Network-based analysis of diagnosis progression patterns using claims data.

MedStar author(s):
Citation: Scientific Reports. 7(1):15561, 2017 Nov 14PMID: 29138438Institution: MedStar Washington Hospital CenterDepartment: PathologyForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Diagnosis | *Neural Networks (Computer) | *Precision Medicine | Age Factors | Gender Identity | Genome, Human | Humans | Risk FactorsYear: 2017ISSN:
  • 2045-2322
Name of journal: Scientific reportsAbstract: In 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.All authors: Han HW, Jeong E, Ko K, Oh SFiscal year: FY2018Digital Object Identifier: Date added to catalog: 2017-12-05
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Journal Article MedStar Authors Catalog Article 29138438 Available 29138438

In 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.

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