Journal article 845 views 215 downloads
Ranking sets of morbidities using hypergraph centrality
Jim Rafferty 
,
Alan Watkins ,
Jane Lyons,
Ronan Lyons ,
Ashley Akbari ,
Niels Peek,
Farideh Jalali-najafabadi,
Thamer Ba Dhafari,
Alexander Pate,
Glen P. Martin,
Rowena Bailey
,
Alan Watkins ,
Jane Lyons,
Ronan Lyons ,
Ashley Akbari ,
Niels Peek,
Farideh Jalali-najafabadi,
Thamer Ba Dhafari,
Alexander Pate,
Glen P. Martin,
Rowena Bailey
Journal of Biomedical Informatics, Volume: 122, Start page: 103916
Swansea University Authors:
Jim Rafferty , Alan Watkins , Jane Lyons, Ronan Lyons , Ashley Akbari , Rowena Bailey
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DOI (Published version): 10.1016/j.jbi.2021.103916
Abstract
Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and out...
| Published in: | Journal of Biomedical Informatics |
|---|---|
| ISSN: | 1532-0464 |
| Published: |
Elsevier BV
2021
|
| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa57958 |
| first_indexed |
2021-09-22T15:18:31Z |
|---|---|
| last_indexed |
2024-11-14T12:12:48Z |
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cronfa57958 |
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SURis |
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Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the cen-trality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and in-formation available in EHR data.</abstract><type>Journal Article</type><journal>Journal of Biomedical Informatics</journal><volume>122</volume><journalNumber/><paginationStart>103916</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1532-0464</issnPrint><issnElectronic/><keywords>Multi-morbidity, Network analysis, Hypergraph</keywords><publishedDay>1</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-10-01</publishedDate><doi>10.1016/j.jbi.2021.103916</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>SU College/Department paid the OA fee</apcterm><funders>This work was funded by the Medical Research Council (MRC) (Grant No.: MR/S027750/1); and supported by Health Data Research UK (Grant No.: HDR-9006), which receives its funding from the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust; and Administrative Data Research UK, which is funded by the Economic and Social Research Council (Grant No.: ES/S007393/1). FJ is supported by an MRC/University of Manchester Skills Development Fellowship (Grant No. MR/R016615). 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2023-09-13T16:14:07.4740099 v2 57958 2021-09-19 Ranking sets of morbidities using hypergraph centrality 52effe759a718bd36eb12cdd10fe1a09 0000-0002-1667-7265 Jim Rafferty Jim Rafferty true false 81fc05c9333d9df41b041157437bcc2f 0000-0003-3804-1943 Alan Watkins Alan Watkins true false 1b74fa5125a88451c52c45bcf20e0b47 Jane Lyons Jane Lyons true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 455e2c1e6193448f6269b9e72acaf865 Rowena Bailey Rowena Bailey true false 2021-09-19 MEDS Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the cen-trality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and in-formation available in EHR data. Journal Article Journal of Biomedical Informatics 122 103916 Elsevier BV 1532-0464 Multi-morbidity, Network analysis, Hypergraph 1 10 2021 2021-10-01 10.1016/j.jbi.2021.103916 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University SU College/Department paid the OA fee This work was funded by the Medical Research Council (MRC) (Grant No.: MR/S027750/1); and supported by Health Data Research UK (Grant No.: HDR-9006), which receives its funding from the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust; and Administrative Data Research UK, which is funded by the Economic and Social Research Council (Grant No.: ES/S007393/1). FJ is supported by an MRC/University of Manchester Skills Development Fellowship (Grant No. MR/R016615). The funder was not involved in the study design, analysis of the data or preparation of the manuscript. 2023-09-13T16:14:07.4740099 2021-09-19T16:47:52.2857944 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Jim Rafferty 0000-0002-1667-7265 1 Alan Watkins 0000-0003-3804-1943 2 Jane Lyons 3 Ronan Lyons 0000-0001-5225-000X 4 Ashley Akbari 0000-0003-0814-0801 5 Niels Peek 6 Farideh Jalali-najafabadi 7 Thamer Ba Dhafari 8 Alexander Pate 9 Glen P. Martin 10 Rowena Bailey 11 57958__20970__9763dde8098a4a0590fc3295e81437f4.pdf 57958.VOR.pdf 2021-09-22T16:21:37.8123167 Output 2115253 application/pdf Version of Record true Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title |
Ranking sets of morbidities using hypergraph centrality |
| spellingShingle |
Ranking sets of morbidities using hypergraph centrality Jim Rafferty Alan Watkins Jane Lyons Ronan Lyons Ashley Akbari Rowena Bailey |
| title_short |
Ranking sets of morbidities using hypergraph centrality |
| title_full |
Ranking sets of morbidities using hypergraph centrality |
| title_fullStr |
Ranking sets of morbidities using hypergraph centrality |
| title_full_unstemmed |
Ranking sets of morbidities using hypergraph centrality |
| title_sort |
Ranking sets of morbidities using hypergraph centrality |
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52effe759a718bd36eb12cdd10fe1a09 81fc05c9333d9df41b041157437bcc2f 1b74fa5125a88451c52c45bcf20e0b47 83efcf2a9dfcf8b55586999d3d152ac6 aa1b025ec0243f708bb5eb0a93d6fb52 455e2c1e6193448f6269b9e72acaf865 |
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52effe759a718bd36eb12cdd10fe1a09_***_Jim Rafferty 81fc05c9333d9df41b041157437bcc2f_***_Alan Watkins 1b74fa5125a88451c52c45bcf20e0b47_***_Jane Lyons 83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari 455e2c1e6193448f6269b9e72acaf865_***_Rowena Bailey |
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Jim Rafferty Alan Watkins Jane Lyons Ronan Lyons Ashley Akbari Rowena Bailey |
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Jim Rafferty Alan Watkins Jane Lyons Ronan Lyons Ashley Akbari Niels Peek Farideh Jalali-najafabadi Thamer Ba Dhafari Alexander Pate Glen P. Martin Rowena Bailey |
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Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the cen-trality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and in-formation available in EHR data. |
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2021-10-01T14:08:33Z |
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