本文選題：體檢數(shù)據(jù) + 預(yù)處理��； 參考：《計(jì)算機(jī)應(yīng)用研究》2017年04期

【摘要】：原始體檢數(shù)據(jù)存在信息模糊、有噪聲、不完整和冗余的問(wèn)題,無(wú)法直接用于疾病的風(fēng)險(xiǎn)評(píng)估與預(yù)測(cè)。由于體檢數(shù)據(jù)在結(jié)構(gòu)和格式等方面的不足,不適合采用傳統(tǒng)的數(shù)據(jù)預(yù)處理方法。為了充分挖掘體檢數(shù)據(jù)中有價(jià)值的信息,從多角度提出了針對(duì)體檢數(shù)據(jù)的預(yù)處理方法:通過(guò)基于壓縮方法的數(shù)據(jù)歸約,降低了體檢數(shù)據(jù)預(yù)處理的時(shí)間及空間復(fù)雜度;通過(guò)基于分詞和權(quán)值的字段匹配算法,完成了體檢數(shù)據(jù)的清洗,解決了體檢數(shù)據(jù)不一致的問(wèn)題;通過(guò)基于線性函數(shù)的數(shù)據(jù)變換,實(shí)現(xiàn)了歷年體檢數(shù)據(jù)的一致性和連續(xù)性。實(shí)驗(yàn)結(jié)果表明,基于分詞和權(quán)值的字段匹配算法,相對(duì)于傳統(tǒng)算法具有更高的準(zhǔn)確性。
[Abstract]:The original physical examination data has some problems such as fuzzy information, noise, incomplete and redundancy, which can not be directly used for disease risk assessment and prediction. Because of the deficiency of the structure and format of the physical examination data, it is not suitable to adopt the traditional data preprocessing method. In order to fully mine the valuable information in the physical examination data, the preprocessing method for the physical examination data is put forward from many angles: by reducing the data based on the compression method, the time and space complexity of the medical examination data preprocessing are reduced; Through the field matching algorithm based on word segmentation and weight value, the cleaning of medical examination data is completed, and the problem of inconsistent medical examination data is solved, and the consistency and continuity of medical examination data over the years are realized through the data transformation based on linear function. The experimental results show that the field matching algorithm based on word segmentation and weight is more accurate than the traditional algorithm.
【作者單位】： 鄭州大學(xué)互聯(lián)網(wǎng)醫(yī)療與健康服務(wù)河南省協(xié)同創(chuàng)新中心;鄭州大學(xué)軟件與應(yīng)用科技學(xué)院;鄭州大學(xué)信息工程學(xué)院;
【基金】：河南省重點(diǎn)科技攻關(guān)項(xiàng)目(152102210249)
【分類號(hào)】：TP311.13
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本文編號(hào)：1810115