Handling Diagnosis of Schizophrenia by a Hybrid Method

Psychotics disorders, most commonly known as schizophrenia, have incapacitated professionals in different sectors of activities. Those disorders have caused damage in a microlevel to the individual and his/her family and in a macrolevel to the economic and production system of the country. The lack of early and sometimes very late diagnosis has provided reactive measures, when the professional is already showing psychological signs of incapacity to work. This study aims to help the early diagnosis of psychotics’ disorders with a hybrid proposal of an expert system that is integrated to structured methodologies in decision support (multicriteria decision analysis: MCDA) and knowledge structured representations into production rules and probabilities (artificial intelligence: AI)

Unveiling the Emotional and Psychological States of Instagram Users: A Deep Learning Approach to Mental Health Analysis

People can now communicate with others who have common tastes to them and engage in conversation together while furthermore exchanging ideas, photos, and clips that convey their emotional states due to social media’s technology. As a consequence, there is an opportunity to investigate person sentiments and thoughts in social networking sites data in order to understand their viewpoints and sentiments when utilizing these digital platforms for interaction. Utilizing social network data to diagnose depression has gained extensive acceptance, there is still a number of unidentified characteristics. Due to its potential to shed light on the forecasting model, model complexity is crucial for facilitating communication. For example, the majority of algorithms for machine learning produce results in the automatic depression forecasting test that are challenging for people to understand. In this research the mental health condition is analyzed using deep learning approach by considering the data from Instagram data. In this investigation, researchers created the Hybrid deep learning approach, which divided the sentiment ratings into different categories: Neutral, Positive, Negative. Researchers also contrasted the performance of the recommended approach with other machine learning algorithm on a number of criteria, including accuracy, sensitivity, F1 score, and precision

Employment outcomes in people with bipolar disorder : a systematic review

Objective: Employment outcome in bipolar disorder is an under investigated, but important area. The aim of this study was to identify the long-term employment outcomes of people with bipolar disorder. Method: A systematic review using the Medline, PsychInfo and Web of Science databases. Results: Of 1962 abstracts retrieved, 151 full text papers were read. Data were extracted from 25 papers representing a sample of 4892 people with bipolar disorder and a mean length of follow-up of 4.9 years. Seventeen studies had follow-up periods of up to 4 years and eight follow-up of 5–15 years. Most studies with samples of people with established bipolar disorder suggest approximately 40–60% of people are in employment. Studies using work functioning measures mirrored this result. Bipolar disorder appears to lead to workplace underperformance and 40–50% of people may suffer a slide in their occupational status over time. Employment levels in early bipolar disorder were higher than in more established illness. Conclusion: Bipolar disorder damages employment outcome in the longer term, but up to 60% of people may be in employment. Whilst further studies are necessary, the current evidence provides support for extending the early intervention paradigm to bipolar disorder

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

A Review on Machine Learning Techniques for Neurological Disorders Estimation by Analyzing EEG Waves

With the fast improvement of neuroimaging data acquisition strategies, there has been a significant growth in learning neurological disorders among data mining and machine learning communities. Neurological disorders are the ones that impact the central nervous system (including the human brain) and also include over 600 disorders ranging from brain aneurysm to epilepsy. Every year, based on World Health Organization (WHO), neurological disorders affect much more than one billion people worldwide and count for up to seven million deaths. Hence, useful investigation of neurological disorders is actually of great value. The vast majority of datasets useful for diagnosis of neurological disorders like electroencephalogram (EEG) are actually complicated and poses challenges that are many for data mining and machine learning algorithms due to their increased dimensionality, non stationarity, and non linearity. Hence, an better feature representation is actually key to an effective suite of data mining and machine learning algorithms in the examination of neurological disorders. With this exploration, we use a well defined EEG dataset to train as well as test out models. A preprocessing stage is actually used to extend, arrange and manipulate the framework of free data sets to the needs of ours for better training and tests results. Several techniques are used by us to enhance system accuracy. This particular paper concentrates on dealing with above pointed out difficulties and appropriately analyzes different EEG signals that would in turn help us to boost the procedure of feature extraction and enhance the accuracy in classification. Along with acknowledging above issues, this particular paper proposes a framework that would be useful in determining man stress level and also as a result, differentiate a stressed or normal person/subject

Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method

BACKGROUND: In recent years, both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging (fMRI) have been widely used for the study of schizophrenia (SCZ). In addition, a few studies have been reported integrating both SNPs data and fMRI data for comprehensive analysis. METHODS: In this study, a novel sparse representation based variable selection (SRVS) method has been proposed and tested on a simulation data set to demonstrate its multi-resolution properties. Then the SRVS method was applied to an integrative analysis of two different SCZ data sets, a Single-nucleotide polymorphism (SNP) data set and a functional resonance imaging (fMRI) data set, including 92 cases and 116 controls. Biomarkers for the disease were identified and validated with a multivariate classification approach followed by a leave one out (LOO) cross-validation. Then we compared the results with that of a previously reported sparse representation based feature selection method. RESULTS: Results showed that biomarkers from our proposed SRVS method gave significantly higher classification accuracy in discriminating SCZ patients from healthy controls than that of the previous reported sparse representation method. Furthermore, using biomarkers from both data sets led to better classification accuracy than using single type of biomarkers, which suggests the advantage of integrative analysis of different types of data. CONCLUSIONS: The proposed SRVS algorithm is effective in identifying significant biomarkers for complicated disease as SCZ. Integrating different types of data (e.g. SNP and fMRI data) may identify complementary biomarkers benefitting the diagnosis accuracy of the disease