eDRAM: Effective early disease risk assessment with matrix factorization on a large-scale medical database: A case study on rheumatoid arthritis

• Published in: PloS one 2018-11, Vol.13 (11), p.e0207579-e0207579
• Main Authors: Chin, Chu-Yu, Hsieh, Sun-Yuan, Tseng, Vincent S
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Alzheimer's disease; Alzheimers disease; Analysis; Antiarthritic agents; Arthritis; Arthritis, Rheumatoid - diagnosis; Arthritis, Rheumatoid - pathology; Bioinformatics; Biology and Life Sciences; Case studies; Classification; Cohort Studies; Computer and Information Sciences; Computer science; Data bases; Data mining; Data processing; Databases, Factual; Diabetes; Diagnosis; Diagnostic systems; Electronic health records; Engineering; Factor analysis; Factorization; Female; Health care; Health informatics; Health insurance; Health risk assessment; Health risks; Hospitals; Humans; Informatics; International conferences; Learning algorithms; Liver cancer; Machine Learning; Male; Medical diagnosis; Medical informatics; Medical records; Medical research; Medicine and Health Sciences; Middle Aged; Mining; Natural language processing; Novels; Online health care information services; Optimization; Patients; Personal health; Physical Sciences; Prognosis; Regression analysis; Research and Analysis Methods; Rheumatoid arthritis; Rheumatoid factor; Risk analysis; Risk Assessment; Risk Factors; State of the art
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0207579

Recently, a number of analytical approaches for probing medical databases have been developed to assist in disease risk assessment and to determine the association of a clinical condition with others, so that better and intelligent healthcare can be provided. The early assessment of disease risk is an emerging topic in medical informatics. If diseases are detected at an early stage, prognosis can be improved and medical resources can be used more efficiently. For example, if rheumatoid arthritis (RA) is detected at an early stage, appropriate medications can be used to prevent bone deterioration. In early disease risk assessment, finding important risk factors from large-scale medical databases and performing individual disease risk assessment have been challenging tasks. A number of recent studies have considered risk factor analysis approaches, such as association rule mining, sequential rule mining, regression, and expert advice. In this study, to improve disease risk assessment, machine learning and matrix factorization techniques were integrated to discover important and implicit risk factors. A novel framework is proposed that can effectively assess early disease risks, and RA is used as a case study. This framework comprises three main stages: data preprocessing, risk factor optimization, and early disease risk assessment. This is the first study integrating matrix factorization and machine learning for disease risk assessment that is applied to a nation-wide and longitudinal medical diagnostic database. In the experimental evaluations, a cohort established from a large-scale medical database was used that included 1007 RA-diagnosed patients and 921,192 control patients examined over a nine-year follow-up period (2000-2008). The evaluation results demonstrate that the proposed approach is more efficient and stable for disease risk assessment than state-of-the-art methods.