Drug utilization study in the otorhinolaryngology department in a tertiary care hospital

Background: Drug utilization is defined by the World Health Organization (WHO) as the marketing, distribution, prescription, and use of drugs in society, with special emphasis on the resulting medical, social, and economic consequences. The aim of this study was to evaluate the pattern of prescription and then drug utilization in outpatient (OPD) of the Department of Otolaryngology in a tertiary care teaching hospital.Methods: This was a retrospective study conducted at the A.C.S. medical college and hospital, Chennai for a period of 7 months. All the patients who attended the Ear Nose and Throat (ENT) OPD were included. The total number who attended the OPD was 10,249 which include 6,956 new cases and 3313 old cases.Results: The antibacterials commonly used were β Lactams (56%), macrolides (14%), fluoroquinolones (12%), aminoglycosides (8%). Among the penicillin group, the commonest drug prescribed was a combination of amoxicillin and clavulanic acid (27%), in cephalosporins was cefixime + clavulanic acid (19%). Aminoglycosides include gentamycin in refractory cases. Fluoroquinolones include ciprofloxacin and levofloxacin. Others Drugs like antihistamines and mucolytics were prescribed in 27%, anti- ulcer drugs in 36% cases, analgesics in 33% cases and herbal medicines in 4%. The average number of drugs used in each prescription was 3.20. All the drugs were prescribed with brand names. The average cost per prescription per day for OPD patients is 37 Rupees.Conclusions: β Lactams were commonly used antibacterials in the otorhinolaryngology department

PocketNet: A Smaller Neural Network for Medical Image Analysis

Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling the growth of the number of channels in convolutional neural networks. We demonstrate that, for a range of segmentation and classification tasks, PocketNet architectures produce results comparable to that of conventional neural networks while reducing the number of parameters by multiple orders of magnitude, using up to 90% less GPU memory, and speeding up training times by up to 40%, thereby allowing such models to be trained and deployed in resource-constrained settings

Simulation Based Predictive Analysis of Indian Airport Transportation System Using Computational Intelligence Techniques

Normally, flight delays and cancellations have significant impact on airlines operations and passenger’s satisfaction. Flight delays reduce the performance of airline operations and make significant effect on airports on time performance. Previously statistical models have been used for flight delays analysis. This study was applied in Indian aviation industry and it has given statistical analysis of domestic airlines. In this research paper, we have applied Machine Learning models with the help of computational intelligence techniques for predicting airport transport management system. We have also applied computational intelligence techniques such as Particle Swarm Optimization (PSO) and Ant Colonization Optimization (ACO) to optimize the prediction model for delay period time and calculating the most optimal dependability. We have made comprehensive analysis of Data Efficiency Model for different airlines with various approaches as well as comparative analysis of accuracy for predicting airport model by using various machine learning models. In this study we have presented invaluable insights for the analysis of flight delay models

R.Rajarajeswari, "A Design and Solving LPP Method for Binary Linear Programming Problem Using DNA Approach ",International

Abstract Molecular computing is a discipline that aims at harnessing individual molecules for computational purposes. This paper presents the applie

Web Services: A BI Perspective

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