Current medical research and opinion
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Review
An insight into diagnosis of depression using machine learning techniques: a systematic review.
In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. ⋯ The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Review
An insight into diagnosis of depression using machine learning techniques: a systematic review.
In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. ⋯ The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Herbal medicine, a form of complementary and alternative medicine (CAM), is used throughout the world, in both developing and developed countries. The ingredients in herbal medicines are not standardized by any regulatory agency. Variability exists in the ingredients as well as in their concentrations. ⋯ Therefore, harm can occur to the kidney, liver, and blood components after ingestion. We encourage scientific studies to identify the active ingredients in herbs and to standardize their concentrations in all herbal preparations. Rigorous studies need to be performed in order to understand the effect of herbal ingredients on different organ systems as well as these substances' interaction with other medications.
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Type 2 diabetes mellitus (T2DM) represents a leading cause of morbidity and premature mortality, low-grade inflammation being acknowledged as a key contributor to its development and progression. A tailored therapeutic approach, based on sensitive and specific biomarkers, could allow a more accurate analysis of disease susceptibility/prognostic and of the response to treatment. ⋯ Several of the assessed parameters may possess prognostic value for diabetics, especially when comparing subgroups with a different smoking history and could prove useful in clinical practice for assessing disease progress and therapeutic efficacy.
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Efforts toward eradicating the Hepatitis C virus (HCV) have advanced rapidly, due to the development of direct-acting antivirals (DAAs), especially with the appearance of pan-genotypic combinations. Real-world studies, in particular, have verified the efficacy and safety of DAA combinations documented in registration trials. This review documents the results of using DAA combinations in real-life settings in everyday clinical practice in Egypt, the country with the highest prevalence of HCV. ⋯ Most adverse reactions reported in real-world settings were mild and resulted in treatment discontinuation in only a minority of cases. Data from real-life studies covered most aspects of HCV management that were lacking after initial approval studies. More research is needed to tailor treatment and produce generic HCV combinations to overcome the residual limitations of the currently available DAAs.