Yrjö Auramo

Otoneurological expert system.

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.

Database for a hearing conservation program

We have developed a database and an analysis program (NoiseScan) for noise-induced hearing loss (NIHL). The exposure data are based on the evaluation of the noise immission level, which includes duration, frequency content, and the use of, and the attenuation performance of, hearing protectors. The input data can handle an unlimited number of exposure periods. If the noise exposure level is not known, the program lists noise levels of comparable work places, and thus provides an estimate of exposure. Confounding medical factors that may contribute to NIHL, such as elevated serum cholesterol level, hypertension, and extensive use of pain killers, are collected. Combined exposure to agents that clearly contribute to NIHL, such as hand-arm vibration, tobacco smoking, use of aminoglycosides and exposure to solvents are also assessed. An unlimited number of audiograms can be stored, and all the data can be completed and edited following collection. The program gives the predicted hearing loss according to the ISO 1999 model based on total exposure. At present, our NoiseScan program (under continuous development in an EU research program) is suitable for the data collection of various risk factors. It can be used to determine whether the hearing loss is occupational in origin and to estimate the efficiency of hearing conservation measures. NoiseScan also predicts the development of hearing loss in individuals in 5-year periods. The goal is to improve and validate the rules by which single and combined risk factors contribute to HIHL, thus leading to more precise prediction of individual hearing loss, and for the evaluation of success of the hearing conservation programs.

Positive experiences and quality of life in Ménière's disorder.

Abstract The objective of this study was to determine the influence of positive experiences on the quality of life of patients with Ménières disorder. Its deign comprised a cross-sectional investigation of members of a self-help group, using a questionnaire of positive experiences, an oto-neurological questionnaire, EuroQol, and Ménières impact rating. The study sample was 542 members of the Finnish Ménière Federation. Results showed that the positive questions explained 21% of the variance of the EuroQol TTO, 31% of the EuroQol VAS measure, and 35% of the Ménières impact. This compared with 40% of the variance explained by symptomatic complaints. When both the symptoms and positive measures were taken together, there was an increase in the proportion of the variance explained for the VAS (42%) and Ménières rating (50%), but not for the TTO (42%). A perspective on the disorder was the main positive experience associated with the increased variance explained. Positive experiences have a significant impact on self-rated quality of life. The results suggest that encouraging a positive perspective on the condition, in addition to treating the underlying symptoms, could be useful in rehabilitative management. Sumario El objetivo de este estudio fue determinar la influencia de las experiencias positivas en la calidad de vida de los pacientes con el trastorno de Ménière. Su diseño involucra una investigación transversal de los miembros de un grupo de auto-ayuda, utilizando un cuestionario de experiencias positivas, un cuestionario de oto-neurología, el EuroQoL, y la escala de calificación del impacto del Ménière. La muestra del estudio fue de 542 miembros de la Federación Finlandesa de Ménière. Los resultados mostraron que las preguntas positivas explicaron el 21% de la varianza del EuroQoL TTO, 31% de la medición EuroQoL VAS, y 35% de la escala de impacto de Ménière. Esto se compara con 40% de la varianza explicada por quejas sintomáticas. Cuando se juntaron tanto los síntomas como las medidas positivas, existió una aumento en la proporción de la varianza explicada por el VAS (42%) sobre elescala Ménière (50%), pero no para el TTO (42%). Una perspectiva del trastorno fue la principal experiencia positiva asociada con el aumento de la varianza explicada. Los experiencias positivas tiene un impacto significativo en la calidad de vida auto estimada. Los resultados sugieren que la estimulación de una perspectiva positiva de la condición, además de tratar los síntomas subyacentes, puede ser útil en el manejo rehabilitativo.

Tinnitus complaint behaviour in long‐standing Menière’s disorder: its association with the other cardinal symptoms

Clin. Otolaryngol. 2011, 36, 461–467

Neural Network Recognition of Otoneurological Vertigo Diseases with Comparison of Some Other Classification Methods

We have studied computer-aided diagnosis of otoneurological diseases which are difficult, even for experienced specialists, to determine and separate from each other. Since neural networks require plenty of training data, we restricted our research to the commonest otoneurological diseases in our database and to the very most essential parameters used in their diagnostics. According to our results, neural networks can be efficient in the recognition of these diseases provided that we shall be able to add our available cases concerning those diseases which are rare in our database. We compared the results yielded by neural networks to those given by discriminant analysis, genetic algorithms and decision trees.

Application of artificial intelligence in audiology

In this paper, machine learning methods based on artificial intelligence theory are applied to the computer-aided decision making of some otoneurological diseases, for example Me´nie`res disease. Three methods explored are decision trees, genetic algorithms and neural networks. By using such a machine learning method, the decision-making program is trained with a representative training set of cases and tested with another set. The machine learning methods are useful also for our otoneurological expert system, One, which is based on a pattern recognition approach. The methods are able to differentiate most of the cases tested between the six diseases included, provided that a sufficiently large training set is available.