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Unsupervised dynamic topic model for extracting adverse drug reaction from health forums

Eslami Behnaz, Motlagh Mehdi Habibzadeh, Rezaei Zahra, Eslami Mohammad, Amini Mohammad Amin

Applied Computer Science

2020

Vol. 16, no 1

41--59

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.

Analysis of deterministic and geostatistical interpolation techniques for mapping meteorological variables at large watershed scales

Amini Mohammad Amin, Torkan Ghazale, Eslamian Saeid, Zareian Mohammad Javad, Adamowski Jan Franklin

Acta Geophysica

2019

Vol. 67, no. 1

191--203

The widely scattered pattern of meteorological stations in large watersheds and remote locations, along with a need to estimate meteorological data for point sites or areas where little or no data have been recorded, has encouraged the development and implementation of spatial interpolation techniques. The various interpolation techniques featured in GIS software allow for the extraction of this new information from spatially distinct point data. Since no one interpolation method can be accurate in all regions, each method must be evaluated prior to each geographically distinct application. Many methods have been used for interpolating minimum temperature ( Tmin ), maximum temperature ( Tmax ) and precipitation data; however, only a few methods have been used in the Zayandeh-Rud River basin, Iran, and no comparison of methods has ever been carried out in the area. The accuracies of six spatial interpolation methods [Inverse Distance Weighting, Natural Neighbor (NN), Regularized Spline, Tension Spline, Ordinary Kriging, Universal Kriging] were compared in this study simultaneously, and the best method for mapping monthly precipitation and temperature extremes was determined in a large semi-arid watershed with high temperature and rainfall variation. A cross-validation technique and long-term (1970–2014) average monthly Tmin , Tmax and precipitation data from meteorological stations within the basin were used to identify the best interpolation method for each variable dataset. For Tmin , Kriging (Gaussian) proved to be the most accurate interpolation method (MAE = 1.827 °C), whereas, for Tmax and precipitation the NN method performed best (MAE = 1.178 °C and 0.5241 mm, respectively). Accordingly, these variable-optimized interpolation methods were used to define spatial patterns of newly generated climatic maps.