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Purpose: In this article, we present the details and the pilot outcome of an Internet-based self-help program for Ménière's disease (MD). Method: The Norton--Kaplan model is applied to construct a strategic, person-focused approach in the enablement process. The program assesses the disorder profile and diagnosis. In the therapeutic component of the program, the participant defines vision and time frame, inspects confounding factors, determines goals, establishes a strategy, and starts to work on the important problems caused by the disorder. The program works interactively, utilizes collaboration with significant others, and enhances positive thinking. Participants took part in an Internet-based self-help program. Data were collected interactively using open-ended and structured questionnaires on various disease-specific and general health aspects. The pilot outcome of 41 patients with MD was evaluated. Results: The analysis of the pilot data showed statistically significant improvement in their general health-related quality of life (p < .001). Also, the outcome of the Posttraumatic Growth Inventory (Cann et al., 2010) showed small to moderate change as a result of the intervention. Conclusions: The Internet-based self-help program can be helpful in the rehabilitation of patients with MD to supplement medical therapy. [ABSTRACT FROM AUTHOR]

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 Ménière’s 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. [ABSTRACT FROM AUTHOR]

A novel machine learning system, Galactica, has been developed for knowledge discovery from databases. This system was applied to discover diagnostic rules from a patient database containing 564 cases with vestibular schwannoma, bening paroxysmal positional vertigo, Me´nie`re’s disease, sudden deafness, traumatic vertigo and vestibular neuritis diagnoses. The rules were evaluated using an independent testing set. The accuracy of rules for these diagnoses were 91%, 96%, 81%, 95%, 92% and 98%, respectively. Besides being accurate, the rules contained the five most important diagnostic questions identified in the earlier research. The knowledge presented with rules can be easily comprehended and verified. [ABSTRACT FROM AUTHOR]

Decision tree induction is a machine learning method used to generate classification models from data sets. Numerous decision trees were constructed to examine relationships between oculomotor test parameters and lesion sites in a data set containing cases with operated cerebello-pontine angle tumour, operated hemangioblastoma, infarction of cerebello-brainstem and Me´nie`re’s disease, and control subjects. The aim was to find useful parameter combinations with discriminatory power. Decision trees constructed using both pursuit eye movements and saccadic eye movements yielded the best classification results. This is reasonable: oculomotor test results vary according to the site of the lesion and so the performance ability of subjects has to be taken into account in the classification. The decision tree program was able to generate classification models from the oculomotor data set. Generated decision trees were intelligible and can be utilized in physicians’ research work. [ABSTRACT FROM AUTHOR]

We have developed an OtoNeurological Expert system (ONE) to aid the diagnostics of vertigo, to assist teaching and to implement the database for research. The database contains detailed information on the patient history, signs and test results necessary for the diagnostic work with vertiginous patients. The pattern recognition method was used in the reasoning process. Questions regarding symptoms, signs and test results are weighted and scored for each disease, and the most likely disease is recognized from the defined disease profiles. Uncertainties in reasoning, caused by missing information, were solved with a method resembling fuzzy logic. We have also applied adaptive computer applications, such as genetic algorithms and decision trees, in the reasoning process. In the validation the expert system ONE proved to be a sound decision maker, by solving 65% of the cases correctly, while the physicians’ mean was 69%. To improve the expert system ONE further, a follow-up should be implemented for the patients, to ease the diagnostic work of some difficult diseases. The six diseases were detected with high accuracy also with adaptive learning methods and discriminant analysis. An expert system is a practical tool in otoneurology. We aim to construct a hybrid program for the reasoning, where the best reasoning method for each disease is used. [ABSTRACT FROM AUTHOR]

Background: Adverse events are common in health care. In psychiatric treatment, compensation claims for patient injuries appear to be less common than in other medical specialties. The most common types of patient injury claims in psychiatry include diagnostic flaws, unprevented suicide, or coercive treatment deemed as unnecessary or harmful., Objectives: The objective was to study whether it is possible to form different categories of patient injury types associated with the psychiatric evaluations of compensation claims and to base machine learning classification on these categories. Further, the binary classification of positive and negative decisions for compensation claims was the other objective., Methods: Finnish psychiatric specialist evaluations for the compensation claims of patient injuries were classified into six different categories called classes applying the machine learning methods of artificial intelligence. In addition, another classification of the same data into two classes was performed to test whether it was possible to classify data cases according to their known decisions, either accepted or declined compensation claim., Results: The former classification task produced relatively good classification results subject to separating between different classes. Instead, the latter was more complex. However, classification accuracies of both tasks could be improved by using the generation of artificial data cases in the preprocessing phase before classifications. This preprocessing improved the classification accuracy of six classes up to 88% when the method of random forests was used for classification and that of the binary classification to 89%., Conclusion: The results show that the objectives defined were possible to solve reasonably., Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

We extracted a collection of eye movement signals employed for almost two decades in clinical otoneurological tests at a balance laboratory. During those years we designed and programmed signal analysis methods to analyse their features in detail and to compute medically important attributes. In the present study, using such attributes and their results computed we classified test cases into groups of healthy subjects and patients with multilayer perceptron neural networks. Classification succeeded in total accuracies from 60% to 90% depending on the type of eye movements, which were saccades, nystagmus, sinusoidal movements and vestibulo-ocular reflex stimulated in two different ways; these are the chief eye movement tests applied in otoneurology.

We constructed a connectionist model with multilayer perceptron networks for the simulation of word production errors in the Finnish language. Such errors occur as slips of the tongue in healthy subjects and even more often in aphasic patients suffering from brain damage. Word production errors are of theoretical interest as they open an avenue to inner workings of the language production system. Here we present our coding schemes for semantic and phoneme information of words, investigate the general properties of the model, and compare the results of the model against empirical naming error data from ten aphasic patients. The model was reasonable in simulating word production errors in this heterogeneous group of aphasic patients.

It is frequently useful and advantageous to investigate not only the classification efficacy of neural networks, but also the reasons for misclassification and relations between input variables and output classes. We have developed novel techniques to disentangle these dilemmas: a network structure and learning strategy for biased output class distributions, a method to measure the classification information incorporated in variables and variable groups, and methods to express properties learned by a network from its structure. We tested these techniques with otoneurological data from the conjunction with vertiginous diseases that we have explored in our previous neural network studies.

We constructed a signal analysis and recognition system to compute revealing results from vestibulo-ocular reflex eye movements and their stimulation head movements of impulsive type that were generated as passive head movements with a special device designed by us for this purpose. Further, we implemented perceptron neural networks to separate healthy subjects from patients suffering from dysfunction of vestibulo-ocular reflex in either ear. We gained high classification accuracies with this method ready for routine use.

In the next study we consider two distance metrics that were presented in the machine leaming literature for mixed-type variables. We show that they are not really metrics, but pseudometrics. The problem arose from missing values. The metrics can be redefined to satisfy the metricity definition. Distance computation can then be performed reliably without a possibility that a distance between exactly similar patient cases would not be zero. We experimented with both procedures and so-called ignore mode using two medical data sets and observed that the redefined procedure was as good as the original approach as measured with the classification ability of one-nearest neighbour searching. It is noticeable that the described problem caused by missing values can be with any distance measure if its missing values are treated in the pessimistic manner as in the original version of these distance measures.

The present study investigates the prediction of how successfully hearing returns to normal after an acute acoustic trauma caused by a firearm shot, an explosion or other impulse noise. Study material consisted of 113 conscripts who had suffered an acute acoustic trauma during their military service. A logistic regression model was formed to predict hearing recovery. From several possible variables at least five were found to be important among our dataset, which is still rather limited. However, more data will be acquired later in order to elaborate the research more widely by using other methods also.