal component analysis) Clustering XGboost Signal processing Statistical features Spatiotemporal classification |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025002841 |
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