Psychotherapy for Somatoform Disorder (신체형 장애의 정신치료)
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- Korean Journal of Psychosomatic Medicine
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- v.4 no.2
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- pp.269-276
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- 1996
A theroretical study was made on the psychodynamism of somatoform disorder. Somatoform disorder is caused by a defense mechanism of somatization. Somatization is the tendency to react to stimuli(drives, defenses, and conflict between them) physically rather than psychically(Moore, 1990). Ford(1983) said it is a way of life, and Dunbar(1954) said it is the shift of psychic energy toward expression in somatic symptoms. As used by Max Shur(1955), somatization links symptom formation to the regression that may occur in response to acute and chronic conflict. In the neurotic individual psychic conflict often provokes regressive phenomena that may include somatic manifestations characteristic of an earlier developmental phase. Schur calls this resomatization. Pain is the most common example of a somatization reaction to conflict. The pain has an unconscious significance derived from childhood experiences. It is used to win love, to punish misdeeds, as well as a means to amend. Among all pains, chest pain has a special meaning. Generally speaking, 'I have pain in my chest' is about the same as 'I have pain in my mind'. The chest represent the mind, and the mind reminds us about the heart. So we have a high tendency to recognize mental pain as cardiac pain. Kellner(1990) said rage and hostility, especially repressed hostility, are important factors in somatization. In 'Psychoanalytic Observation on Cardiac Pain', psychoanalyst Bacon(1953) presented clinical cases of patients who complained of cardiac pain in a psychoanalytic session that spread from the left side of their chests down their left arms. The pain was from rage and fear which came after their desire to be loved was frustrated by the analyet. She said desires related to cardiac pain were dependency needs and aggressions. Empatic relationship and therapeutic alliances are indispensable to psychotherapy in somatoform disorder. The beginning of therapy is to discover a precipitating event from the time their symptoms have started and to help the patient understand a relation between the symptom and precipitating event. Its remedial process is to find and interpret a intrapsychic conflict shown through the symptoms of the patient. Three cases of somatoform disorder patients treated based on this therapeutic method were introduced. The firt patient, Mr. H, had been suffering from hysterical aphasia with repressed rage as ie psychodynamic cause. An interpretation related to the precipitating event was given by written communication, and he recovered from his aphasia after 3 days of the session. The second patient was a dentist in a cardiac neurosis with agitation and hypochondriasis, whose psychodynamism was caused by a fear that he might lose his father's love. His symptom was also interpreted in relation to the precipitating event. It showed the patient a child-within afraid of losing his father's love. His condition improved after getting a didactic interpretation which told him, to be master of himself, The third patient was a lady transferred from the deparment of internal medicine. She had a frequent and violent fit of chest pains, whose psychodynamic cause was separation anxiety and a rage due to the frustration of dependency needs. Her symptom vanished dramatically when she wore a holler EKG monitor and did not occur during monitoring. By this experience she found her symptom was a psychogenic one, and a therapeutic alliance was formed. later in reguar psychotherapy sessions, she was told the relaton between symptoms and precipitating events. Through this she understood that her separation anxiety was connected to the symptom and she became less terrifide when it occurred. Now she can travel abroad and take well part in social activities.
One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.