基于Matlab智能算法的GERD中药治疗方案优选
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After the diagnosis, then? Where is the corresponding treatments?
What is ANN?
?
Artificial neural networks (ANNs), a form of connectionism, are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the analytic results to identify cats in other images. They have found most use in applications difficult to express in a traditional computer algorithm using rulebased programming.
Why we need?
1. AI challenges increased in human society, including those traditional fields; 2. The idea of TCM has big possibility to be simulated by ANN. 3. The development of some toolbox make it easier to make research into this field. 4. Matlab is a good tool for Simulink and explore the possibility of “Treatment based on symptoms recognition”.
somehow like TCM evaluation • Possible to be prescribed by machine based on study • ANN is an important way to realize that
Rough set used for the diagnosis of GERD
0.2273 0.2727 0.1364 0.1818 0.0455 0.1818 0.1818 0.1818 0.3636 0.0000 0.0909 0.0000 0.0909 0.0455 0.0000
0.5455 0.4545 0.3636 0.5455 0.0909 0.3636 0.3636 0.4545 0.5909 0.0909 0.0455 0.0909 0.2727 0.0455 0.0000
Symptoms set
S1=[1 1 1 1 0 0 0 0 0 0 0 0];
S2=[1 1 1 0 0 1 0 0 0 0 0 0];
S3=[1 1 1 0 0 0 0 1 0 0 0 0];
D1=[1 1 1 1 1 1 0 1 0 1 0 1];
D2=[1 1 1 1 1 1 0 1 0 1 0 1];
Symptoms and Herbs Sets
Sss纳d47a1===y差呕胸陈s1，吐痞=皮中s，，，11酸=ssd胁58，2===胸呕痛甘s2痹逆=，草嘈， ，s，1杂2ss=d69，嗳==3=梅 胃s白气3核 痞=术。反气 ，，药胃s，1物，0= ddd471==0=半 人吴夏参萸，，，ddd58==11苍生=砂术姜仁，，，dd69d==1茯 神2=苓 曲黄， ，连。 Composition of any four symptoms: MatLab program: combntns([1,2,3,4,5,6,7,8,9,10,11, 12],4)
Programed Realization of TCM D&T
对于一种疾病，首先要通过大量学习样本的 研究，得出哪些为该病的主要症状及证型， 然后通过Rough集分析，进行属性集的约简、 属性值的约简，导出决策规则，这就是“辨 证”的过程；在此基础上，广泛学习古今各 种文献，建立数据库，并通过前述“记忆权 数”矩阵，学习相关“论治”规则，通过神 经网络的训练与泛化，建立ANN模型，这就 是“论治”的过程。然后编写程式，通过人 机交互界面，完成机器“辨证论治”的自动 过程，这也许是新一代中医专家系统的一个 理想模式。
The patient has｛反酸，胃痞，纳差，嗳气｝symptoms，so the symptoms set would be：
S4=[1 0 0 0 0 0 1 0 1 0 0 1]'; D4=sim(net,S4)
结果显示应该采用的药物为｛陈皮，半夏，苍术，黄连｝， 基本勾勒出了苍连丸（见《古今医鉴》卷五）的主要成份。 苍连丸由苍术、陈皮、半夏、黄连、茯苓、吴萸六味药组 成，用治郁积吞酸。患者在反酸的同时症见胃痞、纳差、 嗳气，恰为郁积的表现，因此基本符合苍连丸的应用指征。
Case Test 2
Patient 2 has｛嘈杂，胸痹，胁痛，嗳气｝symptoms，the symptoms set would be: S5=[0 1 0 0 1 0 0 0 0 0 1 1]’; D5=sim(net,S5) 据其输出结果，可选用的药物为｛陈皮，甘草，白术，半夏，苍术，茯苓，生姜，吴萸， 黄连｝，基本包含了二陈、左金等对治之剂，能够较好地体现中医的辨证论治思想。 上述过程的输出层也可应用硬限幅函数“hardlim”，则结果将以“0”或“1”的二值形 式出现，0表示该药不选用，1表示选用该药。
X=
0.5909 0.3636 0.2727 0.3636 0.3636 0.1818 0.4091 0.3182 0.3636 0.3182 0.3636 0.0909 0.3182 0.2273 0.1364
0.2273 0.1364 0.2273 0.2273 0.0909 0.0000 0.0909 0.0909 0.2727 0.1364 0.0455 0.0000 0.0000 0.2273 0.0455
0.0909 0.0909 0.0000 0.1364 0.0455 0.0000 0.0455 0.0455 0.1364 0.0455 0.0455 0.0455 0.0000 0.0000 0.0000
0.0455 0.1818 0.0000 0.0455 0.0000 0.0000 0.1364 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.5455 0.6818 0.4545 0.2727 0.0909 0.1818 0.4545 0.4545 0.3636 0.2273 0.0455 0.1818 0.3182 0.0455 0.0455
0.1818 0.2273 0.0455 0.2273 0.0909 0.2273 0.0909 0.1364 0.1818 0.0000 0.0909 0.0455 0.2273 0.1364 0.0000
0.5909 0.3636 0.3636 0.3182 0.0909 0.0455 0.1364 0.1818 0.2273 0.1364 0.0909 0.2273 0.0909 0.7727 0.0909
1.0000 0.6818 0.5000 0.3636 0.3182 0.0909 0.2727 0.4545 0.2273 0.2273 0.0000 0.1818 0.1818 0.0909 0.1364
D3=[1 1 1 1 1 1 0 1 0 1 0 1];
S=[S1;S2;S3]';
D=[D1;D2;D3]'; net=newff([0,1;0,1;0,1;0,1;0,1;0,1;0, 1;0,1;0,1;0,1;0,1;0,1],[5,3,12],{'tansi g','tansig','tansig'});
Human Training and Machine Study
1. Different Study curve 2. Different Capacity of study 3. Different precision of acquiring knowledge 4. Different utilization power
基于Matlab智能算法的胃食管返流病 中药治疗方案优选
About GERD
• High Incidence Rate (5.2%-8.5% in east Asia after 2005) • Three sub types • Acid reflux and heartburn • Loss of control by PPIs • Herbs are a good alternative choice • Good to be evaluated by symptom scores, which is
BP network
Training and Simulink
Train function for training and Sim function for Simulink:
After around 3090 paces, the error is almost approaching 0
Case Test 1
a3=f3(LW3,2f2(LW2,1f1(IW1,1p＋b1)＋b2)＋b3)=y
Correspondance Bwteen Symptoms and Herbs
D1
D2
…
Dn
S1
S1
…
Sm
Frequency normalized
[0,1]
（x－min）/(max－min)
Memory Weight Matrix
0.t matrix
Lower threshold of selection
S1:0.3636;S2:0.2273;S3:0.2182;S4:0.5272;S5:0.0909;S6:0.0455 ;S7:0.4545;S8:0.2273;S9:0.3636;S10:0.4909;S11:0.0455;S12:0.0818。
0.0909 0.0909 0.0000 0.0000 0.0455 0.0000 0.0455 0.0455 0.0000 0.0455 0.0455 0.0000 0.0000 0.0455 0.0000
0.0909 0.0909 0.0000 0.0455 0.0909
0.0000 0.0455 0.0455 0.0455 0.0000 0.0455 0.0455
What is ANN?
?
Artificial neural networks (ANNs), a form of connectionism, are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the analytic results to identify cats in other images. They have found most use in applications difficult to express in a traditional computer algorithm using rulebased programming.
Why we need?
1. AI challenges increased in human society, including those traditional fields; 2. The idea of TCM has big possibility to be simulated by ANN. 3. The development of some toolbox make it easier to make research into this field. 4. Matlab is a good tool for Simulink and explore the possibility of “Treatment based on symptoms recognition”.
somehow like TCM evaluation • Possible to be prescribed by machine based on study • ANN is an important way to realize that
Rough set used for the diagnosis of GERD
0.2273 0.2727 0.1364 0.1818 0.0455 0.1818 0.1818 0.1818 0.3636 0.0000 0.0909 0.0000 0.0909 0.0455 0.0000
0.5455 0.4545 0.3636 0.5455 0.0909 0.3636 0.3636 0.4545 0.5909 0.0909 0.0455 0.0909 0.2727 0.0455 0.0000
Symptoms set
S1=[1 1 1 1 0 0 0 0 0 0 0 0];
S2=[1 1 1 0 0 1 0 0 0 0 0 0];
S3=[1 1 1 0 0 0 0 1 0 0 0 0];
D1=[1 1 1 1 1 1 0 1 0 1 0 1];
D2=[1 1 1 1 1 1 0 1 0 1 0 1];
Symptoms and Herbs Sets
Sss纳d47a1===y差呕胸陈s1，吐痞=皮中s，，，11酸=ssd胁58，2===胸呕痛甘s2痹逆=，草嘈， ，s，1杂2ss=d69，嗳==3=梅 胃s白气3核 痞=术。反气 ，，药胃s，1物，0= ddd471==0=半 人吴夏参萸，，，ddd58==11苍生=砂术姜仁，，，dd69d==1茯 神2=苓 曲黄， ，连。 Composition of any four symptoms: MatLab program: combntns([1,2,3,4,5,6,7,8,9,10,11, 12],4)
Programed Realization of TCM D&T
对于一种疾病，首先要通过大量学习样本的 研究，得出哪些为该病的主要症状及证型， 然后通过Rough集分析，进行属性集的约简、 属性值的约简，导出决策规则，这就是“辨 证”的过程；在此基础上，广泛学习古今各 种文献，建立数据库，并通过前述“记忆权 数”矩阵，学习相关“论治”规则，通过神 经网络的训练与泛化，建立ANN模型，这就 是“论治”的过程。然后编写程式，通过人 机交互界面，完成机器“辨证论治”的自动 过程，这也许是新一代中医专家系统的一个 理想模式。
The patient has｛反酸，胃痞，纳差，嗳气｝symptoms，so the symptoms set would be：
S4=[1 0 0 0 0 0 1 0 1 0 0 1]'; D4=sim(net,S4)
结果显示应该采用的药物为｛陈皮，半夏，苍术，黄连｝， 基本勾勒出了苍连丸（见《古今医鉴》卷五）的主要成份。 苍连丸由苍术、陈皮、半夏、黄连、茯苓、吴萸六味药组 成，用治郁积吞酸。患者在反酸的同时症见胃痞、纳差、 嗳气，恰为郁积的表现，因此基本符合苍连丸的应用指征。
Case Test 2
Patient 2 has｛嘈杂，胸痹，胁痛，嗳气｝symptoms，the symptoms set would be: S5=[0 1 0 0 1 0 0 0 0 0 1 1]’; D5=sim(net,S5) 据其输出结果，可选用的药物为｛陈皮，甘草，白术，半夏，苍术，茯苓，生姜，吴萸， 黄连｝，基本包含了二陈、左金等对治之剂，能够较好地体现中医的辨证论治思想。 上述过程的输出层也可应用硬限幅函数“hardlim”，则结果将以“0”或“1”的二值形 式出现，0表示该药不选用，1表示选用该药。
X=
0.5909 0.3636 0.2727 0.3636 0.3636 0.1818 0.4091 0.3182 0.3636 0.3182 0.3636 0.0909 0.3182 0.2273 0.1364
0.2273 0.1364 0.2273 0.2273 0.0909 0.0000 0.0909 0.0909 0.2727 0.1364 0.0455 0.0000 0.0000 0.2273 0.0455
0.0909 0.0909 0.0000 0.1364 0.0455 0.0000 0.0455 0.0455 0.1364 0.0455 0.0455 0.0455 0.0000 0.0000 0.0000
0.0455 0.1818 0.0000 0.0455 0.0000 0.0000 0.1364 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.5455 0.6818 0.4545 0.2727 0.0909 0.1818 0.4545 0.4545 0.3636 0.2273 0.0455 0.1818 0.3182 0.0455 0.0455
0.1818 0.2273 0.0455 0.2273 0.0909 0.2273 0.0909 0.1364 0.1818 0.0000 0.0909 0.0455 0.2273 0.1364 0.0000
0.5909 0.3636 0.3636 0.3182 0.0909 0.0455 0.1364 0.1818 0.2273 0.1364 0.0909 0.2273 0.0909 0.7727 0.0909
1.0000 0.6818 0.5000 0.3636 0.3182 0.0909 0.2727 0.4545 0.2273 0.2273 0.0000 0.1818 0.1818 0.0909 0.1364
D3=[1 1 1 1 1 1 0 1 0 1 0 1];
S=[S1;S2;S3]';
D=[D1;D2;D3]'; net=newff([0,1;0,1;0,1;0,1;0,1;0,1;0, 1;0,1;0,1;0,1;0,1;0,1],[5,3,12],{'tansi g','tansig','tansig'});
Human Training and Machine Study
1. Different Study curve 2. Different Capacity of study 3. Different precision of acquiring knowledge 4. Different utilization power
基于Matlab智能算法的胃食管返流病 中药治疗方案优选
About GERD
• High Incidence Rate (5.2%-8.5% in east Asia after 2005) • Three sub types • Acid reflux and heartburn • Loss of control by PPIs • Herbs are a good alternative choice • Good to be evaluated by symptom scores, which is
BP network
Training and Simulink
Train function for training and Sim function for Simulink:
After around 3090 paces, the error is almost approaching 0
Case Test 1
a3=f3(LW3,2f2(LW2,1f1(IW1,1p＋b1)＋b2)＋b3)=y
Correspondance Bwteen Symptoms and Herbs
D1
D2
…
Dn
S1
S1
…
Sm
Frequency normalized
[0,1]
（x－min）/(max－min)
Memory Weight Matrix
0.t matrix
Lower threshold of selection
S1:0.3636;S2:0.2273;S3:0.2182;S4:0.5272;S5:0.0909;S6:0.0455 ;S7:0.4545;S8:0.2273;S9:0.3636;S10:0.4909;S11:0.0455;S12:0.0818。
0.0909 0.0909 0.0000 0.0000 0.0455 0.0000 0.0455 0.0455 0.0000 0.0455 0.0455 0.0000 0.0000 0.0455 0.0000
0.0909 0.0909 0.0000 0.0455 0.0909
0.0000 0.0455 0.0455 0.0455 0.0000 0.0455 0.0455