In recent days healthcare domains need a efficient storage and retrieval systems to provide a effective medical services to the health seekers. But there is a vocabulary gap in understanding the medical terminologies due to ambiguity. So, the existing systems need a intelligent medical storage using some natural language processing. Users post their queries in free text so it will result in complexity for analyzing and providing instant answers. The main aim of this paper is to develop an intelligent medical data warehousing and mining system. This system can be performed by assigning corpus-aware terminologies to the medical records. This system provides efficient answers to the health seekers by extracting the information related to their queries. © 2017 IEEE.

Selvi M

Department of Database Systems

School of Computer Science and Engineering

Vellore Campus

Ganapathy S

Computer Science

Chennai Campus

Kannan Arputharaj

Saranya M.S

Muthurajkumar S

Ramesh L.S

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

2016 Eighth International Conference on Advanced Computing (ICoAC)

Nowadays, medical applications need a lot of storage for storing and providing access to the medical information seekers. Moreover in medical applications, information grows tremendously and hence they must be stored using a suitable storage structure so that it is possible to retrieve them faster from the text corpus in which the medical information is stored. The existing methods for storage and retrieval do not focus on classified organization. However, classified data storage will facilitate fast retrieval. Therefore, a new Latent Dirichlet Allocation (LDA) based topic modeling approach is proposed in this paper which uses temporal rules for effective manipulation of stored data. Therefore, a temporal rule based classification algorithm is proposed in this work by combining Naïve Bayes Classifier with LDA and temporal rules to store the data more efficiently and it helps to retrieve the documents faster. From the experiments conducted in this work by storing and retrieving medical data in a corpus, it is proved that the proposed model is more efficient with respect to classification accuracy leading to organized storage and fast retrieval. © Springer Nature Singapore Pte Ltd. 2019.

Nanoelectronics, Circuits and Communication Systems Lecture Notes in Electrical Engineering

Classification of Medical Dataset Along with Topic Modeling Using LDA

IEEE Transactions on Biomedical Engineering

METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine

Computer Methods and Programs in Biomedicine

A Swarm Optimization approach for clinical knowledge mining

IEEE Transactions on Emerging Topics in Computing

Discover the Expert: Context-Adaptive Expert Selection for Medical Diagnosis

IEEE Transactions on Knowledge and Data Engineering

Bridging the Vocabulary Gap between Health Seekers and Healthcare Knowledge

Computational and Mathematical Methods in Medicine

Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

Intelligent medical data storage system using machine learning approach

Journal	Data powered by Typeset2016 Eighth International Conference on Advanced Computing (ICoAC)
Publisher	Data powered by TypesetIEEE
Open Access	No