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    Tytuł:
    Unsupervised dynamic topic model for extracting adverse drug reaction from health forums
    Autorzy:
    Eslami, Behnaz
    Motlagh, Mehdi Habibzadeh
    Rezaei, Zahra
    Eslami, Mohammad
    Amini, Mohammad Amin
    Powiązania:
    https://bibliotekanauki.pl/articles/117691.pdf
    Data publikacji:
    2020
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    Deep Learning
    topic modeling
    Text Mining
    ADR
    NMF
    analiza tekstu
    uczenie maszynowe
    modelowanie tematyczne
    Opis:
    The relationship between drug and its side effects has been outlined in two websites: Sider and WebMD. The aim of this study was to find the association between drug and its side effects. We compared the reports of typical users of a web site called: “Ask a patient” website with reported drug side effects in reference sites such as Sider and WebMD. In addition, the typical users’ comments on highly-commented drugs (Neurotic drugs, Anti-Pregnancy drugs and Gastrointestinal drugs) were analyzed, using deep learning method. To this end, typical users’ comments on drugs' side effects, during last decades, were collected from the website “Ask a patient”. Then, the data on drugs were classified based on deep learning model (HAN) and the drugs’ side effect. And the main topics of side effects for each group of drugs were identified and reported, through Sider and WebMD websites. Our model demonstrates its ability to accurately describe and label side effects in a temporal text corpus by a deep learning classifier which is shown to be an effective method to precisely discover the association between drugs and their side effects. Moreover, this model has the capability to immediately locate information in reference sites to recognize the side effect of new drugs, applicable for drug companies. This study suggests that the sensitivity of internet users and the diverse scientific findings are for the benefit of distinct detection of adverse effects of drugs, and deep learning would facilitate it.
    Źródło:
    Applied Computer Science; 2020, 16, 1; 41-59
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Family Changes in Iranian Kurdistan: A Mixed Methods Study of Mangor and Gawerk Tribes
    Autorzy:
    Mohammadpur, Ahmad
    Corbin, Juliet
    Sadeghi, Rasoul
    Rezaei, Mehdi
    Powiązania:
    https://bibliotekanauki.pl/articles/1373650.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    Family Changes
    Modernization
    Mixed Methods Research
    Grounded Theory
    Mangor and Gawerk Tribes
    Opis:
    Over the last few decades, the Iranian Kurdish society, including family and kin¬ship systems, has experienced enormous changes as a result of government im-plemented modernization efforts. This paper reports the results of a quantitative/ qualitative mixed methods study aimed at exploring (a) the nature of change in family and kinship systems and (b) how people understand and interpret these changes. The sample for this study was drawn from the Mangor and Gawerk tri¬bes residing in the Mahabad Township located in the West Azerbaijan Province of Iran. Using standardized questionnaires, 586 people were sampled as part of the quantitative portion of the study. For the qualitative portion, data was collected on 20 people using both in-depth interviews and participant observations. The quantitative data was analyzed by SPSS software and the qualitative data was in¬terpreted using grounded theory procedures. The quantitative findings showed that the urbanization, modern education, and mass media have all contributed to the emergence of a new form of family and kinship life. In addition, while sup¬porting quantitative findings, the qualitative results revealed that participants were aware of and sensitive to sources, processes, and effects of modernization on their family and kinship life.
    Źródło:
    Przegląd Socjologii Jakościowej; 2012, 8, 3; 76-96
    1733-8069
    Pojawia się w:
    Przegląd Socjologii Jakościowej
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Patterns of Relapse Risks and Related Factors among Patients with Schizophrenia in Razi Hospital, Iran: A Latent Class Analysis
    Autorzy:
    Noroozi, Mehdi
    Alibeigi, Neda
    Armoon, Bahram
    Rezaei, Omid
    Sayadnasiri, Mohammad
    Nejati, Somayeh
    Fadaei, Farbod
    Ghahestany, Davood Arab
    Dieji, Bahman
    Ahounbar, Elahe
    Powiązania:
    https://bibliotekanauki.pl/articles/2127857.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Razi Hospital
    Iran
    Relapse Risks
    Patients
    Schizophrenia
    Latent Class Analysis
    Opis:
    Objectives: Relapse is very much associated with the management of disorder during the treatment, but also many other factors could trigger it. The aim of this study was to explore classes and patterns of relapse risk in patients with schizophrenia of Razi Hospital. Methods: Using random sampling techniques, we recruited 300 participants with a diagnosis of schizophrenia in Razi hospital of Tehran (Iran) between January and May 2017 in a cross-sectional survey. We used latent class analysis (LCA) to establish a baseline model of risk profiles and to identify the optimal number of latent classes, and we used ordinal regression to identify factors associated with class membership. Results: Three classes of multiple relapse risk were identified. LCA showed that, overall, 52%, 22% and 26% of participants with schizophrenia were divided into class 1, class 2 and class 3, respectively. Compared to members in the lowest-risk class (reference group), the highest-risk class members had higher odds of being the age of disorder onset under 25 (OR = 1.4; CI: 1.42–2.33). Participants with schizophrenia who were unemployed were more likely to categorize in the highest-risk class than members of the low-risk class (OR = 2.5; CI: 1.44–4.1). Also, female patients were more likely to belong to members of the high-risk class than members of the low-risk class (OR = 2.22; CI: 1.74–7.64). Conclusion: These findings emphasize the importance of having targeted prevention programs for all domains of Age of onset, female and unemployed related. So, current study suggested that interventions should focus on these risk factors. Furthermore, Increasing the Job opportunities for participants with schizophrenia is warranted so as to prevent of schizophrenia disorder.
    Źródło:
    Polish Psychological Bulletin; 2018, 49, 3; 355-359
    0079-2993
    Pojawia się w:
    Polish Psychological Bulletin
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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