Disease-Disease Relationships for Rheumatic Diseases: Web-Based Biomedical Textmining an Knowledge Discovery to Assist Medical Decision Making

Abstract

The MEDLINE database (Medical Literature Analysis and Retrieval System Online) contains an enormously increasing volume of biomedical articles. There is urgent need for techniques which enable the discovery, the extraction, the integration and the use of hidden knowledge in those articles. Text mining aims at developing technologies to help cope with the interpretation of these large volumes of publications. Co-occurrence analysis is a technique applied in text mining and the methodologies and statistical models are used to evaluate the significance of the relationship between entities such as disease names, drug names, and keywords in titles, abstracts or even entire publications. In this paper we present a method and an evaluation on knowledge discovery of disease-disease relationships for rheumatic diseases. This has huge medical relevance, since rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. In this study, we interviewed medical experts and searched the ACR (American College of Rheumatology) web site in order to select the most observed rheumatic diseases to explore disease-disease relationships. We used a web based text-mining tool to find disease names and their co-occurrence frequencies in MEDLINE articles for each disease. After finding disease names and frequencies, we normalized the names by interviewing medical experts and by utilizing biomedical resources. Frequencies are normally a good indicator of the relevance of a concept but they tend to overestimate the importance of common concepts. We also used Pointwise Mutual Information (PMI) measure to discover the strength of a relationship. PMI provides an indication of how more often the query and concept co-occur than expected by change. After finding PMI values for each disease, we ranked these values and frequencies together. The results reveal hidden knowledge in articles regarding rheumatic diseases indexed by MEDLINE, thereby exposing relationships that can provide important additional information for medical experts and researchers for medical decision-making.

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

“…Nonetheless, the examination of sets with names was neglected, which demonstrates that there are a few equivalent words in the content that were neglected to be caught in the Bio Thesaurus [59], [60]. [54], [56], [77], [80] all extracted keywords from biomedical records. However, this study only focused on the biomedical literature corpus and could be adapted to literature retrieval in other domains [89], [90].…”

“…Holzinger et al [77] proposed a Sequence Memorizer Based Model (SMBM) that had its roots in what was known as the generative model to oversee its functioning. This method resorted to the utilization of the generative strategy in order to avoid the option of selecting work that was timeconsuming.…”

“…Holzinger et al [77] offered a way to assess knowledge discernment of disease-disease relationships for rheumatic diseases. Also, they resorted to utilizing a Point wise Mutual Information (PMI) calculation to identify a relationship's strength.…”

Mining the web and medline medical records to discover new facts on diabetes

“…This is to develop a prospective system for automating the extraction of relationships between cancer diseases and potential factors from clinical records [10], which differs from the majority of the other works where co-occurrence analysis has been the preferred choice of approach, for example in the research conducted to evaluate knowledge discovery of diseasedisease relationships for rheumatic diseases [11].…”

Prediction and Detection of Rheumatoid Arthritis SNPs Using Neural Networks

“…The MEDLINE database (Medical Literature Analysis and Retrieval System Online) contains an enormously increasing volume of biomedical articles [2]. There is urgent need for techniques which enable the discovery, the extraction, the integration and the use of hidden knowledge in those articles.…”