本文關鍵詞：癥狀監(jiān)測預警數(shù)據(jù)分析及方法研究　出處：《昆明理工大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 癥狀監(jiān)測 CUSUM控制圖 馬爾可夫鏈 ATS

【摘要】：癥狀監(jiān)測對疾病預防具有日益顯著的前置性優(yōu)勢,能夠在大規(guī)模疾病爆發(fā)之前感知到異常趨勢,為醫(yī)療衛(wèi)生預防工作留有更加充足的準備時間。近年來,癥狀監(jiān)測作為一種全新的監(jiān)測手段,正越來越多地引起人們的關注。但是現(xiàn)有的疾病監(jiān)測方式有嚴重的滯后性,在信息錄入形式上的便捷性及信息化程度低,且采集到的樣本信息沒有準確的數(shù)據(jù)處理方式及預警分析方法。因此,為了建立可行的癥狀監(jiān)測預警體系,實現(xiàn)對重點區(qū)域的實時監(jiān)測和數(shù)據(jù)分析,本文應用準確的預警模型及大數(shù)據(jù)關聯(lián)信息挖掘方法對監(jiān)測數(shù)據(jù)進行數(shù)據(jù)分析、處理和預警響應,得到快速、準確的預警結果。論文由幾個部分構成:(1)采用專家評價法篩選設計了癥狀監(jiān)測系統(tǒng)的數(shù)據(jù)源,并運用臟數(shù)據(jù)清洗的方式對采集數(shù)據(jù)進行粗略的預處理。(2)從似然比的角度設計了休哈特控制圖,基于其對數(shù)據(jù)中小偏移監(jiān)測不靈敏的特點提出了累積和控制圖,并完成了監(jiān)控方差的CUSUM控制圖模型設計,同時提出用馬爾可夫鏈的方式計算控制圖參數(shù)組合下的ATS和ARL值,用以對比模型的靈敏度。(3)設計了改進型的具有變動抽樣區(qū)間的動態(tài)CUSUM控制圖,通過ATS及ANSS值的比較得出動態(tài)控制圖具有更好的檢出效果,并提出了差異率的概念,對比后得出不同參數(shù)組合下動態(tài)控制圖都具有優(yōu)于靜態(tài)控制圖的預警效果。(4)將監(jiān)控方差的變動抽樣區(qū)間的CUSUM控制圖用于癥狀監(jiān)測采樣數(shù)據(jù),使用灰色關聯(lián)為癥狀數(shù)據(jù)加權分析,并人為設定異常點,改變采樣數(shù)據(jù)的方差,得出動態(tài)控制圖具有準確度高、靈敏度高的檢出效果。論文采用馬氏鏈的方法計算相應的ATS及差異率,得出采用長短抽樣區(qū)間進行控制圖設計具有更好監(jiān)測效果的結論,并將其應用于癥狀監(jiān)測預警系統(tǒng)之中,有效的提高了報警靈敏度,降低了報警時間,提高了監(jiān)測效率。
[Abstract]:Symptom monitoring has an increasingly significant predominance in disease prevention, and it can perceive abnormal trends before large-scale disease outbreaks, leaving more adequate preparation time for medical and health prevention work in recent years. As a new monitoring method, symptom monitoring is attracting more and more attention. However, the existing disease surveillance methods have serious lag, and the convenience and information level of information entry is low. And the collected sample information has no accurate data processing and early warning analysis method. Therefore, in order to establish a feasible symptom monitoring and early warning system, real-time monitoring and data analysis of key areas can be realized. In this paper, the accurate early warning model and big data correlation information mining method are applied to the monitoring data analysis, processing and early warning response, and get fast. The paper is composed of several parts. (1) the data source of the symptom monitoring system is designed by the expert evaluation method. And using dirty data cleaning method to collect data rough preprocessing. 2) from the angle of likelihood ratio design the control chart of Shewhart. Based on its insensitivity to small and medium offset monitoring, the cumulative and control charts are proposed, and the CUSUM control chart model of monitoring variance is designed. At the same time, the method of Markov chain is used to calculate the values of ATS and ARL under the control chart parameter combination. A modified dynamic CUSUM control chart with variable sampling interval is designed to compare the sensitivity of the model. Through the comparison of ATS and ANSS, it is concluded that the dynamic control chart has better detection effect, and the concept of difference rate is put forward. After comparison, it is concluded that the dynamic control chart with different parameters combination has better warning effect than static control chart.) the CUSUM control chart of the variable sampling interval of the monitoring variance is applied to the symptom monitoring sampling data. Grey correlation is used as the weighted analysis of symptom data, and the abnormal points are set artificially to change the variance of the sampling data, and the dynamic control chart is obtained with high accuracy. The method of Markov chain is used to calculate the corresponding ATS and the difference rate. It is concluded that the control chart design with long and short sampling interval has better monitoring effect. It is applied to the symptom monitoring and warning system, which effectively improves the alarm sensitivity, reduces the alarm time and improves the monitoring efficiency.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R181.8;TP277

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本文編號：1420471