7,479 research outputs found
An application of a hybrid intelligent system for diagnosing primary headaches
[Abstract] (1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F1 score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F1 score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.Ministerio de Ciencia e Innovación; MINECO-TIN2017-84804-RGobierno del Principado de Asturias; FCGRUPIN-IDI/2018/000226Serbia. Ministry of Education, Science and Technological Development; 451-03-68/2020-14/20015
A Comparison of Performances of Different Feature Selection Methods applied to Biomedical Data
Migraine is a debilitating disease whose causes are not yet completely explained. Near-InfraRed Spectroscopy (NIRS) is a non-invasive technology commonly used for the assessment of the cerebral autoregulation during active stimuli.
Feature Selection (FS) allows dimensionality reduction of multivariate datasets, highlighting the most informative variables and deleting redundant and irrelevant information. Rough Set Theory (RST) is one of the most used tool for FS, enables to manage incomplete and imperfect knowledge without any assumption about data model.
This study involved a total of 80 subjects, divided in 3 groups: 15 healthy subjects taken as controls, 14 women suffered from migraine without aura and 51 women from migraine with aura. We apply three different methods of FS based on RST to a set of 26 parameters extracted from NIRS signals recorded in the subjects during breath-holding (BH) and hyperventilation (HYP). We compare the extracted subsets of features in the subjects’ classification by means of Artificial Neural Networks. The results show good performance for all subsets, with a percentage of correct classification above the 90%
Predicting \u27Attention Deficit Hyperactive Disorder\u27 using large scale child data set
Attention deficit hyperactivity disorder (ADHD) is a disorder found in children affecting about 9.5% of American children aged 13 years or more. Every year, the number of children diagnosed with ADHD is increasing. There is no single test that can diagnose ADHD. In fact, a health practitioner has to analyze the behavior of the child to determine if the child has ADHD. He has to gather information about the child, and his/her behavior and environment. Because of all these problems in diagnosis, I propose to use Machine Learning techniques to predict ADHD by using large scale child data set. Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of disease. Lot of new approaches have immerged which allows to develop understanding and provides opportunity to do advanced analysis. Use of classification model in detection has made significant impacts in the detection and diagnosis of diseases. I propose to use binary classification techniques for detection and diagnosis of ADHD
Direct-to-Patient Survey for Diagnosis of Benign Paroxysmal Positional Vertigo
Given the high incidence of dizziness and its frequent misdiagnosis, we aim to create a clinical support system to classify the presence or absence of benign paroxysmal positional vertigo with high accuracy and specificity. This paper describes a three-phase study currently underway for classification of benign paroxysmal positional vertigo, which includes diagnosis by a specialist in a clinical setting. Patient background information is collected by a survey on an Android tablet and machine learning techniques are applied for classification. Decision trees and wrappers are employed for their ability to provide information about the question set. One goal of the study is to attain an optimal question set. Each phase of the study presents a unique set and style of questions. Results achieved in the first two phases of the survey indicate that our approach using decision trees with filters or wrappers does a good job of identifying benign paroxysmal positional vertigo
Somatosensory tinnitus: current evidence and future perspectives
In some individuals, tinnitus can be modulated by specific maneuvers of the temporomandibular joint, head and neck, eyes, and limbs. Neuroplasticity seems to play a central role in this capacity for modulation, suggesting that abnormal interactions between the sensory modalities, sensorimotor systems, and neurocognitive and neuroemotional networks may contribute to the development of somatosensory tinnitus. Current evidence supports a link between somatic disorders and higher modulation of tinnitus, especially in patients with a normal hearing threshold. Patients with tinnitus who have somatic disorders seems to have a higher chance of modulating their tinnitus with somatic maneuvers; consistent improvements in tinnitus symptoms have been observed in patients with temporomandibular joint disease following targeted therapy for temporomandibular disorders. Somatosensory tinnitus is often overlooked by otolaryngologists and not fully investigated during the diagnostic process. Somatic disorders, when identified and treated, can be a valid therapeutic target for tinnitus; however, somatic screening of subjects for somatosensory tinnitus is imperative for correct selection of patients who would benefit from a multidisciplinary somatic approach
A case-based reasoning system for recommendation of data cleaning algorithms in classification and regression tasks
Recently, advances in Information Technologies (social networks, mobile applications, Internet of Things, etc.) generate a deluge of digital data; but to convert these data into useful information for business decisions is a growing challenge. Exploiting the massive amount of data through knowledge discovery (KD) process includes identifying valid, novel, potentially useful and understandable patterns from a huge volume of data. However, to prepare the data is a non-trivial refinement task that requires technical expertise in methods and algorithms for data cleaning. Consequently, the use of a suitable data analysis technique is a headache for inexpert users. To address these problems, we propose a case-based reasoning system (CBR) to recommend data cleaning algorithms for classification and regression tasks. In our approach, we represent the problem space by the meta-features of the dataset, its attributes, and the target variable. The solution space contains the algorithms of data cleaning used for each dataset. We represent the cases through a Data Cleaning Ontology. The case retrieval mechanism is composed of a filter and similarity phases. In the first phase, we defined two filter approaches based on clustering and quartile analysis. These filters retrieve a reduced number of relevant cases. The second phase computes a ranking of the retrieved cases by filter approaches, and it scores a similarity between a new case and the retrieved cases. The retrieval mechanism proposed was evaluated through a set of judges. The panel of judges scores the similarity between a query case against all cases of the case-base (ground truth). The results of the retrieval mechanism reach an average precision on judges ranking of 94.5% in top 3, for top 7 84.55%, while in top 10 78.35%.The authors are grateful to the research groups: Control Learning Systems Optimization Group (CAOS) of the Carlos III University of Madrid and Telematics Engineering Group (GIT) of the University of Cauca for the technical support. In addition, the authors are grateful to COLCIENCIAS for PhD scholarship granted to PhD. David Camilo Corrales. This work has been also supported by: Project Alternativas Innovadoras de Agricultura Inteligente para sistemas productivos agrĂcolas del departamento del Cauca soportado en entornos de IoT financed by Convocatoria 04C-2018 Banco de Proyectos Conjuntos UEES-Sostenibilidad of Project Red de formaciĂłn de talento humano para la innovaciĂłn social y productiva en el Departamento del Cauca InnovAcciĂłn Cauca, ID-3848. The Spanish Ministry of Economy, Industry and Competitiveness (Projects TRA2015-63708-R and TRA2016-78886-C3-1-R)
Psychodynamic psychotherapy for children and adolescents: an updated narrative review of the evidence-base
While the evidence base for psychodynamic therapy with adults is now quite
substantial, there is still a lack of research evaluating the effectiveness of
psychodynamic therapies with children and young people. Those studies that have been
carried out are also not widely known in the field. To help address the second point, in
2011, we carried out a review of the evidence base for psychodynamic psychotherapy
for children and adolescents, which identified 35 studies which together provided some
preliminary evidence for this treatment for a range of childhood disorders. The present
study is an updated review, focusing on research published between March 2011 and
November 2016. During this period, 23 additional studies were published, of which 5
were reports on randomised controlled trials, 3 were quasi-experimental controlled
studies and 15 were observational studies. Although most studies covered children
with mixed diagnoses, there were a number of studies examining specific diagnostic
groups, including children with depression, anxiety and disruptive disorders. whilst
the quality of studies was mixed, some were well-designed and reported, and overall
indicated promising findings. Nevertheless, further high-quality research is needed
in order to better understand the effectiveness of psychodynamic psychotherapy
across a range of different disorders, and to ensure that services can provide a range of
evidence-based treatments for children and young people
Beyond a symptom count:addressing the difficulties of measuring Functional Somatic Symptoms
Functional Somatic Symptoms (FSS) are a constellation of symptoms such as pain, fatigue, and dizziness, which are not explained by objectively measurable underlying pathology (also called medically unexplained). These symptoms are very heterogeneous, and therefore, difficult to assess. The overall aim of this thesis was to address three methodological issues in the assessment of FSS, namely: 1) The heterogeneity of FSS and the problems of using sum-scores, 2) the uncertainty concerning which symptoms are most relevant for FSS assessment, and 3) the ambiguity of the meaning of sum-scores for different subgroups. To address these issues, sophisticated psychometric methods from Item Response Theory were applied to three FSS questionnaires. The findings suggest that items related to weakness, heaviness in extremities, and nausea consistently provide accurate measurements about FSS severity. Moreover, it was found that some symptoms could be biased by sex, which means that these items may be more accurate at measuring FSS severity in either males or females. Joint pain was found not to be very informative when measuring FSS in older adults. Finally, we found that participants did not tend to cluster in subgroups based on their symptoms (e.g., gastrointestinal or musculoskeletal), but based on their overall severity of symptoms. This means that the severity of FSS symptoms could be more important for identifying and classifying individuals than the combination of symptoms. These results can contribute to the improvement of measurement tools for assessing FSS
Investigating patients’ preferences to inform drug development decisions: novel Insights from a discrete choice experiment in migraine
Abstract: There is limited evidence on the scope and overall benefit of patient-centred drug development decisions. The present study assessed patients’ preferences for the characteristics of an ideal
migraine treatment through a discrete choice experiment in order to inform decision-making and
drug development processes. We investigated the preferences according to five treatment attributes
identified from a systematic literature review and two focus group elicitations. The heterogeneity
of preferences was also investigated. Overall, the respondents considered the presence of adverse
events, duration of treatment effect, reduction of symptom intensity, speed of effect and cost born
by the patient as the most relevant treatment features. As expected, the patients preferred treatments with lower levels of adverse events and costs and treatments with greater speed, duration of
treatment effect and effectiveness in reducing symptom intensity. There was significant preference
heterogeneity only for the presence of adverse events. Compared to men, women had significantly
higher preferences for quicker treatment effect and limited adverse events and reported higher
preferences for costly treatments. The results of our survey help address research and development
strategies in the pharmaceutical industry and public policy regarding treatments that are clinically
effective and responsive to the needs expressed by patients
Perspectives on next steps in classification of oro-facial pain - part 1: role of ontology
The purpose of this study was to review existing principles of oro-facial pain classifications and to specify design recommendations for a new system that would reflect recent insights in biomedical classification systems, terminologies and ontologies. The study was initiated by a symposium organised by the International RDC/TMD Consortium Network in March 2013, to which the present authors contributed. The following areas are addressed: problems with current classification approaches, status of the ontological basis of pain disorders, insufficient diagnostic aids and biomarkers for pain disorders, exploratory nature of current pain terminology and classification systems, and problems with prevailing classification methods from an ontological perspective. Four recommendations for addressing these problems are as follows: (i) develop a hypothesis-driven classification structure built on principles that ensure to our best understanding an accurate description of the relations among all entities involved in oro-facial pain disorders; (ii) take into account the physiology and phenomenology of oro-facial pain disorders to adequately represent both domains including psychosocial entities in a classification system; (iii) plan at the beginning for field-testing at strategic development stages; and (iv) consider how the classification system will be implemented. Implications in relation to the specific domains of psychosocial factors and biomarkers for inclusion into an oro-facial pain classification system are described in two separate papers
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