scholarly journals Technologies for Complex Intelligent Clinical Data Analysis

2016 ◽  
Vol 71 (2) ◽  
pp. 160-171 ◽  
Author(s):  
A. A. Baranov ◽  
L. S. Namazova-Baranova ◽  
I. V. Smirnov ◽  
D. A. Devyatkin ◽  
A. O. Shelmanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Chronic Diseases ◽  
Clinical Information ◽  
Structured Data ◽  
Unstructured Data ◽  
Intelligent Analysis ◽  
Pediatric Center

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient’s features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented.Background: Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality.Aims: the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center.Materials and methods: Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as «negation» (indicates that the disease is absent), «no patient» (indicates that the disease refers to the patient’s family member, but not to the patient), «severity of illness», «disease course», «body region to which the disease refers». Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the method for determining the most informative patients’ features are also proposed.Results: Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records of patients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases.Conclusions: The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare. 

TRAFFIC FLOWS FORECASTING BASED ON MACHINE LEARNING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Traffic Flows ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Packet Arrival ◽  
Entire Flow ◽  
The Mean ◽  
Active Flow

The article aims to develop a model for forecasting the characteristics of traffic flows in real-time based on the classification of applications using machine learning methods to ensure the quality of service. It is shown that the model can forecast the mean rate and frequency of packet arrival for the entire flow of each class separately. The prediction is based on information about the previous flows of this class and the first 15 packets of the active flow. Thus, the Random Forest Regression method reduces the prediction error by approximately 1.5 times compared to the standard mean estimate for transmitted packets issued at the switch interface.

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.

scholarly journals Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Theyazn H.H Aldhyani ◽  
Ali Saleh Alshebami ◽  
Mohammed Y. Alzahrani
Keyword(s):  
Machine Learning ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Upper Approximation ◽  
Healthcare Data ◽  
Ambiguous Objects

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people’s lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

scholarly journals MACHINE LEARNING IMPLEMENTATION FOR THE CLASSIFICATION OF ATTACKS ON WEB SYSTEMS. PART 1

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
K. Smirnova ◽  
A. Smirnov ◽  
O. Olshevska
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Web Application ◽  
Main Idea ◽  
Approximate Model ◽  
Training Sample ◽  
Network Layers ◽  
Entire Network

The possibility of applying machine learning is considered for the classification of malicious requests to a Web application. This approach excludes the use of deterministic analysis systems (for example, expert systems), and based on the application of a cascade of neural networks or perceptrons on an approximate model to the real human brain. The main idea of the work is to enable to describe complex attack vectors consisting of feature sets, abstract terms for compiling a training sample, controlling the quality of recognition and classifying each of the layers (networks) participating in the work, with the ability to adjust not the entire network, But only a small part of it, in the training of which a mistake or inaccuracy crept in.  The design of the developed network can be described as a cascaded, scalable neural network.  The developed system of intrusion detection uses a three-layer neural network. Layers can be built independently of each other by cascades. In the first layer, for each class of attack recognition, there is a corresponding network and correctness is checked on this network. To learn this layer, we have chosen classes of things that can be classified uniquely as yes or no, that is, they are linearly separable. Thus, a layer is obtained not just of neurons, but of their microsets, which can best determine whether is there some data class in the query or not. The following layers are not trained to recognize the attacks themselves, they are trained that a set of attacks creates certain threats. This allows you to more accurately recognize the attacker's attempts to bypass the defense system, as well as classify the target of the attack, and not just its fact. Simple layering allows you to minimize the percentage of false positives.

scholarly journals LOCALIZATION OF SOIL MONITORING DATA PROCESSING

2021 ◽  
pp. 22-25
Author(s):  
V. Fartukov ◽  
N. Hanov
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Processing ◽  
The State ◽  
Monitoring Data ◽  
Soil Monitoring ◽  
Monitoring Data Processing ◽  
State Of The Field

A tree of data analysis for the formation and preprocessing, storage and protection of data based on Big Data and Blockchain technologies has been developed. The developed algorithm allows for the classification of data on the state of the field, split testing of data, forecasting and machine learning for the implementation of differential irrigation with sprinklers.

scholarly journals Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing

2020 ◽  
Vol 59 (S 02) ◽  
pp. e64-e78
Author(s):  
Antje Wulff ◽  
Marcel Mast ◽  
Marcus Hassler ◽  
Sara Montag ◽  
Michael Marschollek ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Data ◽  
Expert Knowledge ◽  
Clinical Information ◽  
Structured Data ◽  
Unstructured Data ◽  
Mapping Rules ◽  
Medical Histories

Abstract Background Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. Objectives The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. Methods We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. Results We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. Conclusion The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.

Evaluation and classification of otoneurological data with new data analysis methods based on machine learning

2007 ◽  
Vol 177 (9) ◽  
pp. 1963-1976 ◽  
Author(s):  
Markku Siermala ◽  
Martti Juhola ◽  
Jorma Laurikkala ◽  
Kati Iltanen ◽  
Erna Kentala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Analysis Methods ◽  
Data Analysis Methods

Postmarital Life Challenges After Foster Care Discharge: A Qualitative Study

2021 ◽  
pp. 104438942096830
Author(s):  
Fardin Alipour ◽  
Negin Khoramdel ◽  
Maliheh Arshi ◽  
Mohammad Sabzi Khoshnami
Keyword(s):  
Quality Of Life ◽  
Content Analysis ◽  
Data Analysis ◽  
Foster Care ◽  
Qualitative Content Analysis ◽  
Life Challenges ◽  
Semistructured Interviews ◽  
History Of

Many children have entered foster care centers due to different reasons, and they will experience new conditions after leaving these centers. This research explored the experiences of the postmarital life of women with a history of residence in foster care centers. It was conducted using a qualitative content analysis. The data were collected through semistructured interviews with 21 former foster care women and experts. Data analysis was performed using coding and classification of codes. The main extracted theme was “Life in Suspension.” The extracted codes were placed in 10 subcategories and three categories, including (a) spoiled identity, (b) social pressures, and (c) unstable marital life. The need for planning to reduce the various challenges of this group and increase their quality of life, both during and after foster care centers, is essential.

scholarly journals A Review : Classification of Food Grains and Quality Prediction

2020 ◽  
pp. 34-37
Author(s):  
Mahalaxmi P P ◽  
Kavita D. Hanabaratti
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
Quality Prediction ◽  
Food Grains ◽  
Geometrical Features ◽  
Image Procession

This review paper discuss about recent techniques and methods used for grain classification and grading. Grains are important source of nutrients and they play important role in healthy diet. The production of grains across worldwide each year is in terms of hundreds of millions. The common method to classify these hugely produced grains is manual which is mind-numbing and not accurate. So the automated system is required which can classify the verities and predict the quality (i.e. grade A, grade B) of grain fast and accurate. As machine learning had done most of the difficult things easy, many machine learning algorithms can be used which can easily classify and predict the quality of grains. The system uses colour and geometrical features like size and area of grains as attributes for classification and quality prediction. Here, several image procession methods and machine learning algorithms are reviewed.

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