中华肝脏病杂志
中華肝髒病雜誌
중화간장병잡지
CHINESE JOURNAL OF HEPATOLOGY
2013年
4期
304-307
,共4页
肝疾病,酒精性%诊断%肝功能试验%神经网络(计算机)
肝疾病,酒精性%診斷%肝功能試驗%神經網絡(計算機)
간질병,주정성%진단%간공능시험%신경망락(계산궤)
Liver diseases,alcoholic%Diagnosis%Liver function tests%Neural network (computer)
目的 探讨广义回归神经网络(GRNN)模型在酒精性肝病(ALD)诊断中的可行性.方法 建立ALD诊断的GRNN模型,其输入层7个指标分别为y-谷酰氨转移酶、总胆汁酸、碱性磷酸酶、总胆红素、ALT、AST及AST/ALT值,输出层3个指标分别为酒精性肝硬化肝功能失代偿期、酒精性肝硬化肝功能代偿期和酒精性肝炎.收集湘雅二医院确诊并分类的135例患者,选取其中120例为模型训练样本,另外15例作为待诊断样本；同时,收集文献发表的40例酒精性肝炎患者的临床数据,以其中34例为模型训练样本,6例作为待诊断样本.运用GRNN方法对其进行运算与诊断,检验GRNN模型的诊断结果与临床诊断结果的符合率.结果 用建立的GRNN模型分别对120例和34例模型训练样本进行回判诊断,其诊断结果与临床诊断结果的符合率分别为100.00％和94.12％；对15例和6例待诊断样本进行诊断,符合率均为100.00％.结论 GRNN模型作为 临床诊断多指标综合化的方法,对ALD的诊断有较高的准确率.
目的 探討廣義迴歸神經網絡(GRNN)模型在酒精性肝病(ALD)診斷中的可行性.方法 建立ALD診斷的GRNN模型,其輸入層7箇指標分彆為y-穀酰氨轉移酶、總膽汁痠、堿性燐痠酶、總膽紅素、ALT、AST及AST/ALT值,輸齣層3箇指標分彆為酒精性肝硬化肝功能失代償期、酒精性肝硬化肝功能代償期和酒精性肝炎.收集湘雅二醫院確診併分類的135例患者,選取其中120例為模型訓練樣本,另外15例作為待診斷樣本；同時,收集文獻髮錶的40例酒精性肝炎患者的臨床數據,以其中34例為模型訓練樣本,6例作為待診斷樣本.運用GRNN方法對其進行運算與診斷,檢驗GRNN模型的診斷結果與臨床診斷結果的符閤率.結果 用建立的GRNN模型分彆對120例和34例模型訓練樣本進行迴判診斷,其診斷結果與臨床診斷結果的符閤率分彆為100.00％和94.12％；對15例和6例待診斷樣本進行診斷,符閤率均為100.00％.結論 GRNN模型作為 臨床診斷多指標綜閤化的方法,對ALD的診斷有較高的準確率.
목적 탐토엄의회귀신경망락(GRNN)모형재주정성간병(ALD)진단중적가행성.방법 건립ALD진단적GRNN모형,기수입층7개지표분별위y-곡선안전이매、총담즙산、감성린산매、총담홍소、ALT、AST급AST/ALT치,수출층3개지표분별위주정성간경화간공능실대상기、주정성간경화간공능대상기화주정성간염.수집상아이의원학진병분류적135례환자,선취기중120례위모형훈련양본,령외15례작위대진단양본；동시,수집문헌발표적40례주정성간염환자적림상수거,이기중34례위모형훈련양본,6례작위대진단양본.운용GRNN방법대기진행운산여진단,검험GRNN모형적진단결과여림상진단결과적부합솔.결과 용건립적GRNN모형분별대120례화34례모형훈련양본진행회판진단,기진단결과여림상진단결과적부합솔분별위100.00％화94.12％；대15례화6례대진단양본진행진단,부합솔균위100.00％.결론 GRNN모형작위 림상진단다지표종합화적방법,대ALD적진단유교고적준학솔.
Objective To study the feasibility and rationale of using a generalized regression neural network model integrated with multiple disease indicators for diagnosing alcoholic liver disease (ALD).Methods ALD indicators were identified by reviewing the clinical testing results of 40 ALD patients from the literature and 135 patients from the Second Xiangya Hospital of Central South University,who were also classified by physician experts upon clinical consultation.Seven indicators were selected as diagnosis indexes and applied to a general regression neural network diagnostic model.Thirty-four of the reported patients and 120 of the clinical patients were selected for use as training samples to establish the indicator recognition pattem for the model,and the remaining six and 15 patients from the two respective groups were selected for use as testing samples to determine the model's diagnostic ability.Results The model provided a correct diagnosis of ALD sub-classification for 94.1％ (32/34) of the reported patients and 100％ (120/120) of the clinical patients in the training set.The correct diagnosis rates achieved with the training sets were 100％ for both the reported patient group (6/6) and the clinical patient group (15/15),indicating that the results of the diagnostic model were in good agreement with the ALD classifications generated by the clinical expert consultations.Conclusion The general regression neural network model based on multiple indicators of ALD is capable of providing accurate and comprehensive diagnosis of ALD and may be feasible for clinical applications.
