000			02231nam a22003737a 4500
008			171205s20172017 xxu||||| |||| 00| 0 eng d
022			_a2045-2322
040			_aOvid MEDLINE(R)
099			_a29138438
245			_aNetwork-based analysis of diagnosis progression patterns using claims data.
251			_aScientific Reports. 7(1):15561, 2017 Nov 14
252			_aSci. rep.. 7(1):15561, 2017 Nov 14
253			_aScientific reports
260			_c2017
260			_fFY2018
266			_d2017-12-05
520			_aIn recent years, several network models have been introduced to elucidate the relationships between diseases. However, important risk factors that contribute to many human diseases, such as age, gender and prior diagnoses, have not been considered in most networks. Here, we construct a diagnosis progression network of human diseases using large-scale claims data and analyze the associations between diagnoses. Our network is a scale-free network, which means that a small number of diagnoses share a large number of links, while most diagnoses show limited associations. Moreover, we provide strong evidence that gender, age and disease class are major factors in determining the structure of the disease network. Practically, our network represents a methodology not only for identifying new connectivity that is not found in genome-based disease networks but also for estimating directionality, strength, and progression time to transition between diseases considering gender, age and incidence. Thus, our network provides a guide for investigators for future research and contributes to achieving precision medicine.
546			_aEnglish
650			_a*Diagnosis
650			_a*Neural Networks (Computer)
650			_a*Precision Medicine
650			_aAge Factors
650			_aGender Identity
650			_aGenome, Human
650			_aHumans
650			_aRisk Factors
651			_aMedStar Washington Hospital Center
656			_aPathology
657			_aJournal Article
700			_aKo, Kyungmin
790			_aHan HW, Jeong E, Ko K, Oh S
856			_uhttps://dx.doi.org/10.1038/s41598-017-15647-4 _zhttps://dx.doi.org/10.1038/s41598-017-15647-4
942			_cART _dArticle
999			_c2854 _d2854