Mining balance disorders' data for the development of diagnostic decision support systems

In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts

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

Improving Benign Paroxysmal Positional Vertigo Diagnosis

Benign Paroxysmal Positional Vertigo (BPPV) is one of the most common causes of dizziness. Especially for people over 45, the risk of BPPV is substantial. On the other hand, BPPV is often misdiagnosed and may require expensive examinations. This thesis introduces a prediction model based on machine learning to quickly, inexpensively, and accurately diagnose BPPV. The thesis starts by introducing BPPV and the statistics of BPPV misdiagnosis. Then, a patient survey is introduced. The patient survey includes 50 BPPV-related questions, which are used as training data for the machine learning model. Logistic Regression, Decision Tree, and Naïve Bayes were compared for machine learning models and their results were discussed. Three machine learning approaches are explored, logistic regression, decision tree, and naïve Bayes with cross validation accuracies of 89.8%, 81.9%, and 75.1%, respectively

Pendekatan Metode Bayes untuk Menentukan Jenis Penyakit pada Ternak Babi

Penelitian ini bertujuan untuk membuat sebuah model penentuan jenis penyakit yang dialami oleh ternak babi. Metode yang digunakan adalah metode bayes. Gejala-gejala yang digunakan sesuai dengan gejala umum yang terdapat pada ternak babi. Metode penelitian dilakukan dengan proses observasi dan wawancara pada pelaku ternak babi. Selain itu dilakukan wawancara dengan dokter hewan yang mengetahui persis tentang ternak babi. Diambil 2 tempat ternak babi yang ada di Kota Kupang dan 1 di Kabupaten Kupang. Penelitian ini belum mewakili semua ternak babi secara keseluruhan di Kota dan Kabupaten Kupang. Namun bisa dijadikan sebuah model penentuan jenis penyakit sebuah ternah babi.Penelitian ini belum sampai pada sebuah solusi terhadap penyakit ternak babi. Penelitian ini masih dikembangkan sampai pada tingkat diagnosis penykait ternak babi. Dan pada akhirnya dibuatkan sebuah aplikasi. Hasil penelitian ini masih sebatas model penentuan penykait ternak babi berdasarkan gejala yang dialami dan diamati secara fisik pada ternak bab

Parameterizing neural networks for disease classification

Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set-up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency

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)

Benign Paroxysmal Positional Vertigo Predictive Diagnosis from Patient-facing Survey

Benign Paroxysmal Positional Vertigo (BPPV) is a leading cause of dizziness and imbalance that is responsible for one-third of fall incidents. Diagnosis, however, is ridden with uncertainties and errors. This thesis explores various techniques for BPPV predictive diagnosis from a survey study and proposes measures for predictive performance improvement. Patient-facing surveys are established ways of acquiring medical history in clinical settings and, as this thesis demonstrates, are capable of conveying patterns distinguishable for accurate diagnosis. This work begins by discussing BPPV and vestibular disorders in general, and the risks associated with misdiagnosis or elusive diagnosis. Innovative efforts by medical professionals in vestibular therapy for handling the intricacies of diagnosis and clinical protocols are also explained. To predict BPPV successfully, there are distinguishing marks present in a patient’s dizziness episodic history including the frequency and duration of episodes, the specific nature of the dizziness, and the positional trigger. Given these indicators for predicting BPPV, we develop a number of statistical models on a dataset of survey responses acquired from a clinical cohort study. Next, the thesis establishes a connection between the performance limits of the machine learning methods, and the existence of incorrect answers to the survey prompts. By demonstrating that question misinterpretation and ambiguities exist in the cohort study, we show that certain data quality improvement measures have significant influence on classification performance

Bayesian Networks for Health Care Support.

PhDBayesian Networks (BNs) have been considered as a potentially useful technique in the health service domain since they were invented. Many authors have presented BNs for managing health care and waiting time, predicting outcomes, improving treatment recommendation process and many more. Despite all these development effort, BNs have been rarely applied to provide support in any of these clinical areas. This thesis investigates the use of BNs for analysing clinical evidence data from observational studies, currently considered the type of study proving the weakest evidence. It begins by investigating challenges around the analysis of data and evidence faced by health professionals in health service. It then discusses the importance of observational studies to understand how disease, treatments and other clinical factors interact with each other. Further it describes the various techniques, such as using statistical inference methods and clinical judgements, available to justify any discovered interactions. In contrary to Frequentist approaches, Bayesian Networks can combine knowledge and data to derive evidence of relationships between different factors. This thesis proposes a novel way to combine knowledge and observational data in Bayesian Networks to derive evidence for clinical queries. Firstly, it shows how to construct and refine a Bayesian Network model by performing hypothesis tests to check which out of a number of experts’ judged causal relations between a set of domain variables are plausible for the available observational data. Secondly, it proposes techniques to evaluate the strength of all plausible relations/associations. Finally, it shows how these techniques are combined into a novel data analysis method for answering clinical queries by combining knowledge with data. In order to illustrate this method this thesis uses a case study and data about the operation of a multidisciplinary team (MDT) that provided treatment recommendations to cancer patients, at Barts and the London HPB Centre over five years. In summary, the case study shows the potential for the method and allows us to propose ways to present results in a comprehensible format.ImpactQ

Machine learning approaches for early DRG classification and resource allocation

Recent research has highlighted the need for upstream planning in healthcare service delivery systems, patient scheduling, and resource allocation in the hospital inpatient setting. This study examines the value of upstream planning within hospital-wide resource allocation decisions based on machine learning (ML) and mixed-integer programming (MIP), focusing on prediction of diagnosis-related groups (DRGs) and the use of these predictions for allocating scarce hospital resources. DRGs are a payment scheme employed at patients’ discharge, where the DRG and length of stay determine the revenue that the hospital obtains. We show that early and accurate DRG classification using ML methods, incorporated into an MIP-based resource allocation model, can increase the hospital’s contribution margin, the number of admitted patients, and the utilization of resources such as operating rooms and beds. We test these methods on hospital data containing more than 16,000 inpatient records and demonstrate improved DRG classification accuracy as compared to the hospital’s current approach. The largest improvements were observed at and before admission, when information such as procedures and diagnoses is typically incomplete, but performance was improved even after a substantial portion of the patient’s length of stay, and under multiple scenarios making different assumptions about the available information. Using the improved DRG predictions within our resource allocation model improves contribution margin by 2.9% and the utilization of scarce resources such as operating rooms and beds from 66.3% to 67.3% and from 70.7% to 71.7%, respectively. This enables 9.0% more nonurgent elective patients to be admitted as compared to the baseline

Pendekatan metode bayes untuk menentukan jenis penyakit pada ternak babi

Penelitian ini bertujuan untuk membuat sebuah model penentuan jenis penyakit yang dialami oleh ternak babi. Metode yang digunakan adalah metode bayes. Gejala-gejala yang digunakan sesuai dengan gejala umum yang terdapat pada ternak babi. Metode penelitian dilakukan dengan proses observasi dan wawancara pada pelaku ternak babi. Selain itu dilakukan wawancara dengan dokter hewan yang mengetahui persis tentang ternak babi. Diambil 2 tempat ternak babi yang ada di Kota Kupang dan 1 di Kabupaten Kupang. Penelitian ini belum mewakili semua ternak babi secara keseluruhan di Kota dan Kabupaten Kupang. Namun bisa dijadikan sebuah model penentuan jenis penyakit sebuah ternah babi.Penelitian ini belum sampai pada sebuah solusi terhadap penyakit ternak babi. Penelitian ini masih dikembangkan sampai pada tingkat diagnosis penykait ternak babi. Dan pada akhirnya dibuatkan sebuah aplikasi. Hasil penelitian ini masih sebatas model penentuan penykait ternak babi berdasarkan gejala yang dialami dan diamati secara fisik pada ternak bab