Mining for knowledge to build decision support system for diagnosis and treatment of tinnitus

Tinnitus problems affect a significant portion of the population and are difficult to treat. Treatment processes are plentiful, yet not completely understood. In this dissertation, we present a knowledge discovery approach which can be used to build a decision support system for supporting tinnitus treatment. Our approach is based on a significant enlargement of the initial tinnitus database by adding many new tables containing new temporal features related to tinnitus evaluation and treatment outcome. Research presented in this thesis includes knowledge discovery with temporal, text, and quantitative data from a patient dataset of 3013 visits representing 758 unique patient tuples. Additionally, a new rule generating technique and clustering methods are presented and used to develop additional new temporal features and knowledge in this complex domain. Of particular interest is the role that emotions play in treatment success for tinnitus following the TRT method developed by Dr. Pawel Jastreboff. The ultimate goal of understanding the relationships among the treatment factors and measurements in order to better understand tinnitus treatment will result in the design foundations of a decision support system to aid in tinnitus treatment effectiveness

Mining and analysis of audiology data to find significant factors associated with tinnitus masker

Objectives: The objective of this research is to find the factors associated with tinnitus masker from the literature, and by using the large amount of audiology data available from a large NHS (National Health Services, UK) hearing aid clinic. The factors evaluated were hearing impairment, age, gender, hearing aid type, mould and clinical comments. Design: The research includes literature survey for factors associated with tinnitus masker, and performs the analysis of audiology data using statistical and data mining techniques. Setting: This research uses a large audiology data but it also faced the problem of limited data for tinnitus. Participants: It uses 1,316 records for tinnitus and other diagnoses, and 10,437 records of clinical comments from a hearing aid clinic. Primary and secondary outcome measures: The research is looking for variables associated with tinnitus masker, and in future, these variables can be combined into a single model to develop a decision support system to predict about tinnitus masker for a patient. Results: The results demonstrated that tinnitus maskers are more likely to be fit to individuals with milder forms of hearing loss, and the factors age, gender, type of hearing aid and mould were all found significantly associated with tinnitus masker. In particular, those patients having Age<=55 years were more likely to wear a tinnitus masker, as well as those with milder forms of hearing loss. ITE (in the ear) hearing aids were also found associated with tinnitus masker. A feedback on the results of association of mould with tinnitus masker from a professional audiologist of a large NHS (National Health Services, UK) was also taken to better understand them. The results were obtained with different accuracy for different techniques. For example, the chi-squared test results were obtained with 95% accuracy, for Support and Confidence only those results were retained which had more than 1% Support and 80% Confidence. Conclusions: The variables audiograms, age, gender, hearing aid type and mould were found associated with the choice of tinnitus masker in the literature and by using statistical and data mining techniques. The further work in this research would lead to the development of a decision support system for tinnitus masker with an explanation that how that decision was obtained

AUTOMATED META-ACTIONS DISCOVERY FOR PERSONALIZED MEDICAL TREATMENTS

Healthcare, among other domains, provides an attractive ground of work for knowl- edge discovery researchers. There exist several branches of health informatics and health data-mining from which we find actionable knowledge discovery is underserved. Actionable knowledge is best represented by patterns of structured actions that in- form decision makers about actions to take rather than providing static information that may or may not hint to actions. The Action rules model is a good example of active structured action patterns that informs us about the actions to perform to reach a desired outcome. It is augmented by the meta-actions model that rep- resents passive structured effects triggered by the application of an action. In this dissertation, we focus primarily on the meta-actions model that can be mapped to medical treatments and their effects in the healthcare arena. Our core contribution lies in structuring meta-actions and their effects (positive, neutral, negative, and side effects) along with mining techniques and evaluation metrics for meta-action effects. In addition to the mining techniques for treatment effects, this dissertation provides analysis and prediction of side effects, personalized action rules, alternatives for treat- ments with negative outcomes, evaluation for treatments success, and personalized recommendations for treatments. We used the tinnitus handicap dataset and the Healthcare Cost and Utilization Project (HCUP) Florida State Inpatient Databases (SID 2010) to validate our work. The results show the efficiency of our methods

A practical application of text mining to literature on cognitive rehabilitation and enhancement through neurostimulation

The exponential growth in publications represents a major challenge for researchers. Many scientific domains, including neuroscience, are not yet fully engaged in exploiting large bodies of publications. In this paper, we promote the idea to partially automate the processing of scientific documents, specifically using text mining (TM), to efficiently review big corpora of publications. The “cognitive advantage” given by TM is mainly related to the automatic extraction of relevant trends from corpora of literature, otherwise impossible to analyze in short periods of time. Specifically, the benefits of TM are increased speed, quality and reproducibility of text processing, boosted by rapid updates of the results. First, we selected a set of TM-tools that allow user-friendly approaches of the scientific literature, and which could serve as a guide for researchers willing to incorporate TM in their work. Second, we used these TM-tools to obtain basic insights into the relevant literature on cognitive rehabilitation (CR) and cognitive enhancement (CE) using transcranial magnetic stimulation (TMS). TM readily extracted the diversity of TMS applications in CR and CE from vast corpora of publications, automatically retrieving trends already described in published reviews. TMS emerged as one of the important non-invasive tools that can both improve cognitive and motor functions in numerous neurological diseases and induce modulations/enhancements of many fundamental brain functions. TM also revealed trends in big corpora of publications by extracting occurrence frequency and relationships of particular subtopics. Moreover, we showed that CR and CE share research topics, both aiming to increase the brain's capacity to process information, thus supporting their integration in a larger perspective. Methodologically, despite limitations of a simple user-friendly approach, TM served well the reviewing process

Tinnitus: network pathophysiology-network pharmacology

Tinnitus, the phantom perception of sound, is a prevalent disorder. One in 10 adults has clinically significant subjective tinnitus, and for one in 100, tinnitus severely affects their quality of life. Despite the significant unmet clinical need for a safe and effective drug targeting tinnitus relief, there is currently not a single Food and Drug Administration (FDA)-approved drug on the market. The search for drugs that target tinnitus is hampered by the lack of a deep knowledge of the underlying neural substrates of this pathology. Recent studies are increasingly demonstrating that, as described for other central nervous system (CNS) disorders, tinnitus is a pathology of brain networks. The application of graph theoretical analysis to brain networks has recently provided new information concerning their topology, their robustness and their vulnerability to attacks. Moreover, the philosophy behind drug design and pharmacotherapy in CNS pathologies is changing from that of “magic bullets” that target individual chemoreceptors or “disease-causing genes” into that of “magic shotguns,” “promiscuous” or “dirty drugs” that target “disease-causing networks,” also known as network pharmacology. In the present work we provide some insight into how this knowledge could be applied to tinnitus pathophysiology and pharmacotherapy

Applicability of Immersive Analytics in Mixed Reality: Usability Study

Nowadays, visual analytics is mainly performed by programming approaches and viewing the results on a desktop monitor. However, due to the capabilities of smart glasses, new user interactions and representation possibilities become possible. This refers especially to 3D visualizations in the medical field, as well as, the industry domain, as valuable depth information can be related to the complex real-world structures and related data, which is also denoted as immersive analytics. However, the applicability of immersive analytics and its drawbacks, especially in the context of mixed reality, are quite unexplored. In order to validate the feasibility of immersive analytics for the aforementioned purposes, we designed and conducted a usability study with 60 participants. More specifically, we evaluated the effects of spatial sounds, performance changes from one analytics task to another, expert status, and compared an immersive analytics approach (i.e., a mixed-reality application) with a desktop-based solution. Participants had to solve several data analytics tasks (outlier’s detection and cluster recognition) with the developed mixed-reality application. Thereby, the performance measures regarding time, errors, and movement patterns were evaluated. The separation into groups (low performer vs. high performer) was performed using a mental rotation pretest. When solving analytic tasks in mixed reality, participants changed their movement patterns in the mixed reality setting significantly, while the use of spatial sounds reduced the handling time significantly, but did not affect the movement patterns. Furthermore, the usage of mixed reality for cluster recognition is significantly faster than the desktop-based solution (i.e., a 2D approach). Moreover, the results obtained with self-developed questionnaires indicate 1) that wearing smart glasses are perceived as a potential stressor and 2) that the utilization of sounds is perceived very differently by the participants. Altogether, industry and researchers should consider immersive analytics as a suitable alternative compared to the traditional approaches

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

Presbycusis patterns in the portuguese population: identification and association with epidemiological and genetic factors

Trabalho de projeto de mestrado, Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2017Presbycusis or Age-Related Hearing Loss (ARHL) is the most prevalent sensorial impairment in the elderly, affecting more than 30% of people older than 65 years old. This condition has a negative impact on quality of life, which may lead to social isolation and the development of some psychiatric disorders. Although there are several studies based on prevalence of Hearing Loss (HL), only a few studies based on audiogram configurations or HL pattern were made. The main aim of this study was to identify dominant audiogram patterns. Furthermore, based on that a classification procedure was build relying in a sample of 321 individuals aged between 62 and 115 years old. The obtained classification was validated through Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) and then compared with audiogram pattern identification procedures existing in the literature. Finally, some statistical models were adjusted to the data in order to investigate the influence of demographic, environmental, medical and genetic factors in both, audiogram pattern and mean quantity of HL. In this study, the overall prevalence of presbycusis was 79.1%, being significantly different among age groups, increasing gradually with aging. The most common audiogram configuration was High Frequency Steeply Sloping (HFSS) (51.2%), followed by High Frequency Gently Sloping (HFGS) (29.6%) and FLAT (14.5%). Through cluster analysis techniques it was possible to identify three distinct groups of audiogram patterns. These patterns were significantly associated with gender and noise exposure. Besides the audiogram pattern, the mean quantity of HL, increases with the age of the individuals. The results suggest the existence of three main audiogram patterns, significantly associated with gender and noise exposure and confirm the positive association between age and HL prevalence or mean amount of HL.A Presbiacusia ou Perda Auditiva Associada ao Envelhecimento é a limitação sensorial mais comum, afetando mais de 30% das pessoas com idade superior a 65 anos. Esta condição tem um impacto negativo na qualidade de vida dos indivíduos, podendo levar ao isolamento social e ao desenvolvimento de doenças neurodegenerativas. Embora existam alguns estudos cujo objetivo tenha sido determinar a prevalência da perda auditiva na população, poucos foram efetuados com o intuito de investigar o padrão de perda ou a configuração do audiograma. O objetivo deste trabalho consistiu na identificação de padrões dominantes de perda auditiva recorrendo à análise de clusters e construção de um procedimento de classificação com base numa amostra de 321 indivíduos com idade compreendida entre os 62 e 115 anos. A classificação obtida foi validada com recurso à análise de componentes principais e à análise discriminante, e posteriormente, comparada com procedimentos de identificação de padrões descritos na literatura. Por fim, foram ajustados alguns modelos estatísticos com o intuito de investigar a influência de fatores demográficos, ambientais, clínicos e genéticos quer nos padrões determinados, quer na perda auditiva média. Neste estudo, a prevalência de presbiacusia foi de 79.1% sendo significativamente diferente entre faixas etárias, verificando-se um aumento gradual com o avançar da idade. A configuração do audiograma mais comum foi a HFSS (51.2%), seguida da HFGS (29.6%) e da FLAT (14.5%). Através de técnicas de análise de clusters foi possível identificar a existência de três grupos distintos de padrões de audiograma. A distribuição dos indivíduos em cada um desses grupos foi associada significativamente ao género e à exposição ao ruído. Independentemente do padrão, verificou-se que a perda auditiva média dependia da idade. Os resultados sugerem a existência de três padrões de presbiacusia, significativamente associados ao género e à exposição ao ruído, e confirmam, a associação positiva existente entre a idade e a ocorrência de perda auditiva ou perda média auditiva