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Wyświetlanie 1-2 z 2
Tytuł:
The system developing of forming research schools basis of publication elements analysis
Autorzy:
Shakhovska, N.
Noha, R
Powiązania:
https://bibliotekanauki.pl/articles/117910.pdf
Data publikacji:
2014
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
research school
clustering
text mining
Opis:
In this paper the method of research publications elements analysis that is determining common qualities of research publications and their clustering as an instrument of selecting and sorting out the information about research schools has been introduced. In module structuring documents transmitted there are tape that indicates the address of the file. Depending on where the file is, it can be a path to a file on the local disk or URL on the Internet.
Źródło:
Applied Computer Science; 2014, 10, 2; 57-66
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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
    Wyświetlanie 1-2 z 2

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