本文選題：呼吸系統(tǒng) + 氣象因子　； 參考：《蘭州大學(xué)學(xué)報(bào)(自然科學(xué)版)》2014年01期

【摘要】：利用北京市2009-2011年呼吸系統(tǒng)疾病急診就診人數(shù)資料和同期的氣象資料及污染資料,分析了氣象因素及污染物分別與上感、下感急診就診人數(shù)的相關(guān)性,在此基礎(chǔ)上,通過BP人工神經(jīng)網(wǎng)絡(luò)分別建立了上感和下感急診就診人數(shù)的預(yù)報(bào)模型,并對其效果進(jìn)行評價(jià).結(jié)果表明:氣象因素和污染物與上感、下感的發(fā)病有密切的關(guān)系;建立的上感、下感就診急診人數(shù)的神經(jīng)網(wǎng)絡(luò)預(yù)報(bào)模型結(jié)構(gòu)分別為13-7-1(即有13個(gè)輸入、7個(gè)隱含節(jié)點(diǎn)和1個(gè)輸出)和13-6-1(即有13個(gè)輸入、6個(gè)隱含節(jié)點(diǎn)和1個(gè)輸出),預(yù)測準(zhǔn)確率分別為77.11%和75.57%.與統(tǒng)計(jì)預(yù)報(bào)方法相比較,該方法計(jì)算簡便、誤差較小、預(yù)測準(zhǔn)確率高,對上感和下感急診人數(shù)有較好的預(yù)測效果,為醫(yī)療氣象預(yù)報(bào)提供了一種新方法,具有進(jìn)一步的研究價(jià)值.
[Abstract]:Based on the data of emergency attendance of respiratory diseases in Beijing from 2009 to 2011, meteorological data and pollution data of the same period, the correlation of meteorological factors and pollutants with the number of emergency patients with upper and lower sensations were analyzed. Based on BP artificial neural network, the prediction models of the number of emergency patients with upper and lower sensations were established, and the effects of the models were evaluated. The results show that meteorological factors and pollutants are closely related to the incidence of upper and lower sensations. The structure of neural network prediction model for emergency patients was 13-7-1 (13 inputs, 7 hidden nodes and 1 output) and 13-6-1 (13 inputs, 6 hidden nodes and 1 output). The prediction accuracy was 77.11% and 75.57%, respectively. Compared with the statistical forecasting method, this method has the advantages of simple calculation, small error and high prediction accuracy. It has a better prediction effect on the number of emergency patients with upper and lower senses. It provides a new method for medical weather forecast and has further research value.
【作者單位】： 蘭州大學(xué)大氣科學(xué)學(xué)院 甘肅省干旱氣候變化與減災(zāi)重點(diǎn)實(shí)驗(yàn)室;
【基金】：中央高校基本科研業(yè)務(wù)費(fèi)專項(xiàng)資金(自由探索)項(xiàng)目(LZUJBKY-2012-123) 國家自然科學(xué)基金項(xiàng)目(41075103) 公益性行業(yè)(氣象)科研專項(xiàng)項(xiàng)目(GYHY201106034)
【分類號(hào)】：R56

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