Розроблення методу визначення стилю автора україномовних текстів на основі технологій лінгвометрії, стилеметрії та глоттохронології

We solved the problem of development of algorithmic software for processes of content monitoring for solving the problem of recognition of the style of an author of a Ukrainian text based on Web Mining and NLP technology. Decomposition of the method for recognition of the style of an author, based of analysis of the found stop words, was carried out. Specific features of the method include adaptation of morphological and syntactic analysis of lexical units to structural peculiarities of words/ texts in Ukrainian. It is syntactic words (stop words or anchor words) that are significant for an author’s individual style, as they are not related to the theme and content of the publication. Recognition of the author's style is based on analysis of coefficients of lexical author’s language: coherence of speech, lexical diversity, syntactic complexity indices of concentration and exclusivity for the author's fragment. They are used for subsequent comparison and determining of a degree of belonging of the analyzed text to a particular author. We studied internal "dynamics" of a text of randomly selected authors through analysis of coefficients of lexical author’s language for the first k, n and m (without the title) words of the author's fragment and the analyzed one. The obtained results were compared. We obtained results of experimental testing of the proposed method for content-monitoring for determining and analysis of stop words in Ukrainian scientific texts of technical area based on Web Mining technology. It was found that for the selected experimental base that contains 100 works, the method for analysis of an article without compulsory initial information and list of references attains the best results by density criterion. It is achieved through learning of the system and by checking specified blocked words and specified thematic vocabulary. Testing of the proposed method for determining of keywords from other categories of texts – of scientific humanitarian area, belles-lettres, journalistic, etc. – requires subsequent experimental research.Рассмотрены особенности применения технологий лингвометрии, стилеметрии и глоттохронологии для определения стиля автора публикаций. Лингвостатистический анализ авторского текста использует преимущества контент-мониторинга на основе методов NLP для определения стоповых слов. Квантитативный анализ стоповых слов использовано для определения степени принадлежности анализируемого текста конкретному автору. Предложен метод определения стиля автора украиноязычного текстаРозглянуто особливості застосування технологій лінгвометрії, стилеметрії та глоттохронології для визначення стилю автора публікацій. Лінгвостатистичний аналіз авторського тексту використовує переваги контент-моніторінгу на основі методів NLP для визначення стопових слів. Квантитативний аналіз стопових слів використано для визначення степеня приналежності аналізованого тексту конкретному авторові. Запропоновано метод визначення стилю автора україномовного текст

Intelligent Systems Approach for Classification and Management of Patients with Headache

Primary headache disorders are the most common complaints worldwide. The socioeconomic and personal impact of headache disorders is enormous, as it is the leading cause of workplace absence. Headache patients’ consultations are increasing as the population has increased in size, live longer and many people have multiple conditions, however, access to specialist services across the UK is currently inequitable because the numbers of trained consultant neurologists in the UK are 10 times lower than other European countries. Additionally, more than two third of headache cases presented to primary care were labelled with unspecified headache. Therefore, an alternative pathway to diagnose and manage patients with primary headache could be crucial to reducing the need for specialist assessment and increase capacity within the current service model. Several recent studies have targeted this issue through the development of clinical decision support systems, which can help non-specialist doctors and general practitioners to diagnose patients with primary headache disorders in primary clinics. However, the majority of these studies were following a rule-based system style, in which the rules were summarised and expressed by a computer engineer. This style carries many downsides, and we will discuss them later on in this dissertation. In this study, we are adopting a completely different approach. The use of machine learning is recruited for the classification of primary headache disorders, for which a dataset of 832 records of patients with primary headaches was considered, originating from three medical centres located in Turkey. Three main types of primary headaches were derived from the data set including Tension Type Headache in both episodic and chronic forms, Migraine with and without Aura, followed by Trigeminal Autonomic Cephalalgia that further subdivided into Cluster headache, paroxysmal hemicrania and short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing. Six popular machine-learning based classifiers, including linear and non-linear ensemble learning, in addition to one regression based procedure, have been evaluated for the classification of primary headaches within a supervised learning setting, achieving highest aggregate performance outcomes of AUC 0.923, sensitivity 0.897, and overall classification accuracy of 0.843. This study also introduces the proposed HydroApp system, which is an M-health based personalised application for the follow-up of patients with long-term conditions such as chronic headache and hydrocephalus. We managed to develop this system with the supervision of headache specialists at Ashford hospital, London, and neurology experts at Walton Centre and Alder Hey hospital Liverpool. We have successfully investigated the acceptance of using such an M-health based system via an online questionnaire, where 86% of paediatric patients and 60% of adult patients were interested in using HydroApp system to manage their conditions. Features and functions offered by HydroApp system such as recording headache score, recording of general health and well-being as well as alerting the treating team, have been perceived as very or extremely important aspects from patients’ point of view. The study concludes that the advances in intelligent systems and M-health applications represent a promising atmosphere through which to identify alternative solutions, which in turn increases the capacity in the current service model and improves diagnostic capability in the primary headache domain and beyond