On Knowledge Discovery Experimented with Otoneurological Data

Diagnosis of otoneurological diseases can be challenging due to similar kind of and overlapping symptoms that can also vary over time. Thus, systems to support and aid diagnosis of vertiginous patients are considered beneficial. This study continues refinement of an otoneurological decision support system ONE and its knowledge base. The aim of the study is to improve the classification accuracy of nine otoneurological diseases in real world situations by applying machine learning methods to knowledge discovery in the otoneurological domain. The phases of the dissertation is divided into three parts: fitness value formation for attribute values, attribute weighting and classification task redefinition. The first phase concentrates on the knowledge update of the ONE with the domain experts and on the knowledge discovery method that forms the fitness values for the values of the attributes. The knowledge base of the ONE needed update due to changes made to data collection questionnaire. The effect of machine learnt fitness values on classification are examined and classification results are compared to the knowledge set by the experts and their combinations. Classification performance of nearest pattern method of the ONE is compared to k-nearest neighbour method (k-NN) and Naïve Bayes (NB). The second phase concentrates on the attribute weighting. Scatter method and instance-based learning algorithms IB4 and IB1w are applied in the attribute weighting. These machine learnt attribute weights in addition to the weights defined by the domain experts and equal weighting are tested with the classification method of the ONE and attribute weighted k-NN with One-vs-All classifiers (wk-NN OVA). Genetic algorithm (GA) approach is examined in the attribute weighting. The machine learnt weight sets are utilized as a starting point with the GA. Populations (the weight sets) are evaluated with the classification method of the ONE, the wk-NN OVA and attribute weighted k-NN using neighbour’s class-based attribute weighting (cwk-NN). In the third phase, the effect of the classification task redefinition is examined. The multi-class classification task is separated into several binary classification tasks. The binary classification is studied without attribute weighting with the k-NN and support vector machines (SVM)

Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review.

Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis

Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry

BACKGROUND Spontaneous episodic vertigo syndromes, namely vestibular migraine (VM) and Menière's disease (MD), are difficult to differentiate, even for an experienced clinician. In the presence of complex diagnostic information, automated systems can support human decision making. Recent developments in machine learning might facilitate bedside diagnosis of VM and MD. METHODS Data of this study originate from the prospective patient registry of the German Centre for Vertigo and Balance Disorders, a specialized tertiary treatment center at the University Hospital Munich. The classification task was to differentiate cases of VM, MD from other vestibular disease entities. Deep Neural Networks (DNN) and Boosted Decision Trees (BDT) were used for classification. RESULTS A total of 1357 patients were included (mean age 52.9, SD 15.9, 54.7% female), 9.9% with MD and 15.6% with VM. DNN models yielded an accuracy of 98.4 ± 0.5%, a precision of 96.3 ± 3.9%, and a sensitivity of 85.4 ± 3.9% for VM, and an accuracy of 98.0 ± 1.0%, a precision of 90.4 ± 6.2% and a sensitivity of 89.9 ± 4.6% for MD. BDT yielded an accuracy of 84.5 ± 0.5%, precision of 51.8 ± 6.1%, sensitivity of 16.9 ± 1.7% for VM, and an accuracy of 93.3 ± 0.7%, precision 76.0 ± 6.7%, sensitivity 41.7 ± 2.9% for MD. CONCLUSION The correct diagnosis of spontaneous episodic vestibular syndromes is challenging in clinical practice. Modern machine learning methods might be the basis for developing systems that assist practitioners and clinicians in their daily treatment decisions

Entwicklung und Testung eines datenbasierten Diagnosealgorithmus für vestibuläre Erkrankungen im Bereich der hausarztzentrierten Versorgung

Mit einer Lebenszeitprävalenz von etwa 30% und steigender Inzidenz mit dem Alter ist Schwindel eines der häufigsten Leitsymptome und stellt für Patient:innen eine schwere Beeinträchtigung des täglichen Lebens dar [1]. Die oft unklare Symptomlage und fehlende Erfahrung im hausärztlichen Bereich führt häufig zu falscher Einordnung, somit zu erfolglosen Behandlungsversuchen [2]. Die Diagnose wird dadurch erschwert, dass sich häufig Symptome verschiedener Schwindelerkrankungen überlagern [4]. Somit ist es von Interesse, Ärzt:innen Möglichkeiten an die Hand zu geben, dass sie Schwindelerkrankungen in einem ersten Schritt zuverlässiger einordnen können. Die ersten Ansätze für die Klassifizierung von Schwindelerkrankungen mit den anfangs verfügbaren einfachen Modellen waren jedoch nicht vielversprechend genug, um weiter verfolgt zu werden [9]. Die Fragestellung dieser Arbeit war es daher, zu untersuchen, ob diese Verfahren in ihren weiterentwickelten Formen anhand symptomorientierter Charakteristika der Patient:innen dazu in der Lage sind, vestibuläre Erkrankungen zu differenzieren. Es war zudem wichtig, welche vestibulären Symptome besonders relevant für die Vorhersagegüte sein könnten. In beiden Studien wurden Daten aus der DizzyReg Patientendatenbank des Deutschen Schwindel- und Gleichgewichtszentrum verwendet [14]. Aus der Vielzahl der Machine Learning Modelle [13, 18] fiel die Wahl für die Differenzierung von Morbus Menière und vestibulärer Migräne, aufgrund ihrer Leistung in anderen medizinischen Anwendungen [9], auf die Modelle Deep Neural Networks [13] und der Boosted Decision Trees [29]. Für die Klassifizierung mit möglichst großer Transparenz zur Entscheidung und die Ermittlung der Relevanz der Variablen wählten wir Classification and Regression Trees und Random Forests aus. CART bieten den Vorteil, dass sie eine visuelle Darstellung bereitstellen, die die menschliche Entscheidungsfindung nachbildet [25]. Gemittelt über alle fünf imputierten Datensätze ergab sich mit einem DNN für MM ein F-Measure von 55,5% (Accuracy 91,4%). Zum Vergleich erreichten wir mit DNN für VM ein F-Measure von 36,8% (Accuracy 81,8%). Boosted Decision Trees, trainiert auf VM, ergaben lediglich ein F-Measure von 27,6% und eine Accuracy von 84,5%. Aus den Experimenten mit den CART wurden acht Variablen als relevant ermittelt. Als übergeordnete Genauigkeit bei der Klassifizierung aller sieben Diagnosen ergab eine Genauigkeit von 42,2%. DNN lieferten sehr gute Klassifizierungsergebnisse, aber aufgrund der inhärenten Struktur des Modells nicht die geforderte Transparenz. Die bei der gleichzeitigen Klassifizierung der häufigsten Diagnosen verwendeten Algorithmen basieren auf einem transparenten Ansatz. Sie können für eine initiale Triage der Patient:innen verwendet werden, aber es muss sich eine klinische Untersuchung der vestibulären und okularen Funktionen anschließen, um die Genauigkeit der Diagnose zu verbessern. Für eine abschließende Beurteilung ist noch weitere Forschung not-wendig.With a lifetime prevalence of about 30% and an increasing incidence with age, dizziness is one of the most common symptoms and represents a severe impairment of daily life for patients [1]. The often-unclear symptoms and lack of experience with general practitioners often lead to incorrect classification and thus to unsuccessful attempts at treatment [2]. The diagnosis is complicated by the fact that symptoms of different vertigo diseases often overlap [4]. Therefore, it is of interest to provide general practitioners with possibilities to classify dizziness more reliably in a first step. However, the initial approaches to classify vertigo disorders with the sim-ple models available at the beginning were not promising enough to be pursued further [9]. The question of this work was therefore to investigate whether these methods in their more developed forms can differentiate vestibular disorders based on symptom-oriented patient characteristics. It is also important to determine which vestibular symptoms might be particular-ly relevant to predictive accuracy. In both studies, data from the DizzyReg patient database of the German Schwindel- und Gleichgewichtszentrum were used [14]. From the multitude of machine learning models [13, 18], the choice for differentiating Meniere's disease and vestibular migraine fell on the Deep Neural Networks and Boosted Decision Trees [29] models, due to their performance in other medical applications [9]. We selected Classification and Regression Trees for classification with the greatest possible transparency and for determining the relevance of variables. They offer the advantage of providing a visual representation that mimics human decision making [25]. Averaged over all five imputed datasets, a DNN for Menière’s Disease yielded an F-measure of 55.5% (accuracy 91.4%). In comparison, with DNN for Vestibular Migraine we achieved an F-Measure of 36.8% (Accuracy 81.8%). Boosted decision trees trained on Vestibular Migraine yielded only an F-Measure of 27.6% and an accuracy of 84.5%. Eight variables were determined to be relevant from the experiments with the CART. The overall accuracy when classifying all seven diagnosis yielded a result of 42.2%. DNN achieved very good classification results but did not provide the required transparency due to the inherent structure of the model. The algorithms used in the classification of the most common diagnoses are based on a transparent approach. They can be used for initial triage of patients but must be followed by clinical examination of vestibular and ocular functions to im-prove the accuracy of the diagnosis. More research is needed to make a final assessment

Computational models and approaches for lung cancer diagnosis

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results

Development and benchmarking a novel scatter search algorithm for learning probabilistic graphical models in healthcare

Healthcare data of small sizes are widespread, and the challenge of building accurate inference models is difficult. Many machine learning algorithms exist, but many are black boxes. Explainable models in healthcare are essential, so healthcare practitioners can understand the developed model and incorporate domain knowledge into the model. Probabilistic graphical models offer a visual way to represent relationships between data. Here we develop a new scatter search algorithm to learn Bayesian networks. This machine learning approach is applied to three case studies to understand the effectiveness in comparison with traditional machine learning techniques. First, a new scatter search approach is presented to construct the structure of a Bayesian network. Statistical tests are used to build small Directed acyclic graphs combined in an iterative process to build up multiple larger graphs. Probability distributions are fitted as the graphs are built up. These graphs are then scored based on classification performance. Once no new solutions can be found, the algorithm finishes. The first study looks at the effectiveness of the scatter search constructed Bayesian network against other machine learning algorithms in the same class. These algorithms are benchmarked against standard datasets from the UCI Machine Learning Repository, which has many published studies. The second study assesses the effectiveness of the scatter search Bayesian network for classifying ovarian cancer patients. Multiple other machine learning algorithms were applied alongside the Bayesian network. All data from this study were collected by clinicians from the Aneurin Bevan University Health Board. The study concluded that machine-learning techniques could be applied to classify patients based on early indicators. The third and final study looked into applying machine learning techniques to no-show breast cancer follow-up patients. Once again, the scatter search Bayesian network was used alongside other machine learning approaches. Socio-demographic and socio-economic factors involving low to middle-income families were used in this study with feature selection techniques to improve machine learning performance. It was found machine learning, when used with feature selection, could classify no-show patients with reasonable accuracy

Stratification of patient subgroups using high-dimensional and time-series observations

Precision medicine and patient stratification are expanding as a result of innovations in high-throughput technologies applied to clinical medicine. Stratification can explain differences in disease trajectories and outcomes in heterogeneous cohorts. Thus, approaches employed for patient treatment can be tailored by taking into account individual variabilities and specificities. This thesis focuses on clustering approaches and how they can be applied to both single time points and time-series high-dimensional data for the identification of disease subtypes defined by distinct mechanisms, also called endotypes, in complex and/or heterogeneous diseases. Multiple carefully selected clustering strategies were compared to highlight which would produce the most relevant stratification in terms of mathematical robustness and biological meaning, both of which quantified using standardised methods. More specifically, this strategy was applied to time-series multi-omics data from a cohort of patients with acute pancreatitis, an inflammatory disease of the pancreas. Using this high-dimensional multi-omics data as well as routine lab and clinical measurements, the cohort was stratified into four subgroups. Findings from the analysis of acute pancreatitis data showed that two of the four subgroups could be detected in another syndrome, acute respiratory distress syndrome, suggesting that inflammatory signatures are comparable between diseases. With the aim of applying these principles to other diseases and using preliminary results from other studies suggesting that relevant subgroups might be highlighted, data from inflammatory bowel disease and Parkinson's disease cohorts was analysed. Results from our analyses confirmed that disease knowledge could be gained using this approach. Work from this thesis provides novel approaches for the application and evaluation of stratification methods. Furthermore, results may constitute a basis for the development of tailored treatment approaches for acute pancreatitis, acute respiratory distress syndrome, inflammatory bowel disease and Parkinson’s disease. Also, the observation of commonalities between distinct inflammatory diseases will broaden the perspectives when analysing disease data and more specifically, in biomarker discovery and drug development processes