Name
Title | ||
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Spatial analysis and data mining techniques for identifying risk factors of Out-of-Hospital Cardiac Arrest. |
Abstract | ||
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Out-of-Hospital Cardiac Arrest (OHCA) is a critical issue of emergency medical service (EMS).Ubiquitous computing technologies could significantly improve the survival rate of OHCA patients.Public health institutions should manage first-aid resources more efficiently and make EMS policies more effectively.New Taipei City, Taiwan was chosen as the scope of this study. Spatial Analysis and Data Mining Techniques were used.Spatial clustering of OHCA events was found. Risk factors to 2-hour survival rate after OHCA were identified. Out-of-Hospital Cardiac Arrest (OHCA) is a critical issue of emergency medical service (EMS). In addition to the first aids given to OHCA patients by witnesses or bystanders, time factors such as arrival of ambulance and transportation from site to EMS are also important. Comprehensive coverage of EMS, especially enhanced by ubiquitous computing technologies, could significantly improve the survival rate of OHCA patients. However, it heavily challenges the resource allocation and management policy in the public health system of a metropolis. ObjectivesIn this study, we first used spatial analysis techniques with a finer granularity to identify high risk areas of OHCA in a metropolis. We then used data mining techniques to elucidate the effects of patients' characteristics, pre-hospital resuscitation treatments, and spatial factors on post-OHCA survivability. With this information, public health institutions can enhance the EMS by allocating properly first-aid resources at the right places to improve the survival rate of OHCA patients. MethodsWe used New Taipei City, Taiwan as the scope of this study. Data of all registered OHCA cases in New Taipei City in 2011 were reviewed retrospectively. The dataset was combined with the National Doorplate Database to enhance the granularity of spatial analyses. Global and local spatial analyses based on Global Moran's Index, Local Moran's Index, and Getis-Ord Gi* statistic were performed to cluster high risk districts for OHCA in New Taipei City. Statistical methods such as Chi-square test, logistic regression, and decision tree were then adopted to analyze factors influencing 2-h survivability after OHCA. ResultsSignificant spatial clustering of OHCA events was found (p |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.ijinfomgt.2016.04.008 | Int J. Information Management |
Keywords | Field | DocType |
Out-of-Hospital Cardiac Arrest (OHCA),Emergency medical service (EMS),Factor analysis,Spatial analysis,Data mining | Public health,Data mining,Survival rate,Automated external defibrillator,Engineering,Logistic regression,Return of spontaneous circulation,Drug administration | Journal |
Volume | Issue | ISSN |
37 | 1 | 0268-4012 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jui-Hung Kao | 1 | 0 | 1.01 |
| Ta-Chien Chan | 2 | 2 | 1.39 |
| Feipei Lai | 3 | 846 | 81.35 |
| Bo-Cheng Lin | 4 | 0 | 0.34 |
| Wei-Zen Sun | 5 | 8 | 5.51 |
| Kuan-Wu Chang | 6 | 0 | 0.34 |
| Fang-Yie Leu | 7 | 383 | 82.72 |
| Jeng-Wei Lin | 8 | 35 | 7.52 |