scholarly journals Machine learning and deep learning techniques to support the clinical diagnosis of arboviral diseases: A systematic review

2021 ◽  
Author(s):  
Sebastião Rogério da Silva Neto ◽  
Thomás Tabosa Oliveira ◽  
Igor Vitor Teixeira ◽  
Samuel Benjamin Aguiar de Oliveira ◽  
Vanderson Souza Sampaio ◽  
...  
Keyword(s):  
Clinical Diagnosis ◽  
Rural Areas ◽  
Binary Classification ◽  
Clinical Decision ◽  
Conflict Zones ◽  
Use Of Resources ◽  
Learning Techniques ◽  
Arboviral Diseases

Abstract Background: NTDs primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on ML and DL models. Method: We carried out a SLR in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and 7 from single backward snowballing technique), only 15 relevant papers were identified. Results: Results show that current research is focused on the binary classification of Dengue, primarily using Tree based ML algorithms and only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its levels) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life.

scholarly journals Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review

2022 ◽  
Vol 16 (1) ◽  
pp. e0010061
Author(s):  
Sebastião Rogério da Silva Neto ◽  
Thomás Tabosa Oliveira ◽  
Igor Vitor Teixeira ◽  
Samuel Benjamin Aguiar de Oliveira ◽  
Vanderson Souza Sampaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Diagnosis ◽  
Rural Areas ◽  
Neglected Tropical Diseases ◽  
Conflict Zones ◽  
Use Of Resources ◽  
Arboviral Diseases

Background Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. Method We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. Results Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life.

scholarly journals Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Jesús Leonardo López-Hernández ◽  
Israel González-Carrasco ◽  
José Luis López-Cuadrado ◽  
Belén Ruiz-Mezcua
Keyword(s):  
Machine Learning ◽  
Positive Emotions ◽  
Machine Learning Techniques ◽  
Visual Disabilities ◽  
Brain Signals ◽  
Learning Techniques ◽  
Sensory Disabilities ◽  
Recognition Of Emotions

Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.

Theoretical and Methodological Approach to Rural Life Support Research

2020 ◽  
pp. 43-47
Author(s):  
A.V. Turyanskiy ◽  
◽  
I.N. Merenkova ◽  
A.I. Dobrunova ◽  
A.A. Sidorenko ◽  
...  
Keyword(s):  
Rural Areas ◽  
Rural Population ◽  
Life Support ◽  
Social Economic ◽  
Methodological Approach ◽  
Rural Life ◽  
Rural Residents ◽  
Use Of Resources ◽  
Support Research

The article justifies the need for a theoretical and methodological approach to the study of the life support of rural residents as a system that takes into account social, economic, environmental and institutional specifics. A model of life support of the rural population characterizing the structure of its components is presented. A methodological approach has been proposed and a system of indicators has been defined linking the use of resources to meet the basic needs of rural residents and the quality of their lives. Rural areas of the region were typed according to the level of life support of the population, which allowed to identify the degree of their differentiation.

scholarly journals An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Umashankar Subramaniam ◽  
M. Monica Subashini ◽  
Dhafer Almakhles ◽  
Alagar Karthick ◽  
S. Manoharan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Left Lung ◽  
X Ray ◽  
Detection Algorithms ◽  
Learning Techniques ◽  
Accuracy Of Results

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.

scholarly journals Negative mpMRI Rules Out Extra-Prostatic Extension in Prostate Cancer Before Robot-Assisted Radical Prostatectomy

2022 ◽  
Author(s):  
Eoin Dinneen ◽  
Clare Allen ◽  
Tom Strange ◽  
Daniel Heffernan-Ho ◽  
Jelena Banjeglav ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Binary Classification ◽  
Characteristic Curve ◽  
Robot Assisted ◽  
Diagnostic Quality ◽  
Prostate Imaging ◽  
Likert Score ◽  
Operative Staging

The accuracy of multi-parametric MRI (mpMRI) in pre-operative staging of prostate cancer (PCa) remains controversial. Objective: To evaluate the ability of mpMRI to accurately predict PCa extra-prostatic extension (EPE) on a side-specific basis using a risk-stratified 5-point Likert scale. This study also aimed to assess the influence of mpMRI scan quality on diagnostic accuracy. Patients and Methods: We included 124 men who underwent robot-assisted RP (RARP) as part of the NeuroSAFE PROOF study at our centre. Three radiologists retrospectively reviewed mpMRI blinded to RP pathology and assigned a Likert score (1-5) for EPE on each side of the prostate. Each scan was also ascribed a Prostate Imaging Quality (PI-QUAL) score for assessing the quality of the mpMRI scan, where 1 represents poorest and 5 represents best diagnostic quality. Outcome measurements and statistical analyses: Diagnostic performance is presented for binary classification of EPE including 95% confidence intervals and area under the receiver operating characteristic curve (AUC). Results: A total of 231 lobes from 121 men (mean age 56.9 years) were evaluated. 39 men (32.2%), or 43 lobes (18.6%) had EPE. Likert score ≥3 had sensitivity (SE), specificity (SP), NPV, PPV of 90.4%, 52.3%, 96%, 29.9%, respectively, and AUC was 0.82 (95% CI: 0.77-0.86). AUC was 0.63 (95% CI: 0.37-0.9), 0.77 (0.71-0.84) and 0.92 (0.88-0.96) for biparametric scans, PI-QUAL 1-3 and PI-QUAL 4-5 scans, respectively. Conclusions: MRI can be used effectively by genitourinary radiologists to rule out EPE and help inform surgical planning for men undergoing RARP. EPE prediction was more reliable when the MRI scan was a) multi-parametric and b) of a higher image quality according to the PI-QUAL scoring system.

scholarly journals Text Classification by Genre Based on Rhythm Features

2021 ◽  
Vol 28 (3) ◽  
pp. 280-291
Author(s):  
Ksenia Vladimirovna Lagutina ◽  
Nadezhda Stanislavovna Lagutina ◽  
Elena Igorevna Boychuk
Keyword(s):  
Binary Classification ◽  
Software Tool ◽  
Statistical Features ◽  
Good Marker ◽  
Text Corpora ◽  
Text Model ◽  
Multi Class Classification ◽  
Almost All

The article is devoted to the analysis of the rhythm of texts of different genres: fiction novels, advertisements, scientific articles, reviews, tweets, and political articles. The authors identified lexico-grammatical figures in the texts: anaphora, epiphora, diacope, aposiopesis, etc., that are markers of the text rhythm. On their basis, statistical features were calculated that describe quantitatively and structurally these rhythm features.The resulting text model was visualized for statistical analysis using boxplots and heat maps that showed differences in the rhythm of texts of different genres. The boxplots showed that almost all genres differ from each other in terms of the overall density of rhythm features. Heatmaps showed different rhythm patterns across genres. Further, the rhythm features were successfully used to classify texts into six genres. The classification was carried out in two ways: a binary classification for each genre in order to separate a particular genre from the rest genres, and a multi-class classification of the text corpus into six genres at once. Two text corpora in English and Russian were used for the experiments. Each corpus contains 100 fiction novels, scientific articles, advertisements and tweets, 50 reviews and political articles, i.e. a total of 500 texts. The high quality of the classification with neural networks showed that rhythm features are a good marker for most genres, especially fiction. The experiments were carried out using the ProseRhythmDetector software tool for Russian and English languages. Text corpora contains 300 texts for each language.

scholarly journals Predicting Theropod Hunting Tactics using Machine Learning.

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Matthew Millar
Keyword(s):  
Machine Learning ◽  
Test Subject ◽  
Behavioral Pattern ◽  
Machine Learning Techniques ◽  
Actual Behavior ◽  
Quality Of Data ◽  
Learning Techniques ◽  
Biomechanical Features

The use of machine learning in different fields is becoming a more common practice thanks to Big Data and better granularity in data being collected. The application of machine learning to animal behavioral pattern analysis is becoming more popular due to the increase in size, types, and quality of data that can be gathered. Machine learning can even be used to predict the actual behavior of animals based off of certain features. This approach can also be used for predicting the behavior of extinct animals. This paper is the goal is to explore the possibility of using machine learning techniques to predict the hunting habits of dinosaurs based solely off of physical characteristic of the animal. By using the biomechanical features, a model can be created to aid in the classification of animals into either a scavenger or hunter roles. The results from the test show that there is a strong correlation between the physical characteristics and potential hunting habits. The models used here can then be used as a good baseline in predicting other theropods based solely on their bodies. The T-Rex was used as the test subject and was correctly classified as a primary hunter in most of the models.

scholarly journals Quality management of electricity service with photovoltaic generation distributed in rural area

2020 ◽  
Vol 4 (1) ◽  
pp. 39-44
Author(s):  
Gino Joaquín Mieles ◽  
Alcira Magdalena Vélez Quiroz ◽  
Ciaddy Gina Rodríguez Borges ◽  
Antonio Vázquez Pérez
Keyword(s):  
Rural Areas ◽  
Radical Change ◽  
Poor Quality ◽  
Energy Planning ◽  
Electricity Grid ◽  
Energy Models ◽  
Use Of Resources ◽  
New Energy ◽  
Good Living

The need to search for new energy models that are integrally sustainable for the present and the future, especially photovoltaic solar energy that would contribute to a radical change in Manabí Ecuador where populations are living in rural areas away from the electricity grid, which causes impacts negative economic and in some rural electrification projects, and low quality have oriented national policies towards the search for the best alternatives, such as renewable sources, that is, the efficient use of resources and the increase in reliability, coverage, and quality in the electrical supply that Manabí has. Emphasizing "good living" as an objective of the Ecuadorian government, meeting its needs for the development of its agricultural, artisanal, commercial and industrial activities. Thus, avoiding that due to lack or poor quality of energy that prevents them from being able to carry out an activity typical of the countryside or rural areas, these people migrate to the cantonal headwaters, further thickening the cords of misery. The work presents an analysis on the quality of the electric service in isolated areas of the Chone municipality, proposing solutions that can improve the quality of the service, through sustainable energy planning using indigenous resources from the territory.

Sound Distortion Errors in Aphasia With Apraxia of Speech

2019 ◽  
Vol 28 (1) ◽  
pp. 121-135 ◽  
Author(s):  
Katarina L. Haley ◽  
Michael Smith ◽  
Julie L. Wambaugh
Keyword(s):  
Diagnostic Criteria ◽  
Binary Classification ◽  
Apraxia Of Speech ◽  
Motor Speech ◽  
Phonetic Transcription ◽  
Replicated Observations

Purpose Loosely defined diagnostic criteria for acquired apraxia of speech (AOS) limit clinicians' ability to diagnose the disorder validly and reliably. The purpose of this study was to contribute to the development of more precise diagnostic guidelines by characterizing the frequency and quality of sound distortion errors in speakers with clinically diagnosed AOS. Method Audio-recorded motor speech evaluations from 24 speakers with AOS and aphasia were analyzed by trained listeners using a narrow phonetic transcription protocol that included 12 distortion categories. We calculated percentage of segments transcribed with phonemic error, distortion error, and a combination of phonemic and distortion error. Results Distortion frequency varied substantially across participants, distributing on a continuum from 5% to 22% of segments. The frequency of phonemic errors was significantly greater than the frequency of distortion errors, which, in turn, was greater than the frequency of distorted substitution errors. The most common distortion qualities were voicing ambiguity and segment lengthening, but over 40% of distortion errors were distributed across an assortment of tongue modifications. Conclusions The results replicated observations from previous studies of speakers with quantitatively defined AOS in a new sample of participants with clinically diagnosed AOS. Similar distortion qualities were observed across studies, offering focus for diagnosticians and guidance for operationalizing future measures. The broad performance continua we observed help explain why binary classification of the presence/absence of AOS can be challenging and indicate a need to develop quantitative norms.

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