visualization system of herbal prescription effects in oriental medicine by self-organizing map

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IJBSCHS (2009-14-01-13) Biomedical Soft Computing and Human Sciences, Vol.14, No.1, pp.101-108 (2009)[Original article] Copyright1995 Biomedical Fuzzy Systems Association

(Accepted on 2008.10.3)

101

Visualization System of Herbal Prescription Effects in Oriental

Medicine by Self-Organizing Map

Thang CAO*, Katsuari KAMEI ** and Tuan Linh DANG ***

*Graduate School of Science and Engineering, Ritsumeikan University, Japan

** College of Information Science and Engineering, Ritsumeikan University, Japan

*** Hanoi University of Technology, Vietnam

(The paper was received on Nov. 1, 2007.)

Abstract. Fairness evaluating effects of prescriptions is indispensably importance in disease treatment. The

development of Intelligence Technologies nowadays makes it realizable for a moderation of subjective

prescription evaluations in medicine. In this paper, we present an application of Self-Organizing Map (SOM) tovisualization of herbal prescription effects in Oriental Medicine (OM). The application is used to estimate

influence of herbal ingredients prescribed for patients who have several concurrent diseases. Inputs to the SOM

are herbal treatment prescriptions, and an output is a map that shows disease states that can be influenced by

these prescriptions. Training data is 133 standard prescriptions with 221 popular herbs in 17 disease groups

collected from oriental medical text books by experienced doctors. First, the prescription problem in OM is

described. Next, the system structure and implementation of the proposed system are shown. Finally, the

visualization abilities of the proposed system verified by experienced doctors are presented.

Keywords: Self-Organizing Map, Neural Networks, Oriental Medicine

1. IntroductionIn oriental countries like China, Korea and

Vietnam, there are two medicine treatment systems:Oriental Medicine (OM) that has been used forthousands years and Western Medicines (WM) thatwas introduced since 19th century from westerncountries. Treatments in WM are based on drugcompounds and advanced equipment, whiletreatments in OM are based on herbal ingredients,acupuncture, physiotherapy, together with physiciansaccumulated experience. Although WM has beendeveloped quickly and gained many achievements intreating diseases, OM has been still an indispensablepart because it has fewer side-effects than WM, doesnot face antibiotic resistance problems and gives goodtreatment results. Besides, herbal prescriptions areeasy to find and relatively cheap in comparison withwestern drugs. Currently, the number of OM hospitalsin Vietnam is almost equal to that of WM hospitals,and the number of Vietnamese patients treated by OMis about 50% of the national population.

Evaluations of the effects of herbal treatment

prescriptions have an important role in OM treatment.If the evaluation is misjudged, the treatment prescription may have a bad influence on patientshealth conditions as well as on the treatment process.Successful realization of a decision support systemfor visualization of the herbal effects based onstandard prescriptions, popular treatment herbs andknowledge from experienced OM physicians willhelp to objectively verify evaluations of prescriptioneffects which tend to be subjective. It will indirectlyhelp physicians to provide the right treatment to theright patients while improving the quality of thehealth care services as a whole. It will also help to

reduce the prescription time, to increase positiveinfluence and reduce negative impact of the finaltreatment prescription, and to assist medical studentsor young physicians, especially those living andworking in remote areas. The visualization of effectsof treatment prescriptions can also help doctors togive patients an easily understandable explanationabout the treatment process.

From the last decades, the advent of thecomputer greatly stimulated developments ofArtificial Intelligence (AI), especially Expert Systems(ES) and Decision Support Systems (DSS) which play the roles of a specialist and assist people incarrying out works requiring specific expertise. Sincethe beginning of AI, ES and DSS have been

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Email: [email protected]


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successfully applied to WM, and then to OM withreasoning techniques including Neural Networks [4],Fuzzy Logic [5] and Uncertainty Reasoning [6-7].Most of ES and DSS in OM focused on problems ofdisease diagnoses and analyses with the specificcharacteristics of OM such as four inspection steps,

the six internal organs and the viscera, Yin and Yang.A few recent researches dealt with modifyingtreatment herbal prescriptions in OM using FuzzyLogic [8] and Multilayer Neural Networks [1-3].

In this paper, we present an application ofSelf-Organizing Map (SOM) to visualization ofherbal effects of oriental treatment prescriptions. Theapplication is used to assess effects of herbalingredients prescribed for patients who have severalconcurrent diseases. It is also used to investigatetreatment relations among standard prescriptions.Inputs to the SOM are herbal treatment prescriptions,and an output is a map that shows a distribution of

prescriptions with their treatment distances. From thelocation of a specific prescription on the map and itstreatment relations with other prescriptions, doctorscan easily estimate which disease states can beinfluenced by the specific prescription.

2. Prescriptions in OM

When prescribing herbal medicines for apatient based on severities of symptoms observed onthe patient, first doctors consider which disease statethe patient is infected, and then choose a standard prescription that is suitable cure for the infected

disease state. Second, the doctors adjust herbalingredients, add some catalyzing herbs to the standardprescription if necessary, and then adjust all of herbalamounts to make it suitable for the patient condition,so that the positive treatment effects of theprescription on the patient is maximized and negativeeffects is minimized [15]. Fig. 1 shows a generalprescribing procedure in OM.

The standard prescriptions for single diseasestates are easily found in medical books, and it is notso difficult for doctors to prescribe for a patient.However, there are no standard prescriptions forseveral concurrent diseases in such books because of

herbs properties that some herbs have good effectson a disease, but that they may have negativeinfluence on the other concurrent diseases. In somecases, some herbs and some diseases are exclusiveand such herbs should not be used for treating thosediseases.

When a patient has some diseases at thesame time, doctors have to carefully consider eachamong these concurrent diseases, give herbalingredients, clarify exclusive herbs, add some morecatalyzing herbs and then adjust amounts of all herbsto harmonize undesired effects, so that the final prescription that is the most suitable for the main

disease has positive influence and less side-effect onthe other concurrent diseases. In this case, thevisualization of herbal effects to the doctors is quite

important and mostly depends on their experience.Only experienced doctors can give patients suitable prescriptions with reasonable adjustments. Fig. 2illustrates the prescription process in case ofconcurrent diseases.

3. Neural Networks and Its Application toHerbal Prescription Learning

Neural Networks (NN) is an efficienttechnique to help doctors to analyze, model and makesense of complex clinical data across a broad range ofmedical applications [4]. It enables intelligentsystems to learn from experience, examples andclinical records, improving performance of thesystems over time. Based on knowledge accumulatedfrom experienced doctors and popular treatment prescriptions, NN can wisely give the doctors gooddecisions, helping to objectively verify subjective

evaluations in prescribing diseases.There are two main kinds of NN: supervisedand unsupervised NNs. In the supervised NN, thenetwork is provided a set of inputs and appropriateoutputs as a teacher for those inputs. A trainedsupervised NN can assist physicians giving herbaltreatment prescriptions with reasonable amounts [1-3].

symptoms

observedofseveritiesClarify

statesdiseaseinfectedConsider

onsprescripti

standardsuitableFind

ingredientherbalAdjust

onprescriptitreatmentGive

Fig. 1. The general prescribing procedure in OM

gramHerb

..........

gram1Herb

ONPRESCRIPTITREATMENT

zk

x

symptoms

observedofseveritiesClarify

diseasessecondary

andmaineClarify th

diseasemainfor the

cureherbssomeChoose

diseasesconcurrent

otherwith theherbs

selectedofeffectsConsider

diseasesconcurrentotherthe

curethatherbsmoresomeAdd

ingredienttheofeffects-sideand

herbsexclusiveagainConsider

herbsofamountsAdjust

Fig. 2. Prescription procedure for patients who have

several concurrent diseases

Unsupervised NN employs trainingalgorithms that do not make use of desired outputs.The NN itself learns to adapt based on the experience


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T. Cao, et al.: Visualization System of Herbal Prescription Effects in Oriental Medicine by Self-Organizing Map

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of the training patterns. The unsupervised NN is usedto discover structures in the data such as data withclusters or data with relationships among them, and tohelp to describe the data in a more compact way. Atypical method of unsupervised NN is Self-Organizing Map (SOM). The aim of SOM is to

automatically find a mapping from the space of inputvectors to a one- or two-dimensional space. Themapping preserves the closeness between data vectors,two close input vectors are mapped to points on theoutput map keeping the relationship in the input space[9].

The advantages of SOM are simple and easyto understand and good for visualization. Developerscan easily train the network and evaluate how wellthe training is conducted and how similar the objectsare. These advantages become more useful in case ofhigh dimensional and complex input data.

The disadvantage of SOM is distance

accuracy among input vectors. It is easy to see thedistribution of input vectors on the map, but it isdifficult to evaluate correctly distances andsimilarities between them. Moreover, if the outputdimension and learning algorithms are chosenimproperly, similar input vectors may not alwaysclose to each other and the trained network mayconverge to some local optima.

So far many useful SOM applications have been developed in many wide areas including themedicine domain [9-14].

4. Visualization System of Herbal Effects

by SOM

The effects of herbal treatment prescriptionsfor concurrent diseases can be suitably visualized by aSOM that are trained by standard herbal treatment prescriptions with standard herbal amounts. Fig. 3shows an overview of the visualization system ofherbal effects in OM by SOM.

In the prescription procedure described inFig. 2, it is easily to evaluate the influence of newtreatment prescriptions when the number of usedherbs and the number of concurrent infected diseasesare few. It becomes much more difficult when many

herbs are utilized and patients have many diseases atthe same time. In this case, the trained SOM can givedoctors a better view on relations among theprescribed herbal treatment prescriptions and diseasesthat may be cured and influenced by the newprescriptions.

4.1. Amounts and Treatment Effects of Herbs in

Prescriptions

There are no standard amounts of herbs forall treatment prescriptions though there is a properamount of herbs in standard prescriptions. An herb

may have different amounts in different standard prescriptions. The total amount of one prescription

may differ from the total amount of the otherprescriptions.

To make a standard for the amounts of herbsin prescriptions, we normalize the amounts of eachherb in a standard prescription as the percentage ofamount of the herb for total amount of all herbs in the

same prescription. Each herb in a prescription will beassigned a treatment importance that depends onproperties of the herb and treatment diseases.

Depending on the treatment purposes the prescriptions are divided into groups, e.g. HeartFailure Group, Artery Sclerosis Group, RheumatismGroup etc. Each prescription may belong to one orsome groups.

Lets suppose the number of prescriptions tobe n , the number of herbs to be m and the numberof disease groups to be l in the training data. An herb

iH has an amount of ia . In medical text books, a

standard prescriptionk

P is described by a pair of

herb names and their corresponding amounts such as:.),(),...,,( 11 mmk aHaHP

In our system, kP is represented by a

content vector kR defined by Eq. (1).

mHHkrrR ,...,

1= , nk ,...,1= (1)

=

=m

j

j

i

iH

a

ar

1

0.100(2)

where iHr is the rate of amount of the herb iH in kP

DATATRAINING

MapOrganizing-Self

gramHerb

..........

gram1Herb

onPrescriptiNew

zk

x

onDistributionPrescripti

Doctors

dExperienceEvaluate

onsprescriptiStandard

BooksReference

BooksTextMedical

onprescriptinewthe

byinfluencedbecandiseasesWhich-

isonsprescriptiamongclosenesstheHow-

mapon theonprescriptinewtheisWhere-

TIONRECOMMENDA

Fig. 3. An overview of visualization system of herbal

effects in OM by SOM


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In a prescription, the treatment importanceof herbs is also different. An herb may be the mostimportant in a certain prescription, but it may haveless important in other prescriptions. An herb maytake a small amount but it has more treatment effectthan the other herbs, or an herb may take much

amount, but it has just a harmony effect in the sameprescription. Normally in the prescription, an herb ismore important if it is used in most of prescriptions inthe same group.

The importanceiH

of the herb iH in a

prescription kP in disease group dG is defined as:

)1(00.1 += dGiH

dG

iHfc ),...,1( ld= (3)

where di

G

Hf is the frequency of use of iH in dG and

c is a positive constant. The value of c is determinedby how the treatment importance of the herb increases

when it occurs in one more prescription in the samedisease group. In our system 05.0=c . For examplein the disease group 9G = Rheumatism Group, the

herb 24H that has science name Xanthium

Strumarium is used in 5 standard prescriptions, so

the importance 924

G

H in prescriptions of this group is

1.00 + 0.05 (5 - 1) = 1.20. The herb 17H that has

science name Fallopia Multiflora is used in onlyone standard prescription of group ,9G so the

importance 917

G

H in prescriptions of 9G is 1.00.

In practice, there are a few special herbs thathave a very strong effect in some specific prescriptions. In that case the importance of thesespecial herbs is evaluated by experienced doctorsinstead of Eq. (3).

4.2. Distances among Prescriptions

To evaluate differences among the prescriptions we define two distances: content andtreatment distances.

The content distance C qpd between two

prescriptions pP and qP is Euclidean distance

between two content vectors:

qpC

qp RRd = (4)

The treatment distance from prescription pP

to disease state that cured by prescription qP is

defined as follows:

=

=

m

i

G

H

CqpT

qp

q

i

dd

1

(5)

where iH is an herb in pP and qG is disease groupcontaining qP .

Eq. (5) means that in treatment processeffects of pP is closer to disease state cured by qP

when the distance between two content vectors issmaller, and the sum of the herb importance of pP in

the disease group qG of qP is bigger.

Lets see an example of two standardprescriptions: 1P for Backache Disease and 2P for

Rheumatism Disease as shown on Tabs. 1 and 2. Onthese Tables, the Herb Name columns are the listsof herbs written in Vietnamese, the Amountcolumns are the corresponding amounts of herbs ingram, and the Content columns are the values ofcontent vectors of the prescriptions calculated by Eq.(2). Each herb has different treatment importancevalues on the two disease groups as shown inTreatment Importance of Herbs on Groups column.

As shown in Tabs. 1 and 2, 1P has a total

amount of 70 gram and 2P has a total amount of 76gram. These two prescriptions have 5 similar herbssuch as H6, H40, H55, H60 and H62. The content

distance between them is =

CPPd 21

31.09 which

means that regardless of properties of herbs, when thetotal amount of herb in the prescription is normalizedby 100.00 gram, the different amounts of herbs in thetwo prescriptions is 31.09 gram.

The treatment distance from 1P to disease

state cured by 2P (Rheumatism State 3) is =T

PPd 21

3.79. The treatment distance from 2P to disease state

cured by 1P (Backache State 2) is =T

PPd 12 4.29. The

distances mean that 1P and 2P are closer each other if

the treatment purpose is Rheumatism and littlefarther each other if the treatment purpose isBackache. In another word 1P is stronger on the

disease state cured by 2P and 2P is a little weaker on

the disease state cured by 1P .

In the case of a new prescription, theimportance of an herb in the new prescription willhave different values corresponding to the differentconcurrent diseases because the new prescription is

given to treat multi-concurrent diseases.In training process, the content vectors

kR ),...,1( nk= are put into SOM as feature vectors

and the content distances in Eq. (4) are used. Aftertraining, the prescriptions are self-regulated on theoutput map. The prescriptions with a similarity inherbal ingredients will be arranged in the vicinityeach other. The disease states treated by similar herbswill be also in the vicinity each other.

In the visualization process, vector xR of a

new herbal treatment ingredient xP is put into SOMs

input layer, and the location of xP in the output map

is displayed.


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Tab. 1. Backache standard prescription 1P and treatment importance of its herbs in Backache

and Rheumatism Groups

Herb NameAmount(gram)

ContentTreatment Importance Herbs on

GroupsBackache Rheumatism

H5 Cam tho 6 8.57 1.00 1.20H6 Can khng 8 11.43 1.05 1.05H38 Phc linh 12 17.14 1.05 1.10H40 Qu chi 8 11.43 1.10 1.35H55 Thng trut 8 11.43 1.05 1.10H60 Xuyn khung 16 22.86 1.00 1.15H62 d 12 17.14 1.05 1.25

TOTAL 70 100.00 7.30 8.20

Tab. 2. Rheumatism standard prescription 2P and treatment importance of its herbs in Backache

and Rheumatism Groups

Herb Name Amount(gram)

Content Treatment Importance of Herbs onGroupRheumatism Backache

H6 Can khng 8 10.53 1.05 1.05H33 Ngu tt 8 10.53 1.25 1.00H39 Ph t ch 8 10.53 1.15 0.00H40 Qu chi 8 10.53 1.35 1.10H53 Thin nin khin 8 10.53 1.05 1.00H55 Thng trut 8 10.53 1.10 1.05H58 Uy linh tin 8 10.53 1.05 0.00H60 Xuyn khung 8 10.53 1.15 1.00H62 d 12 15.79 1.25 1.05

TOTAL 76 100.00 10.40 7.25

Normally xP is located near prescriptions that have a

similarity in its herbal ingredient. And then thetreatment influences of xP on disease states

calculated by the treatment distances are displayed onthe map.

5. Implementation and Discussion

Based on the OM text books [15] and a preliminary survey on experienced doctors in

Thaibinh OM College, we built a system with 133typical prescriptions, 221 popular herbs in 17common disease groups: Constipation, Cirrhosis,Hepatitis, Cystitis, Pyelitis, Dysentery, Gastralgia,Artery Sclerosis, Rheumatism, Heart Rheumatism,High Blood Pressure, Backache, Embolism, BoneTuberculosis, Digestive Disorder, Tracheitis andAnemia.

In the training, SOM uses a Gaussianneighborhood function with an adaptive variance andadaptive learning rate. The output map is displayed inthe form of both rectangle table and bitmap image.

After training, the prescriptions that have

unusual herbs are normally located on corners oredges of the map, the prescriptions that have most popular herbs are generally located on middle, and

the prescriptions that have similarities in herbalingredients are located closer to each other. Fig. 4shows an example of the output map when a newherbal treatment ingredient is put into SOM, wherethe prescriptions are written by their abbreviation onSOMs output.The treatment effects of standard prescriptions andrelations among them are also conveniently visualized by the SOM. Fig. 4 also shows the location of aRheumatism standard prescription and its treatmentdistances to disease states cured by other standard

prescriptions, where the numerals after and beneaththe abbreviations mean the disease state numbers andthe treatment distance from the selected prescription,respectively. For example, HF2 2.35 means HeartFailure State 2 whose the treatment distance with theselected prescription HR4 (Heart Rheumatism State4) is 2.35.

The map produced by the SOM helpsdoctors much in their treatment process. It brings abetter view on relations among the prescribed herbaltreatment ingredients and their influence on diseasesthat may be cured, helping doctors to evaluate howwell herbal ingredients influence the main andconcurrent diseases. Based on the map, doctors canestimate the herbal ingredients so that positive effects


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of the treatment prescription will be maximized andnegative effects will be minimized. As a result, ithelps doctors to adjust therapy in treatment process. Italso increases the understandability of the treatment

prescriptions for both doctors and patients and thetreatment trend of doctors who give the prescriptioncan be fairly evaluated.

HP6 BT1 AN1 HP9 T4CO2

HF1HP5

4.00BI2 AH5 CH1

KI7 BI5 C3 CO4AN3

CO1 CT1 SI11 BI1 CI3 AH4 AH1 AH3

HP7 CI4 AH6 KI3 AH2 AH7

T3 SI3 SI1 CH2

AC1 BT4 HP4 BI3 DY4

KI8 AR2

SI12 CO3 BI4

AC3 SI2HP83.80

HP23.60

AR1 E3 SI6 HR1

DY3 NEWPRES

AP1 E2 R1

DY1 AC2HP12.70

DH2 HR2

2.97

DY2 AR4 SI7 AP2 E4 E1HR32.51

HR52.47

HR72.55

SI10 CT0 AR3 HR62.63

SI13 KI4 SI8 SI4 CI5HF52.55

SI5 DC2 CH3 SI9 CH5CI2

HR4 C2HF32.57

DC1 DH1 CI1HF41.88

AC52.47

AC4 HP3

CD1 CT2 BT3 C1

CD2 R7 AC6HF22.35

DD2 C4

B1 B2 SI14 B4 KI1 SI15 CO6 CH4 CO5 A2

R6 R4 R3 KI6 B3 R2 BT2 R5 CI6 KI5 KI2 CD4 T1 CD3 DD1 C5 T2

Fig. 4. Locations of a new treatment prescription and a Rheumatism standard prescription on the output map

Abbreviations in Fig. 4:AR: ArthritisAN: AnemiaAC: Artery SclerosisAH: Acute HepatitisAP: Arthritis PrecautionB: Backache

BI: Cystitis Bladder InflammationBT: Bone TuberculosisC: Colitis

CD: Chronic DysenteryCH: Chronic HepatitisCI: CirrhosisCO: ConstipationCT: Coronary ThrombosesDD: Digestive Disorder

DY: DysenteryE: EmbolismHF: Heart Failure

HP: High Blood PressureHR: Heart RheumatismKI: Pyelitis Kidney InflammationR: RheumatismSI: Gastralgia: Stomach

Inflammation and Ulcers State

T: Tracheitis

The disease far in

treatment effects

New prescription on

the mapThe disease close in

treatment effects

The disease closest in

treatment effects

Location of a Rheumatism

prescription and its treatment

distances to other standard

prescriptions


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6. Conclusions

We proposed the application of SOM to thevisualization of treatment effects of herbal medicineand showed the system implementation. Weconfirmed that the application has high performance,high applicability for the visualization of herbalinfluences on concurrent diseases and highunderstandability of the treatment prescriptions. Thetreatment distance among prescriptions calculated byEq. (5) is a relative distance which is recommendedand accepted by experienced doctors. However, itwas reported that in clinical practice the treatmentdistances are vague distances and they could not beevaluated exactly. In other words, an exactmeasurement for treatment distances amongprescriptions is required in practice.

Currently we use the rectangle output map

with Euclidean distance. Therefore sometimes it isnot easy to visualize the relations of prescriptionslocated in corners or on edges of the map. Thedistribution of prescriptions would be easiervisualized when the output map is displayed on thesurface of a sphere as shown in Fig. 5.

Our future work is to use a hyperbolic SOM[16] to visualize prescriptions on surface of a virtualsphere and to add more prescriptions for betterevaluation of the influences of the herbal medicine.

Fig. 5. The distribution of prescriptions would be

better when the output map is displayed on the

surface of a sphere

References

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Computing to RETS: Rheumatic Evaluationand Treatment System by Oriental Medicine, J.of Biomedical Soft Computing and HumanSciences, Vol. 11 (1), pp 7-13, 2007

[2] Cao Thang, Eric W. C., Y. Hoshino, K. Kameiand N. H. Phuong: A Proposed Model ofDiagnosis and Prescription in OrientalMedicine Using RBF Neural Networks, J. ofAdvanced Computational Intelligence andIntelligent Informatics, Vol.10 (4), pp 458-464,2006

[3] Cao Thang, Eric W. C., Y. Hoshino and K.Kamei: A Proposed Model of Soft Computing

in Diagnosing Diseases and Prescribing HerbalPrescriptions by Oriental Medicine, Proc. of

the First Int. Conf. on Complex MedicalEngineering (CME2005), pp 904-908, 2005

[4] R. Dybowski and V. Gant: Clinicalapplications of artificial neural networks,Cambridge University Press, 2001

[5] F. A. Maysam et al,: Survey of utilization offuzzy technology in Medicine and Healthcare,Fuzzy Sets and Systems, 120, pp 331349,2001

[6] E. H. ShortliEe: Computer-Based MedicalConsultations, MYCIN, Elsevier, NY, 1976

[7] R. Montironi et al,: A typical adenomatoushyperplasia (adenosis) of the prostate:development of a bayesian belief network forits distinction from well differentiatedadenocarcinoma, Hum. Pathol., 27 (4) pp 396-407, 1996

[8] H. P. Nguyen, S. Pratit, and K. Hirota: Fuzzy

Modeling for Modifying StandardPrescriptions of Oriental Traditional Medicine,Journal of Advanced ComputationalIntelligence and Intelligent Informatics, Vol. 7(3), pp 339-347, 2003

[9] T. Kohonen: Self-Organizing Maps, 3rdedition, Springer, 2006

[10] Teucci M.C., Braccini G., Carpeggiani C.,Marchesi C.: An application of self-organisingmaps for a knowledge base for usein cardiacdomain, Computers in Cardiology, Vol. 24 (7-10), pp 569-572, 1997

[11] H. Ressom,D. Wang, P. Natarajan: Adaptive

double self-organizing maps for clusteringgene expression profiles, Neural Networks,Vol. 16 (5-6), pp 633- 640, 2003

[12] Markey M. K. et al,: Self-organizing map forcluster analysis of a breast cancer database,Artificial Intelligence in Medicine, Vol. 27 (2),pp. 113-127, 2003

[13] Cheng H. C. et al,: Application of Self-Organizing Map (SOM) for Cerebral CortexReconstruction, International Journal ofComputational Intelligence Research, Vol.3 (1),pp. 26-30, 2007

[14] Peter K. K et al.: Focus on Metabolic

Syndrome Trends using SOM, Journal of JapanSociety for Fuzzy Theory and IntelligentInformatics, Vol. 19 (6) pp. 634-641, 2007

[15] T. Thuy, P.D. Nhac, and H.B. Chau, Lecturesin Oriental Medicine, Medicine Pub. Hanoi,Vol. 1-2, 2002

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Thang CAO

He received B.S. in electronicsfrom Hanoi University ofTechnology, Vietnam in 1994,M.S. and Dr. degrees ofengineering from RitsumeikanUniversity, Japan in 2005 and

2008.His research interests are patternrecognition, decision supportsystems, computer vision, roboticsand applications of softcomputing.

Katsuari KAMEI

He received the B.S. and M.S.degrees in electrical engineering,and the PhD from RitsumeikanUniversity, Japan in 1978, 1980and 1983, respectively. He had

worked as an assistant professorand an associate professor atRitsumeikan University since1983. He has been a professor atthe college of Information Scienceand Engineering, RitsumeikanUniversity since 1998. His presentresearch interests includedevelopment of the emotion processing system (Kanseievaluation and modeling) basedon the vital signal measurements.He is a member of BMFSA,Society of Fuzzy Theory andSystems, IEEE, and so on.

Tuan Linh DANG

He currently is a student inFaculty of InformationTechnology, Hanoi University ofTechnology, Vietnam.His research interests includehuman interface, Kansei modelingand computational intelligence.

* * *

visualization system of herbal prescription effects in oriental medicine by self-organizing map

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