Data Analysis of Out-of-Hospital Cardiac Arrest
Date Issued
2014
Date
2014
Author(s)
Chien, Chia-Ling
Abstract
Out-of-Hospital Cardiac Arrest (OHCA) is defined as the patients before they were taken to the hospital, breathing, heartbeat have stopped. Whether it’s related indicators in Taiwan or abroad, is an important foundation of today''s emergency medical service. The current literatures on the OHCA mostly use only the traditional method of Utstein Style, but it has some limitations. Therefore, how to pick out the really important factor and to clarify its complicated causal relationship has not been validated in New Taipei City OHCA database. This study used 11,010 cases between 2010 and 2013 for data analysis.
The study is divided into two parts: Epidemiological Analysis and Data Mining Analysis. First, we used Chi-square test and Odds Ratios to analyze the relationship between various factors and survival rate, and further established OHCA Geography information systems, spatial data and timeline analysis provide a quick check of medical personnel to visualize data distribution platform. Second, breaking the traditional way, we used Bayesian network technology to explore the necessity of the intervention. The results show that under non-trauma and non-shockable people, Advanced Life Support relative to Basic Life Support for survival rate, the odds ratio is 1.19 and 1.25 times. And in the handling time, run time, total time, when the time is the longest, the overall odds ratio can be as high as 1.50 times more, which means that the longer the time, the implementation of ALS services has more significant effect and necessity.
The study is divided into two parts: Epidemiological Analysis and Data Mining Analysis. First, we used Chi-square test and Odds Ratios to analyze the relationship between various factors and survival rate, and further established OHCA Geography information systems, spatial data and timeline analysis provide a quick check of medical personnel to visualize data distribution platform. Second, breaking the traditional way, we used Bayesian network technology to explore the necessity of the intervention. The results show that under non-trauma and non-shockable people, Advanced Life Support relative to Basic Life Support for survival rate, the odds ratio is 1.19 and 1.25 times. And in the handling time, run time, total time, when the time is the longest, the overall odds ratio can be as high as 1.50 times more, which means that the longer the time, the implementation of ALS services has more significant effect and necessity.
Subjects
緊急醫療救護
地理資訊
貝氏網路
到院前心肺功能停止
Type
thesis
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