"Fuzzy" is defined as "blurred" or "not clear" (Webster's New Twentieth Century Dictionary, 1957). In Japanese, "Fuzzy" is "曖昧" (a-i-ma-i). These Chinese characters originally mean that a substance can not be seen at dawn or in shadows. However, when once the sun shines, it can be clearly noticed. It has a definate core even though it is vague or ambiguous. Natural languages (categorical information) have their definate meanings ("langue" by Saussure), but the meaning must be variously changed in their contexts ("parole" by Saussure). Measured informations as clinical loboratory data are expressed by definate numbers, but they have also different meanings depending on their backgrounds (contexts). When we process the categorical informations in biomedical systems, we usually transform them into the numerical data. The words could not be used directly. We should not use blurred or false data but the correct and most suitable data. Fuzzy inference works most efficiently to discriminate the members and to make a decision. Association of the fuzzy inference in the neural network could afford the most human-like way to process the biomedical data.

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In the framework of inflammatory protein variations, it is illustrated a methodology based on fuzzy logic for diagnosis assistance. The pattern of medical knowledge involves five proteins and eleven groups: eight inflammatory syndromes, two non inflammatory syndromes and the normal condition. Most of the inflammatory syndromes are not typical for protein variations can be dissociated, so that a global analysis of the problem has to be considered. Choosing as an example, Vasculitis (three protein levels are increased, while two others are decreased or normal), it is shown how the biomedical knowledge can be expressed linguistically to be then translated into possibility distributions. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. In some cases, influence of proteins in terms of importance operations is not symmetrical. To face such situations, a calibration of soft-AND operators is performed. These aggregation procedures are illustrated with an example. Defuzzification of results (i.e. diagnostic groups assigned to patients) is presented as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control.

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The authors have developed a diagnosis system with a Fuzzy inference on a personal computer. In this system, we use the membership functions for evaluating data and can get the excellent detecting ability in borderline cases. One of the characteristrics of the new system is a multi-layer structure. As we can examine one case from many point of view, we can often find combined disease. Moreover, We can easily use the information which denies the probability of some disease, and become able to rule out a lot of disease more exactly.

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We have developed an Expert System (ES) which assists the Doctors in the Automated Multiphasic Health Testing and Service (AMHTS), specially in Diabetes Mellitus. In the AMHTS the Doctors classify the patient conditions to 6 special categories (A, B, BF, C, D, G) difined in AMHTS. Almost of all ES can infer the Diagnosis only by using Rule Base with crispy threshold. These are not so sufficient for Doctors, because Doctors have ambiguous sense. We have developed ES, applied to DM, utilizing two types of methods indicating continuous certainty. One is the Index method similar to FUZZY integration theory. The other is the ES with Neural Network (NN). In the Index method, we have newly introduced a Diabetes Mellitus Index (DMI) which indicates the degree of DM. DMI is calculated by converting the 3 blood sugar (FBS, 1hGTT, 2hGTT) using non-linear scale, and multiplying the weightings corresponding to the each blood sugar value. Subsequently, inference is carried out using a rule base and DMI. Consequently, the categories from the ES upon 1000 patients data in Toshiba Rinkan Hospital have demonstrated a high degree of coincidence with the categories by the Doctors employed in the AMHTS. NN is constructed with 3 layers as the input layer, the hidden layer and the output layer. The input layer has 3 units assigned to 3 blood suger. The hidden layer has 50 units. The output layer has 5 units assigned to 5 categories except "A" category. "A" category is infered by the rule base. Consequently, the coincident ratio between the categories from the ES with NN and ones by the Doctors upon 3684 patients including above 1000 data has been sufficient. It is, however, less than the coincident ratio from the ES with DMI. Each of the 4 methods (FUZZY, Index, NN, Rule) is suitable and effective to the Medical Application, and so it is important to apply and combine the 4 methods to the appropriate problems.

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Pleodorina californicaにおける細胞分化を解析し、これを説明するモデルを仮設を立てて作製した。最も重要な仮設は細胞分化の方向に対し確定度の異なる2種類の細胞の存在に関するものである。一つは細胞の分化の方向が確定した細胞であり、sheatにおける空間的な配列にもとずいてsomatic cellまたはreproductive cellに分化する。もう一方は細胞分化の方向が確定していない"fuzzy cell"である。colonyにおけるreproductive cellの割り合いの実測値分布と理論値分布が高い一致を示したことからPleodorina californicaの細胞分化における"fuzzy cell"の存在とその重要性が示唆された。

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This paper describes a design method of a fuzzy control system for artificial hearts. Artificial heart pump outputs and dose frequencies are controlled by the system for mainaining the circulatory system physiologic. To obtain control laws of these variables, the fuzzy inference is based on results of real time computation with circulatory models as well as on empirical knowledge about artificial heart control.

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医療情報と言われるものには、大別して、血清中のGOT, GPTなどの検査データのように測定可能な計量データと主訴にみられるような「疲れが激しい」などの言語情報や±、+、のカテゴリーデータとして把らえられるものがある。血液データのような計量データは測定値が数値で測定されるため客観データとしての地位を占めて、データそのものにファジィ性を有していないように考えられている。これに対して、精神科学領域での「ゆううつである」とか痛みの言葉での「耐えられないような痛み」、「ズキンズキン」、超音波診断における「境界やや不明瞭」などの情報は、ファジィ情報として、すぐに認知されることが多い。厳密に測定された数値データもファジィ性を持っているがこれらのデータの中でも画像情報はとりわけ、ファジィ情報の典型例である。本稿では、この画像情報に注目し、画像情報と画像診断プロセス(意思決定過程)に介在するファジィ性を解説し、診断ロジックにおけるファジィ推論の利点等について考察する。

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