Department of Computer Science (PG)

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Author: search.filters.author.D, Krithika RenukaEnd date: 2023

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    DRUG SUGGESTION CONCERNED AUTOMATED DRUG KNOWLEDGE ONTOLOGY CONSTRUCTION FRAMEWORK
    (Journal of International Pharmaceutical Research, 2019-07) D, Krithika Renuka
    Drug effect identification suggesting proper drug is the most critical task in the medical care environment which needs to be done with more concern. In our previous research work, drug effect identification is performed from the real world tweets gathered from the twitter website using Relevancy and Similarity Aware Drug Comment Classification Framework (RSDCCF). However this method doesn’t focus on the faster response and proper drug suggestion based on side effects. This is focused in this research work by introducing the method namely Drug Suggestion Concerned Automated Drug Knowledge Ontology Construction Framework (DSCADKOCG). This research work can ensure the proper drug suggestion to the patients based on side effects. In this research work, automated ontology construction is performed based on drug tweets gathered from the social websites which can lead to construction of Drug Knowledge Source Ontology Construction. After ontology construction, drug learning is performed using the constructed ontology and the drug database using TSVM classifier. Based on this learned knowledge automated and fast drug suggestion is performed using Semantic query based drug suggestion approach. The overall evaluation of the research method is performed in the matlab simulation environment from which it can be proved that the proposed research technique can lead to provide the optimal outcome than the existing research techniques
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    OPINION MINING ON ADVERSE DRUG REACTIONS IN SOCIAL MEDIA USING MACHINE LEARNING TECHNIQUES
    (Journal of Advanced Research in Dynamical and Control systems, 2017-09) D, Krithika Renuka
    Social media attracts millions of users to share their interests and opinions with other users in the flexible way. Medical organizations started to share their information about newly developed drugs through social media. It provides flexible platform for millions users to share their opinions about the newly developed drugs. With that information various peoples who suffered from disease might get an opinion about them. Analyzing various kind of drugs information posted by the millions of users is a most difficult task which is researched by various authors. In the existing work, two-step analysis framework is implemented which focuses on positive and negative sentiment extraction. It is done for the purpose of ascertaining user opinion of cancer treatment. It is done by using a self-organizing map to analyze word frequency data derived from user’s forum posts. However in the existing system is a static model where only the older posts that are posted by the users online previously are considered which might lead to less accurate detection of consumer opinions. The existing lacks in accurate prediction of user opinions. This is resolved in the proposed research method by introducing Dynamic Drug Data Analysis using Hybrid Fuzzy C Means with Transductive Support Vector Machine (DDDA-HFCM-TSVM) by focusing on user opinions from the user reviews to refine the product based on user opinions. This is implemented in the social media framework where more number of consumers post their reviews based on the drug product. In the proposed research twitter social network is considered for retrieving the user posted opinions. The main goal of the proposed research work is to support the dynamic retrieval of data from the twitter social web site from which drugs related information would be identified. The overall evaluation of the proposed research work is done in the matlab simulation environment from which it is proved that the proposed research methods leads to provide better result than the existing research methods.
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    A SURVEY AND ANALYSIS OF VARIOUS HEALTH- RELATED KNOWLEDGE MINING TECHNIQUES IN SOCIAL MEDIA
    (International Journal of Computer Applications, 2017-01) D, Krithika Renuka
    Smart extraction of knowledge from social media has received the recent interest of the Biomedical and Health Informatics community for the simultaneous improvement of healthcare outcomes and lessen the expenses making use of consumer generated reviews. Social media provides chances for patients and doctors to share their views and experiences without any obtrusion through online communities that might generate information, which is much beyond what is known by the domain experts. Nonetheless, for conventional public health surveillance systems, it is difficult to detect and then monitor the concerns related to health and the changes seen in attitudes of the public towards health-related problems. To solve this problem, several studies have shown the usage of information in social media for the discovery of biomedical and health related information. Several disease-specific knowledge exchanges are now available on Face book and other portals of online social networking. These kind of new sources of information, support, and engagement have gone to become significant for patients who are suffering with the disease, and still the quality and the content of the knowledge contributed in these digital areas are not properly comprehended. The existing research methodologies are discussed with their merits and demerits, so that the further research works can be concentrated more. The experimental tests conducted were on all the research works in mat lab simulation environment and it is compared against each other to find the better approach under various performance measures such as Accuracy, Precision and Recall.
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    DYNAMIC AND RELIABLE INTELLIGENT DATA MINING TECHNIQUE ON SOCIAL MEDIA DRUG RELATED POSTS
    (IEEE, 2018) D, Krithika Renuka
    Social media attracts millions of users to share their interests and opinions with other users in the flexible way. Medical organizations started to share their information about newly developed drugs through social media. It provides flexible platform for millions users to share their opinions about the newly developed drugs. With that information various peoples who suffered from disease might get an opinion about them. Analyzing various kind of drugs information posted by the millions of users is a most difficult task which is researched by various authors. In the existing work, two-step analysis framework is implemented which focuses on positive and negative sentiment extraction. It is done for the purpose of ascertaining user opinion of cancer treatment. It is done by using a self-organizing map to analyze word frequency data derived from users’ forum posts. However in the existing system is a static model where only the older posts that are posted by the users online previously are considered which might lead to less accurate detection of consumer opinions. It utilizes only static dictionary about the drugs where the newly introduced drugs cannot be identified. This problem is resolved in the proposed research method namely Dynamic Drug Data Analysis using Hybrid Transductive Support Vector Machine with Fuzzy C Means algorithm (DDDA-HTSVM-FCM) by focusing on user opinions from the user reviews to refine the product based on user opinions. This is implemented in the social media framework where more number of consumers post their reviews based on the drug product. In the proposed research twitter social network is considered for retrieving the user posted opinions. The main goal of the proposed research work is to support the dynamic retrieval of data from the twitter social web site from which drugs related information would be identified. The overall evaluation of the proposed research work is done in the matlab simulation environment from which it is proved that the proposed research methods leads to provide better result than the existing research methods.
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    A SURVEY AND ANALYSIS ON DATA MINING TECHNIQUES RELATED TO DRUG RELATED POSTS IN SOCIAL MEDIA
    (Dr.N.G.P Arts & Science College, 2017) D, Krithika Renuka
    Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. Social media offers opportunities for patients and doctors to share their opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. However, for traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues. To solve this problem, Multiple studies illustrated the use of information in social media to discover biomedical and health-related knowledge. Several disease-specific information exchanges now exist on Face book and other online social networking sites. These new sources of knowledge, support, and engagement have become important for patients living with disease, yet the quality and content of the information provided in these digital areas are poorly understood. The existing research methodologies are discussed with their merits and demerits, so that the further research works can be concentrated more. The experimental tests conducted were on all the research works in matlab simulation environment and it is compared against each other to find the better approach under various performance measures such as Accuracy, Precision and Recall.
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    DATA MINING TECHNIQUES ON SOCIAL MEDIA DRUG RELATED POSTS – A COMPARATIVE STUDY & ANALYSIS
    (PSGR Krishnammal College for women, 2017) D, Krithika Renuka
    Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. Social media offers opportunities for patients and doctors to share their opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. However, for traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues. To solve this problem, Multiple studies illustrated the use of information in social media to discover biomedical and health-related knowledge. Several disease-specific information exchanges now exist on Face book and other online social networking sites. These new sources of knowledge, support, and engagement have become important for patients living with disease, yet the quality and content of the information provided in these digital areas are poorly understood. The existing research methodologies are discussed with their merits and demerits, so that the further research works can be concentrated more. The experimental tests conducted were on all the research works in matlab simulation environment and it is compared against each other to find the better approach under various performance measures such as Accuracy, Precision and Recall.
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    A SURVEY OF VARIOUS HEALTH- RELATED TECHNIQUES.
    (Dr. N.G.P Arts and Science College., 2016) D, Krithika Renuka
    Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer- generated opinion. Social media offers opportunities for patients and doctors to share their opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. However, for traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues. To solve this problem, Multiple studies illustrated the use of information in social media to discover biomedical and health- related knowledge. Several disease-specific information exchanges now exist on Face book and other online social networking sites. These new sources of knowledge, support, and engagement have become important for patients living with disease, yet the quality and content of the information provided in these digital arenas are poorly understood. The existing research methodologies are discussed with their merits and demerits, so that the further research works can be concentrated more. The experimental tests conducted were on all the research works in matlab simulation environment and it is compared against each other to find the better approach under various performance measures.