An otoneurological expert system was developed to help collect data and diagnose both central and peripheral diseases causing vertigo. Patient history and otoneurological and other examination results are used in the reasoning process. The case history data can be either mandatory or supportive. Mandatory questions are used to confirm a diagnosis, and conflicting answers are used to reject an unlikely disease. Supportive questions support or suppress a diagnosis, but their presence is not obligatory. The reasoning procedure of the otoneurological expert system scores every question independently for different diagnoses, depending on how well they agree with the symptom entity of a disease. Diagnostic criteria are set for each disease. Graphic displays illustrate the linear and nonlinear correlation between the symptoms and diseases. Emphasis is placed on diminishing the possibility of a wrong decision rather than maximizing the likelihood of reaching only one right decision, so that even rare diseases can be taken into consideration.
A decision tree is an artificial intelligence program that is adaptive and is closely related to a neural network, but can handle missing or nondecisive data in decision-making. Data on patients with Meniere's disease, vestibular schwannoma, traumatic vertigo, sudden deafness, benign paroxysmal positional vertigo, and vestibular neuritis were retrieved from the database of the otoneurologic expert system ONE for the development and testing of the accuracy of decision trees in the diagnostic workup. Decision trees were constructed separately for each disease. The accuracies of the best decision trees were 94%, 95%, 99%, 99%, 100%, and 100% for the respective diseases. The most important questions concerned the presence of vertigo, hearing loss, and tinnitus; duration of vertigo; frequency of vertigo attacks; severity of rotational vertigo; onset and type of hearing loss; and occurrence of head injury in relation to the timing of onset of vertigo. Meniere's disease was the most difficult to classify correctly. The validity and structure of the decision trees are easily comprehended and can be used outside the expert system.
The etiology of sudden sensorineural hearing loss, so called sudden deafness, has for long puzzled researchers. Recently we have studied the possibility that a hitherto relatively unknown retrovirus group consisting of human spumaretroviridae (HSRV) might be the causative agent of sudden deafness. During the last 3 months we have screened about 30 cases of sudden deafness. In 4 of them antibodies against HSRV were detected. Three of them had suffered from a flu-like condition about 2 weeks before the onset of hearing loss. In 2 cases the hearing of both ears was involved, in 1 case a relapsing hearing loss was observed, and 1 case developed a Meniere-like symptomatology with a fluctuant hearing loss. Vertigo was present in 3 patients and all suffered from tinnitus. Full recovery of hearing was observed in 4 of 6 affected ears whereas 2 ears became practically deaf with poor speech discrimination. At present it seems likely that a significant part of sudden deafness is caused by HSRV infection. The course of infection follows the spontaneous course of sudden deafness described by many authors. We encourage otologic units to screen for HSRV when assessing the etiology of sudden deafness.
Levo H, Blomstedt G, Pyykkö I. Is hearing preser6ation worthwhile in 6estibular schwannoma surgery. Acta Otolaryngol 2000: Suppl 543: 26 -27.The purpose of the study was to evaluate the usefulness of hearing preservation in vestibular schwannoma (VS) surgery. Hearing preservation was attempted in 123 of 383 patients operated on during the years 1979 to 1993 at Helsinki University Hospital. Hearing was preserved in 47 cases. Pure-tone averages (PTA) better than 30 dB were found in 12 cases postoperatively. Seventy percent of the patients rated their hearing preservation as valuable or very valuable. Only 8% did not find hearing preservation useful. Postoperatively, tinnitus was present in 62% of the patients, and it was a moderate problem in only 23% of the patients. In only one subject was the tinnitus a handicap that reduced the quality of life. Based on these experiences, we encourage surgeons to continue efforts to preserve hearing in VS surgery.
Decision tree induction, as well as other inductive learning methods, requires training data of high quality to be able to generate accurate and reliable classification models. Example cases should form a representative sample from the application area, and the attributes used to describe example cases should be relevant and adequate for the classification task to be solved. In this paper, measures of the strength of association and an entropy-based approach have been used to assess the quality of the training data. Studied classification tasks related to three otological data sets: a conscript data set, a vertigo data set, and a postoperative nausea and vomiting data set. The paper suggests that the studied approaches give some guidelines about the quality of the training data, but other approaches are also needed to guide training data building.
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