Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data
Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbi...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer International Publishing
2018
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| Online Access: | http://hdl.handle.net/1721.1/119053 https://orcid.org/0000-0003-2911-1911 |
| _version_ | 1826193918144806912 |
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| author | Fotouhi, Babak Riolo, Maria A. Buckeridge, David L. Momeni Taramsari, Naghmeh |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Fotouhi, Babak Riolo, Maria A. Buckeridge, David L. Momeni Taramsari, Naghmeh |
| author_sort | Fotouhi, Babak |
| collection | MIT |
| description | Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods. Keywords: Weighted networks; Null model; Comorbidity; Disease networks; Centrality |
| first_indexed | 2024-09-23T09:47:21Z |
| format | Article |
| id | mit-1721.1/119053 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:47:21Z |
| publishDate | 2018 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1190532022-09-26T13:45:26Z Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data Fotouhi, Babak Riolo, Maria A. Buckeridge, David L. Momeni Taramsari, Naghmeh Sloan School of Management Momeni Taramsari, Naghmeh Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods. Keywords: Weighted networks; Null model; Comorbidity; Disease networks; Centrality 2018-11-15T16:23:37Z 2018-11-15T16:23:37Z 2018-11 2018-04 2018-11-08T06:10:41Z Article http://purl.org/eprint/type/JournalArticle 2364-8228 http://hdl.handle.net/1721.1/119053 Fotouhi, Babak et al. "Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data." Applied Network Science 2018, 3 (November 2018): 46 © 2018 The Author(s) https://orcid.org/0000-0003-2911-1911 en https://doi.org/10.1007/s41109-018-0101-4 Applied Network Science Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer International Publishing Springer International Publishing |
| spellingShingle | Fotouhi, Babak Riolo, Maria A. Buckeridge, David L. Momeni Taramsari, Naghmeh Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data |
| title | Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data |
| title_full | Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data |
| title_fullStr | Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data |
| title_full_unstemmed | Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data |
| title_short | Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data |
| title_sort | statistical methods for constructing disease comorbidity networks from longitudinal inpatient data |
| url | http://hdl.handle.net/1721.1/119053 https://orcid.org/0000-0003-2911-1911 |
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