Knowledge about evidence based pharmaceutical care in medical and non-medical population of Lahore, Pakistan Evidence Based Pharmaceutical Care

Pharmaceutical care related services provided by pharmacists in the community are mainly taking patients' medication history, informing patients about use of medications, informing patients about medication storage, and provide information about drug and/or food interaction. Objective: Toevaluate the knowledge about evidence based pharmaceutical care in medical and non-medicalpopulation of Lahore, Pakistan. Methods: A sample of 100 participants was drawn by using non-convenient sampling in this cross-sectional survey. Survey was conducted within the duration of 6 months from 2nd June, 2020 to 15th December, 2020. Data was collected from participants of different universities, societies and hospitals, having age between 25-40 years, both genders without discrimination of profession. Data was analyzed using SPSS version 21.0. Qualitative data was calculated using frequencies and percentages. Results: In this study 63% medical and 37% non-medical participants respond to questionnaire. About 34% of the population had knowledge about pharmaceutical care. Almost 23%had lack of knowledge about pharmaceutical care while 43%participants did not respond. Conclusions: The knowledge about pharmaceutical care in general community is very vital and pharmacist shouldprovide knowledge and pharmaceutical care services to the patients.

A Novel Technique for Finding Rough Set Based Dynamic Reducts

Volumetric increase in data along with the curse of dimensionality has diverted the recent trends of computer science. Processing such a massive amount of data is a computationally expensive job. Feature selection is the process of selecting subset of data from the entire dataset that contains most of the information. The selected subset is called Reduct. Feature selection has materialized the idea of jumbling with attributes. Subset of attributes is favored which bounces the same information as the wide-ranging set of variables. Various dynamic reduct finding algorithms have been proposed. Dynamic reducts is an extension to the idea of reduct extraction based on rough set. Sub-tables are randomly drawn from the original decision table and reducts are extracted from these sub-tables. These reducts are considered to be the stable reducts for complete dataset. However, all the existing dynamic reduct finding algorithms are computationally too expensive to be used for datasets beyond smaller size. In this research, a novel dynamic reduct finding technique based on rough set theory is proposed, where dynamic reducts and relative dependency are the two key notions. Reducts are selected, optimized and further generalized through strenuous Parallel Feature Sampling (PFS) algorithm. In-depth analysis is performed using various benchmark datasets to justify the proposed approach. Results have shown that the proposed algorithm outperforms the existing state of the art approaches in terms of both efficiency and effectiveness.