Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time
Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/26/3/56 |
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| author | Héber H. Arcolezi Selene Cerna Christophe Guyeux Jean-François Couchot |
| author_facet | Héber H. Arcolezi Selene Cerna Christophe Guyeux Jean-François Couchot |
| author_sort | Héber H. Arcolezi |
| collection | DOAJ |
| description | Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is <i>sensitive data</i> because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example. |
| first_indexed | 2024-03-10T07:27:49Z |
| format | Article |
| id | doaj.art-346777d4d0874710b25d34b59ae05e89 |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-10T07:27:49Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj.art-346777d4d0874710b25d34b59ae05e892023-11-22T14:07:09ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472021-08-012635610.3390/mca26030056Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response TimeHéber H. Arcolezi0Selene Cerna1Christophe Guyeux2Jean-François Couchot3FEMTO-ST Institute, UMR 6174 CNRS, Université Bourgogne Franche-Comté (UBFC), 90000 Belfort, FranceFEMTO-ST Institute, UMR 6174 CNRS, Université Bourgogne Franche-Comté (UBFC), 90000 Belfort, FranceFEMTO-ST Institute, UMR 6174 CNRS, Université Bourgogne Franche-Comté (UBFC), 90000 Belfort, FranceFEMTO-ST Institute, UMR 6174 CNRS, Université Bourgogne Franche-Comté (UBFC), 90000 Belfort, FranceEmergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is <i>sensitive data</i> because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.https://www.mdpi.com/2297-8747/26/3/56emergency medical servicesemergency medicinedecision-support systempre-hospital emergency careambulance response timemachine learning |
| spellingShingle | Héber H. Arcolezi Selene Cerna Christophe Guyeux Jean-François Couchot Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time Mathematical and Computational Applications emergency medical services emergency medicine decision-support system pre-hospital emergency care ambulance response time machine learning |
| title | Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time |
| title_full | Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time |
| title_fullStr | Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time |
| title_full_unstemmed | Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time |
| title_short | Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time |
| title_sort | preserving geo indistinguishability of the emergency scene to predict ambulance response time |
| topic | emergency medical services emergency medicine decision-support system pre-hospital emergency care ambulance response time machine learning |
| url | https://www.mdpi.com/2297-8747/26/3/56 |
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