Volume-07 , Special Issue-07 , Mar - 2019 Go Back

Machine based identification and coding of Adverse Events mentioned in the natural language text received in Pharmacovigilance through the different sources be it emails, faxes, literature, complaint reports, forms, study literatures or phone call transcripts poses a compound problem as it deals with two sub problems, firstly Named entity recognition to detect events (symptoms, illnesses, adverse events) and secondly mapping events to a standard medical coding scheme such as MedDRA. Additionally, industrial applications are required to build their systems in compliance with regulatory requirements, thereby limiting their ability. In this paper we focus on mapping laymen terms or adverse event verbatim/ description to the standard medical terms. We use vector representation of standard medical term dictionary Medical Dictionary for Regulatory Activities (MedDRA) and that of the event verbatim along with their similarity score to establish these mappings.

Medical Coding, Adverse Event Identification, Cosine Similarity, Word2Vec, Semi supervised machine learning

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Rajdeep Sarkar, Devyani Sampale, Harshita Rai, "Extraction and Medical Coding of Adverse Events using Word Embedding", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.62-66, 2019.

IT industry is rapidly growing Industry in India. Workforce diversity is the emerging issue in today’s corporate world as it’s not only a challenge for the organizations but also a chance, which if not properly managed can affect the performance of employees and therefore the organization. People are different in not only in gender, age, education, ethnic diversity but also in their perspectives and prejudices. The impact of diversity in the workplace is surprisingly greater than one would think. Studies show that putting together a team composed of people with the same ethnic background and personality may cause conflicts and decrease productivity. As opposed to a team that is composed of people with different ethnic backgrounds and personalities which allows for a higher rate of productivity and a lower rate of conflict. The aim of the present study is to identified the impact of various diversity on employee performance of IT companies of Udaipur. There are numerous diversity factors that can affect employee performance, but this study included only the most critical variables among all of them, such as diversity in age, gender, ethnicity, educational background, religious, workforce, and employee attitude towards job, which were independent variables, and employee performance in an organisation, which was a dependent variable. The data for this study was gathered by sending questionnaires to 300 employees of multiple IT companies in the Udaipur District of Rajasthan (India). The empirical link between age, gender, ethnicity, educational background, religion, workforce, and job attitude was calculated using the Software Package for Social Science (SPSS). Statistical methods such as the percent and Chi-Square Test, the `t` test, and correlation were utilised to examine the SPSS data. The study`s findings revealed that all work force diversity characteristics are favourably connected with employee performances.

Employee Performance, Workforce Diversity, Age Diversity, Education Diversity, Ethnic Diversity IT Industry

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Shakti Singh Shekhawat, S.S. Sarangdevot, "Impact of Workforce Diversity on Employee Performance With Reference to IT Companies of Udaipur", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.67-75, 2019.