本文選題：體表特征　切入點(diǎn)：貝葉斯網(wǎng)絡(luò)　出處：《同濟(jì)大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年08期 　論文類型：期刊論文

【摘要】：基于歷史數(shù)據(jù)的統(tǒng)計(jì)和收集,選取骨盆骨折患者存在的18個(gè)體表特征,采用基于K2算法的貝葉斯網(wǎng)絡(luò)方法挖掘各體表特征之間和骨盆骨折類型與體表特征間的相互關(guān)系;設(shè)計(jì)不同的節(jié)點(diǎn)輸入策略,分析不同輸入策略對(duì)算法性能的影響;基于骨盆穩(wěn)定性將骨盆骨折分成A、B、C三種類型,分別找到與其直接相關(guān)的體表特征,作為判斷骨盆骨折類型的依據(jù).基于體表特征和骨盆骨折類型的分析結(jié)果,借助早期的觀察及簡(jiǎn)單檢查,對(duì)患者進(jìn)行初步分型.
[Abstract]:Based on the statistics and collection of historical data, 18 body surface features of pelvic fracture patients were selected, and Bayesian network method based on K2 algorithm was used to mine the relationships between the surface features and the types of pelvic fractures and body surface features. Different node input strategies were designed to analyze the effect of different input strategies on the performance of the algorithm. Pelvic fractures were classified into three types based on pelvic stability. Based on the analysis results of surface features and pelvic fracture types, early observation and simple examination were used to classify the patients.
【作者單位】： 同濟(jì)大學(xué)經(jīng)濟(jì)與管理學(xué)院;
【基金】：國家自然科學(xué)基金(71090404,71072026)
【分類號(hào)】：TP18
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本文編號(hào)：1637116