Design and Implement for Diagnosis Systems of. Hemorheology on Blood Viscosity Syndrome. Based on GrC. Qing Liu, Feng Jiang, and Dayong Deng.
Design and Implement for Diagnosis Systems of Hemorheology on Blood Viscosity Syndrome Based on GrC Qing Liu, Feng Jiang, and Dayong Deng Department of Computer Science, Nanchang University, Nanchang 330029
Abstract. This paper discusses the design and implement for the diagnosis software of blood flowing dynamic theory on blood viscosity syndrome (BVS). The BVS is a clinical syndrome caused by one or several blood viscosity factors. The software of diagnosis and treatment in medicine is a reasoning system of the experience of simulating clinical experts. In the system, the experience of experts is transformed into the mathematical formulas using rough-fuzzy & fuzzy-rough approach. And then we create the reasoning system by the mathematical formulas and granular computing. The development of diagnosis software is successful via the applications of several thousand cases in clinic. The system is dynamic, it can learn from examples by visiting the case base. So addition, subtraction, adjustment and update for treatment measures are implemented dynamically. The formulas of the system in compare with similar systems are more perfect. The diagnosis efficiency in clinic of the system in compare with the doctors is higher.
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Introduction
The diagnosis software of blood flowing dynamic theory on BVS is a fuzzy reasoning machine of simulating expert experience. BVS is a common clinical manifestation[1] according to the clinical experience, which is always appearing an irregular change. The same disease type may appear different diagnosis of BVS during different clinic. Different disease type may appear the same diagnosis of BVS during different clinic. Hence, the treatment measures must be making adjustment dynamically according to clinical observation, so that the diagnosis software of designing can be used in the ever changing clinic. According to the scientific assertion of basic theory for Chinese traditional medicine on “Dialectical Applying Treatment”, “The Same Disease Using Different Treating” and “Different Disease Using the Same Treating”, we design the diagnosis software system of blood flowing dynamic theory on BVS. The system includes a knowledge base consisted of 6 tables. The 6 tables are both nested and independent, and they can contact each other by a mapping [2],[4]. The tables can also be queried and searched by external key. The system is developed, implemented by Dephi5.0 and Interbase5.0, running in Win98/2000. The operations of the system are normative and easy mastery. G. Wang et al. (Eds.): RSFDGrC 2003, LNAI 2639, pp. 413–420, 2003. c Springer-Verlag Berlin Heidelberg 2003
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The System Structure and Relation between All Parts in It
The following is the structure of system and the flow drawing of its executed:
The knowledge and data base of system is consisted of 6 tables, which are the interval table of reference values for normal people, the table of gathering clinical data, the weight value table of 18 indexes, the level table of subtypes, the tables of disease types and the treatment measures & suggestions. The operations of all in the system are on the tables, the data in the tables is computed dynamically during the system executed. Hence, this system is a dynamic system of diagnosis software. The procedure of system executing is following: (1) Gathering clinical data of patients , including name, sex, age, case history and testing value of indexes, and filling into the table 1. The data of gathering is used as a gist of diagnosis for the patients, also offering some information for inquiring case history aftertime. (2) Granulating the interval of reference value for normal people. The interval of reference value of index for normal people is formulized via rough-fuzzy and fuzzy-rough approach, namely the experience of experts is transformed into the
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mathematical formula. The average value AV and standard deviation SD of each interval are computed by the formulas and to fill in table 0. The level of each index is computed according to the experience of experts, to fill into the table 2, thus the data in table 2 is generated dynamically. (3) Computing the level of subtypes. By the table 2 and the experience formulas of experts via mathematical handling we compute the level of each subtype, filling into the table 3, thus the data in table 3 is generated dynamically. (4) The diagnosis of patients. We diagnose the type of disease for the patients with the table 3 and the experience formulas of experts via mathematical handling, including blood hyper-viscosity syndrome (BHS), blood lower-viscosity syndrome (BLS), blood hyper-lower-syndrome (BHLS) and blood lower-hyperviscosity syndrome (BLHS) etc. The data in table 4 is generated dynamically. (5) Giving treatment measures and suggestions. Based on granular computing, the treatment measures and suggestions are derived from the table 5(Omitting) according to the diagnosing type of disease for patients. Due to create the base of case history in the system, so we arrange the cache of visiting base of case history in the procedure of reasoning treatment measures and suggestions, and comparing with relative case history in the base, implementing intelligence action to learn from examples. (6) Printing recipe. The result of diagnosing, treatment measures and suggestions are included in the recipe. Besides, the explanation is also attached in it, let patients know how treating for oneself to cooperate with the doctor.
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Principles of the System
Designing of the system is based on the rough-fuzzy, fuzzy-rough and granular computing methods. First of all, we handle the interval of reference value of blood viscosity index for normal people using rough-fuzzy and fuzzy-rough approach, namely computing the average value AV and standard deviation SD of each interval in the table 0. Let [a, b] be a interval. An indiscernbility relation R is defined in the interval, namely ∀x1 ,x2 ∈[a, b], x1 R x2 iff | x1 -x2 |≤ 0.618, the transitivity of R is defined on [j*0.618, 1 + j ∗0.618] ⊆ [a, b], j=0,1,. . . .Thus the interval is divided by R, until a + n∗0.618 > b, where n is the total of small intervals. 0.618 here is chosen from “Gold Cut” in Chinese ancient mathematics. And Professor Luogen Hua, famous Chinese mathematician in the world used also the data in his book ”Methods for Plan as a Whole”(in Chinese), which is called best choosing point on [0,1], to be also a fuzzy concept, and defining an indiscernbility relation by it. The partition on [a, b] is also called granulating. On the interval [a, b], We compute the average value and standard deviation n−2 AV=(a+ j=1 (a + j∗0.618)+b)/n (1) and n−1 SD=sqrt( j=0 ((a + j∗0.618)-AV)2 /n) (2) respectively, where sqrt is a functional symbol of square root. And to compute the index value corresponding to the clinical testing data TV, namely (T V − AV )/SD f or T V > a IV = (−T V + AV )/SD f or T V ≤ a (3)
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where TV is the clinic testing blood viscosity data via testing instrument. a is the low bound corresponding to the interval. Computed IV by (3) is also filled in the table 1. Table 0 of Reference Value Intervals for Normal People Index item Male Female
interval1 AV1 SD1 (1-200) [4.42,4.97] ? ? [3.32,4.08] ? ?
interval3 AV3 SD3 interval6 AV6 SD6 (3-5) [8.73,10.27] ? ? [0.00,15.0] ? ? [6.86,8.52] ? ? [0.00,20.00] ? ?
Table 1 of Clinical Data Index item Patient1 Patient2
index1 (1-200) [4.42,4.97] [3.32,4.08]
TV1 IV1 interval3 TV3 IV3 . . . age sex (3-5) ? ? [8.73,10.27] ? ? ... ? male ? ? [6.86,8.52] ? ? ... ? female
Case history emergency EPG
We can compute the level of each index value to use AV , SD & IV with above and following rules: (1) 0≤IVIV≥AV+1∗SD→1 ; (3) 3>IV≥AV+2∗SD→2; (4) 4>IV≥ AV+3∗SD→3; (5) 5>IV≥AV+4∗SD→4; (6) 6>IV≥AV+5∗SD→5; (7) IV≥AV+6∗SD→6; (8) IV a6 ? T V6 > b6
level6 index7 hematocrit ? T V7 ≤ a7 ? T V7 ≤ b7
level7 . . . ? ?
... ...
In table 2, ai and bi is the lower bound of Reference Value Intervals for male and female respectively. We compute the levels of subtypes according to experiential formulas of the experts. Computed levels are filled in the table 3. The subscript number n of related data item in each table is a code corresponding to nth index item. The following is similar. •1 Concentration: ConL=(4∗level7 +4∗level5 +level3 )/9∨(4∗level7 +4∗level5 )/8; •2 Viscosity: VL=( (level1 + level2 +level3 )∗2+4∗ level4 + level5 )/11;
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•3 Aggregation: AL=(3∗ level11 + level18 )/4 ∨(3∗ level11 + (level14 + level15 ) /2∗1.5)/4; •4 Coagulation: CoaL=(2∗ level13 +(level14 + level15 )/2∗1.5∗3+ level18 )/6 ∨(2∗ level13 +(level14 + level15 )/2∗1.5∗3)/5∨ (3∗ level13 +level11 )/4; •5 Hematocrit: HL=(TV7 -AV7 )/SD7 ; •6 Erythrocyte Aggregation: EAL=(level11 +level12 )/2; •7 Red Cell Rigidity: RCRL=(TV10 -AV10 )/SD10 ; •8 Blood Plasma Viscosity: BPVL=(TV5 -AV5 )/SD5 ; •9 Blood Platelet Aggregation: BPAL=(TV18 -AV18 )/SD18. Level Table 3 of Subtypes Subtypes ConL VL Patient1 ? ? Patient2 ? ?
AL ? ?
CoaL HL ? ? ? ?
EAL RCRL BPVL BPGL . . . ? ? ? ? ... ? ? ? ? ...
The facts for influencing blood viscosity syndrome are computed in the system. Disease types will be diagnosed by the positive and negative of values for the nine subtypes. Namely, Normal or BHS or BLS or BHLS or BLHS will be determined by the levels of nine subtypes. We may also decide a degree corresponding to the type of disease using the levels of nine subtypes. Thus, one has the table 4 dynamically. Table 4 of Disease Types attribute conl vl al coal hl eal rcrl bpvl bpgl bhs bls bhls blhs Nor Patient1 ? ? ? ? ? ? ? ? ? ? ? ? ? ? Patient2 ? ? ? ? ? ? ? ? ? ? ? ? ? ?
In the table 4, condition attribute set C = { ConL, VL, AL, CoaL, HL, EAL, RCRL, BPVL, BPGL}, Decision attribute set D = {BHS, BLS, BHLS, BLHS, Nor}. ? in the table is generated by computing during system running. The following introduces the theories of reasoning in the system.
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Knowledge Base and Reasoning in It
We can create the approximate reasoning based on granular computing. Hence we define following concepts. Definition 1 (degree inclusion and closeness) Let G=(ϕ, m(ϕ)) and G’=(ϕ’, m(ϕ’)) be two Granules[2],[3], the degree inclusion and closeness of them are defined as follows respectively:
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(1) G is included in G’ in degree at least p, denoted by Vp (G, G’), where p∈[0,1]. In formally, Card(G ⊗ G )/Card(G) for G = ∅ Vp (G, G ) = 1 for Otherwise where ⊗ is a symbol of granular operations. For given positive real number p∈[0,1], if V(G, G’)≥p, then the relation of G included in G’ in degree at least p is thought to be an extraction or a satisfaction ; (2) G closes to G’ in degree at least p , namely closeness relation Clp (G,G’) is denoted by Vp (G,G’)∧ Vp (G’, G). In formally, having Clp (G, G’) iff Vp (G,G’) ∧ Vp (G’,G). Proposition 1. |= (ϕ → ψ, m(ϕ → ψ)) is derivable iff |= ϕ → ψ is derivable and m(ϕ) ⊆m(ψ) is held. Proposition 2 ∀ϕ,ψ ∈RLIS , then (1) m(av )={x ∈U: a(x) = v ∈V}, where V is the set of attribute values, m is a symbol of semantic function of common logical formulas; (2) (∼ ϕ, m(∼ ϕ))=U-(ϕ, m(ϕ)); (3) (ϕ ∨ ψ, m(ϕ ∨ ψ))=(ϕ, m(ϕ))⊕ (ψ, m(ψ)); (4) (ϕ ∧ ψ, m(ϕ ∧ ψ))=(ϕ, m(ϕ))⊗ (ψ, m(ψ)); (5) ((∀x)ϕ(x), m(ϕ))=(ϕ(e1 )∧. . . ∧ϕ(en ), m(ϕ(e1 ) ∧. . . ∧ϕ(en )))=(ϕ(e1 ), m(ϕ))⊗. . . ⊗ (ϕ(en ), m(ϕ)), assuming here that universe U of all objects is finite. In the fact, IS is finite. And for ∀x∈VAR, ur ∈VAL, there exists an entity ei ∈U, such that ur (x)=ei , i=1,. . . ,n, where ur is an assigned symbol to object variable[2],[4]. The formulas with connectives → and ↔ can be substituted by ∼ and ∨ or ∧. Proposition 3 Let G=((ϕ, m(ϕ)),(ψ, m(ψ))) and G’=((ϕ’, m(ϕ’)), (ψ’, m(ψ’))) be two granules. They are close in degree at least p in IS, written by Clp (G,G’), then having (1) Clp (m(ϕ),m(ϕ’)); (2) Clp (m(ψ)-m(ϕ), m(ψ’)- m(ϕ’)); (3) Clp (U-m(ϕ),U-m(ϕ’)). Proposition 4 If G=((ϕ,m(ϕ)),(ψ,m(ψ))) and G’=((ϕ’,m(ϕ’)), (ψ’,m(ψ’))) are the granules defined by decision rules[2],[3], and Clp ((ϕ,m(ϕ)),(ϕ’,m(ϕ’)), then having Clp ((ψ,m(ψ)),(ψ’,m(ψ’))). This proposition will be an important criterion for search reasoning in Artificial Intelligence. The search reasoning is finished in rule base of expert systems. The rule base of tradition is a set of rules form as ϕ1 → ψ1 ,. . . , ϕi → ψi ,. . . , ϕn → ψn . The granulations corresponding with them are the form ((ϕ1 ,m(ϕ1 )),(ψ1 ,m(ψ1 ))),. . . ,(( ϕi , m(ϕi )), (ψi , m(ψi ))),. . . , (( ϕn , m(ϕn )), (ψn , m(ψn ))) respectively. The set of the granulations is the granulation base of form (( ϕi , m(ϕi ) (ψi , m(ψi ))), where i=1,2,. . . n. Thus, the inference in rule base of tradition is transformed into a reasoning in granulation base. Namely, which is the matching between the granule (ϕ,d(ϕ)) of gathering in situation and condition granule (ϕi , m(ϕi )) of a granulation in granulation base. If Clp ((ϕ,m(ϕ)), (ϕi , m(ϕi ))) is held then the decision (ψi , m(ψi )) of granulation ((ϕi , m(ϕi )), (ψi , m(ψi ))) is chosen. Following is the procedure of inference: (1) Gathering a group data to have contact with goal of searching. (2) The group data is denoted by a rough logical formula ϕ, it is constructed as an elementary granule (ϕ, m(ϕ)) on IS.
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(3) Computing the closeness degree pi between the granule and condition granule (ϕi , m(ϕi )) of each granulation ((ϕi , m(ϕi )), (ψi , m(ψi ))) in granulation base, and if pi ≥p, then {pi }∪List, where p is a given threshold and List is a table of storing closeness degree pi . Until the end of matching with each granulation in the granulation base. That is, For (i=1; eof (f); i++) {pi =V(G, Gi ); pi ’=V(Gi , G); pi ”=min(pi , pi ’); if (pi ≥ p) List = {pi }∪List; }\* f is a file of granulation base, p is a given threshold, List is a table of storing closeness degree of satisfying*\ (4) pi =max(List)i . Therefore, the goal is the decision granule ((ψi , m(ψi )) of the granulation ((ϕi , m(ϕi )), (ψi , m(ψi ))) corresponding to the closeness degree pi . If maximal pi is one more, such as {pi1, . . . , pij ,. . . ,pim }, then ij =min{i1, . . . ,ij ,. . . ,im }, namely the decision part (ψij , m(ψij )) of the most former granulation ( (ϕij , m(ϕij )), (ψij , m(ψij ))) corresponding to the close degree pij is one of our needful goal. For example, Let the data set of gathering in clinic for the patient, P={wang, Male, 65, 3.5, 4.5, 7.2, 16.1, 1.2, 1.0, 0.35, 5.5, 32.2, 2.8, 3.5778, 50.0, 1.5, 8.5, 6.5, 2.5, 10.2, 28.0, },which is written a rough logical formula ϕ = namewang ∧ Sexmale ∧ Age65 ∧ TV3.5 .The granulation corresponding to it is written as G = (ϕ, m(ϕ)),where the granule corresponding with a sub-formula ϕi is Gi = (ϕi , m(ϕi )).Such as, G2 = (ϕ2 , m(ϕ2 )) = (male, m(male)). 1. Computing average value AV and standard deviation SD of each index item interval by rough-fuzzy and fuzzy-rough approach. G2 is matched with the granulation (male, m(male)) of first row in the table 0. So, the decision part m(male) is chosen. Hence, [4.42,4.79], [5.41,6.33], . . . , are granulated by rough-fuzzy and fuzzy-rough respectively. To have AV1 =4.70 SD1 =0.28; AV2 =5.92 SD2 =0.38 ;AV3 =9.58 SD3 =0.59; AV4 =20.49 SD4 =1.54 ; AV5 =1.50 SD5 =0.07; AV6 =7.71 SD6 =4.61 ;AV7 =0.47 SD7 =0.07; AV8 =8.50 SD8 =1.02 ; AV9 =43.46 SD9 =5.15; AV10 =5.16 SD10 =0.92 ;AV11 =3.46 SD11 =0.13; AV12 =36.77 SD12 =21.40 ; AV13 =3.14 SD13 =0.75; AV14 =17.45 SD14 =4.98 ;AV15 =16.11 SD15 =4.96; AV16 =3.25 SD16 =0.30 ; AV17 =13.35 SD17 =1.22; AV18 =37.61 SD18 =8.55; 2. Computing index value IV of each index item by clinical testing value TV,AV and SD. IV1 =-4.29, IV2 =-3.74 , IV3 =-4.03 ,IV4 =-2.85, IV5 = -4.29 ,IV6 =-1.46, IV7 = -1.77 ,IV8 = -2.94 IV9 =-2.19, IV10 = -2.57, IV11 =0.98, IV12 = -0.62, IV13 = -2.19, IV14 = -1.80, IV15 = -1.94, IV16 = -2.50 IV17 =-2.58, IV18 = -1.12 . 3. Computing level of each index value IV by the rules (1)-(14) in above. Level =-1 , level2 =-5 , level3 =-6 , level4 =-6 , level5 =0 , level6 =-1 . . . 4. Computing level of each subtype by the rules •1 -•9 in above. ConL=-3, VL=-3, AL=0, CoaL=-2, HL=-2, EAL=1, RCRL=-3, BPVL=-5, BPGL=0 5. Diagnosing type of disease by the levels of subtype in the table 4. The degree of disease corresponding to the type of disease is computed by average
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value of level sum of relative subtypes and the correct value of case history. Diagnosing type of disease is BLHS ,the degree is –3.22(Low) and 1.00(High) 6. Printing out the recipe(omitting) using table 5 (omitting) and offering the explanation about type of disease for patient
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Feature of the System
(1) Automatic analyzing and diagnosing the type of disease in BVS and computing the degree value of disease corresponding to the type. (2) Automatic printing out the diagnosing result, treatment measures, suggestions and related explanation for patient. (3) Automatic saving case history, offering the search in further consultation with a doctor, realizing the learning from examples, also realizing the clinical observe dynamically and adding, subtracting, adjusting, update the original treatment measures. (4) Realizing analyzing and diagnosing for BVS by the combination between Chinese traditional medicine and Western medicine in medical skill. (5) The experience of experts is formulized by rough-fuzzy and fuzzy-rough approach in the theory of system designing. (6) The system is divided into 4 levels and computing related data of each level dynamically in the art of programming. Summary, the design of medical skill in the system is an original. It uses an approximate reasoning of granular computing in programming of the system. Acknowledgements. This study is supported by the State Natural Science Fund (#60173054) and Natural Science Fund of Jiangxi province. Thanks are due to Professor Guoxian Li for his offering original medical skill.
References 1. Guoxian Li,Hypothesis on Blood Viscosity Syndrome (Clinical Blood Viscosity Syndrome), International Workshop of Clinical Blood Viscosity Syndrome, USA, 1995. 2. Qing Liu and Qun Liu,Approximate Reasoning Based on Granular Computing in Granular Logic, The Proceedings of ICMLS2002, Nov. 4–6, 2002. 3. A.Skowron and J.Stepaniuk, Extracting Patterns Using Information Granules, Proceedings of International workshop on Rough Set Theory and Granular Computing (RSTGC-2001), Bulleting of International Rough Set Society, Vol.5, No.1/2, May 20–22, 2001, 135–142. 4. Qing Liu, Neighborhood Logic and Its Data Reasoning in Information Table of Neighborhood Values, Journal of Computer (in Chinese),Vol.24, No.4,2001,4.
