Prediction of Similarities Among Rheumatic Diseases

Abstract

We introduce a method for extracting hidden patterns seen in rheumatic diseases by using articles from the widely used biomedical database MEDLINE. Rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. Diagnosing rheumatic diseases can be difficult because some symptoms are common to many of them. We use Facta system as a biomedical text mining tool for finding symptoms and then create a dataset with the frequencies of symptoms for each disease and apply hierarchical clustering analysis to find similarities between diseases. Clustering analysis yields four distinct types or groups of rheumatic diseases. Although our results cannot remove all the uncertainty for the diagnosis of rheumatic diseases, we believe they can contribute to the diagnosis of rheumatic diseases to a certain extent. We hope that some similarities exposed can provide additional information at the stage of decision-making.

Diagnosis of Rheumatoid Arthritis Using an Ensemble Learning Approach

“…18 major influence factors were recognized for accurately predicting RA disease but education, BMI, occupation and birthplace were less important as other factors that is similar to the 2010 ACR/EULAR Classification Criteria. The research results could not be comparable with the other similar mining researches in RA such as [8,9,10] because they have used text mining and their search result was related to RA but were different from this study. In addition, 20 diagnosis, classification rules were extracted from this predictive model, and confirmed by three RA specialists to be conformable with the current clinical medical condition and have reference value in diagnosis and prediction of RA disease.…”

“…In most of the data mining studies that were investigated, more attention has been paid to several medical fields, including RA [8,9,10], cardiovascular diseases [11,12,13,14,15,16], cancer [17,18,19,20], lung [21,22,23], traumatic brain injury [24,25,26] and diabetes [27,28,29].…”

Study on text mining algorithm for ultrasound examination of chronic liver diseases based on spectral clustering

“…Therefore, the Gaussian kernel function algorithm is chosen to calculate the similarity matrix. Gaussian kernel function is calculated as follows: (2) Among them, x, x 'represents the sample points of the data, that is, any two row vectors in the document-term matrix A.…”

“…Using biomedical text mining tools to automatically extract the frequency of rheumatic symptoms [2]. Health care analysis system adopts an approach, which is based on dictionary segmentation and use iterative feedback mechanism to enhance self-learning [3].…”

Mining MEDLINE for the Treatment of Osteoporosis

“…They provide the analysis of co-occurrences between biomedical entities such as disease, drugs, genes, proteins and symptoms [7]. Some text mining systems include:…”