DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences

This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant nature of the backgrounds in surveillance videos, visual information of the background can be compressed into a low-dimensional subspace in the encoder part of the variational autoencoder, while the highly variant information of its moving foreground gets filtered throughout its encoding-decoding process. Our deep probabilistic background model (DeepPBM) estimation approach is enabled by the power of deep neural networks in learning compressed representations of video frames and reconstructing them back to the original domain. We evaluated the performance of our DeepPBM in background subtraction on 9 surveillance videos from the background model challenge (BMC2012) dataset, and compared that with a standard subspace learning technique, robust principle component analysis (RPCA), which similarly estimates a deterministic low dimensional representation of the background in videos and is widely used for this application. Our method outperforms RPCA on BMC2012 dataset with 23% in average in F-measure score, emphasizing that background subtraction using the trained model can be done in more than 10 times faster

A Method for Plotting Disease Drug Analysis and Its Complications by Combining Sources of Scientific Documents Using Deep Learning Method with Drug Repurposing: Case Study Metformin

Drugs for medical purposes aim at saving one’s life and improving their life quality. Side effects or adverse drug reactions (ADRs) on patients are studied as an important issue in pharmacology. In order to prevent the adverse drug effects, clinical trials are conducted on the drug production process, but the process of these trials is very costly and time consuming. So, various text mining methods are used to identify ADRs on scientific documents and articles. Using existing articles in the reference websites such as PubMed to predict an effective drug in the disease is a vital way to declare the drug effective. However, the effective integration of biomedical literature and biological drug network information is one of the major challenges in diagnosing a new drug. In this study, we use medical text documents to train the BioBERT model so that we can use it to discover potential drugs for treating diseases. Then, we are able to create a graphical network of drugs and their side effects with this method as well as it provides us with an opportunity to identify effective drugs that have been used in many diseases so far while having the ability to be used effectively on other diseases

UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS

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 dis¬tinct detection of adverse effects of drugs, and deep learning would facilitate it

Frequency and characteristics of Brucellosis in Golestan Province, Iran

BACKGROUND: Brucellosis is a common widespread zoonotic disease between humans and livestock with significant economic and health problems caused by various species of Brucella. The disease is a significant public health issue throughout the world and one of the most socioeconomic problems in many developing countries. This study aimed to report the information available at the Provincial Health Center about the frequency and characteristics of patients with Brucellosis in Golestan Province, Iran.METHODS: This study was analytic-descriptive cross-sectional. The study population included all patients with Brucellosis diagnosed from 2011 to 2015 in the health center of Golestan Province, based on the serological method. The data gathering tool was a questionnaire that included demographic information, clinical presentation and examinations, history of exposure, laboratory findings, and treatment protocols. Descriptive statistics were reported as frequency and mean ± standard deviation (SD) and analyzed by SPSS software.RESULTS: In this study, a total of 1788 cases of Brucellosis were reported. The number of male cases was 1163 (65.04%) and female cases were 625 (34.95%). People who had a history of contact with infected animals were younger than the others. Musculoskeletal pain (79.69%) and fever (76.45%) were the most commonly reported clinical symptoms.CONCLUSION: Overall, the results indicate that Brucellosis is still a health problem in the province. The high incidence of Brucellosis in villages, the lack of full coverage of animal vaccination, and the link between the disease and livestock businesses are significant