vinaitheerthan


Research Papers

Overview of Frequentist and Bayesian approach to Survival Analysis

Vinaitheerthan Renganathan

Applied Medical Informatics, 38(1), 25-38

Abstract

Survival analysis is one of the main areas of focus in medical research in recent years. Survival analysis involves the concept of 'Time to event'. The event may be mortality, onset of disease, response to treatment etc. Purpose of this paper is to provide overview of frequentist and Bayesian Approaches to Survival Analysis. The paper starts with the overview of the basic concepts of survival analysis and then discusses the frequentist and Bayesian approaches to survival analysis in the biomedical domain with help of hypothetical survival dataset. The survival analysis of the hypothetical data sets showed that for the specific dataset and specific hypothesis, Bayesian approach provided direct probability that the null hypothesis is true or not and the probability that the unknown parameter (mean survival time) lies in a given credible interval wherein the frequentist approach provided p-values and confidence interval for interpreting whether the null hypothesis is true or not and the percentage of intervals which will contain the parameter when the experiment is repeated under same condition. The use of Bayesian survival analysis in biomedical domain has increased due to the availability of advanced commercial and free software, its ability to handle design and analysis issues in survival model and the ease of interpretation of the research findings.

Keyword :Biostatistics,surival,overview,frequentist,bayeisan,analysis,applied,medical,informatics,journal


Citation: Renganathan, V. (2016). Overview of Frequentist and Bayesian Approach to Survival Analysis. Applied Medical Informatics, 38(1), 25.

Text Mining in Biomedical Domain with Emphasis on Document Clustering

Vinaitheerthan Renganathan

Healthcare Informatics Research 2017 Jul; 23(03) 141-146

Abstract

Objectives: With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enablethe extraction of unknown knowledge from unstructured documents.
Methods: This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain.
Results: Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail.
Conclusions: Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.


Keyword : Text Mining, Cluster Analysis, Classification, Natural Language Processing, Software


Citation: Renganathan, V. (2017). Text Mining in Biomedical Domain with Emphasis on Document Clustering. Healthcare Informatics Research, 23(3), 141-146.