Mining Disease Risk Patterns from Nationwide Clinical Databases for the Assessment of Early Rheumatoid Arthritis Risk

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune rheumatic disease that can cause painful swelling in the joint lining, morning stiffness, and joint deformation/destruction. These symptoms decrease both quality of life and life expectancy. However, if RA can be diagnosed in the early stages, it can be controlled with pharmacotherapy. Although many studies have examined the possibility of early assessment and diagnosis, few have considered the relationship between significant risk factors and the early assessment of RA. In this paper, we present a novel framework for early RA assessment that utilizes data preprocessing, risk pattern mining, validation, and analysis. Under our proposed framework, two risk patterns can be discovered. Type I refers to well-known risk patterns that have been identified by existing studies, whereas Type II denotes unknown relationship risk patterns that have rarely or never been reported in the literature. These Type II patterns are very valuable in supporting novel hypotheses in clinical trials of RA, and constitute the main contribution of this work. To ensure the robustness of our experimental evaluation, we use a nationwide clinical database containing information on 1,314 RA-diagnosed patients over a 12-year follow-up period (1997–2008) and 965,279 non-RA patients. Our proposed framework is employed on this large-scale population-based dataset, and is shown to effectively discover rich RA risk patterns. These patterns may assist physicians in patient assessment, and enhance opportunities for early detection of RA. The proposed framework is broadly applicable to the mining of risk patterns for major disease assessments. This enables the identification of early risk patterns that are significantly associated with a target disease.

Prodromal signs and symptoms of serious infections with tocilizumab treatment for rheumatoid arthritis: Text mining of the Japanese postmarketing adverse event-reporting database

“…This approach may involve text mining, which enables efficient analysis of unstructured data elements from clinical narratives [15,19]. Indeed, text mining of clinical sources, among them electronic health care data and clinical notes and narratives, has been used in recent large-scale analyses of drug safety and diseases [16,17,20], including those in patients with RA [21].…”

“…Text mining electronic medical records has been used successfully to obtain insights from clinical narratives, such as enhancing opportunities for the early detection of RA [16,21]. However, this approach may be time consuming and costly because of practical issues requiring collaboration between several hospitals and integration of their data formats [21,22] to acquire enough patient data for analysis.…”

“…However, this approach may be time consuming and costly because of practical issues requiring collaboration between several hospitals and integration of their data formats [21,22] to acquire enough patient data for analysis. In addition, it is customary to report diagnostic terms (e.g.…”

Diagnosis of Rheumatoid Arthritis Using an Ensemble Learning Approach

“…If RA is diagnosed in its early stages, it can be easily controlled. Usually RA is diagnosed when severe symptoms appear in the patient and disease need more aggressive treatment [3].…”

“…A variety of methods has been developed for the early diagnosis of RA [3,4], such as 2010 ACR/EULAR classification criteria [5] and the van der Helm-van Mil (vHvM) score [6]. Currently, clinical experience is the basis of the diagnosis of RA using certain RA disease classification criteria.…”