{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T03:20:35Z","timestamp":1782530435442,"version":"3.54.5"},"reference-count":58,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec027\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acute kidney injury (AKI) can lead to an approximate ninefold increased risk for developing chronic kidney disease (CKD). Despite this, many AKI survivors lack proper nephrology follow-up, highlighting the urgent need to identify patient profiles before onset CKD. Thus, we aimed to develop a patient profiling algorithm to identify clinical phenotypes from AKI to CKD progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec028\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study utilized electronic health records data from 2010 to 2022. We classified AKI into three groups: Hospital Acquired AKI (HA-AKI), Community Acquired AKI (CA-AKI), and No-AKI. We developed a custom patient profiling algorithm by combining network-based community and variable clustering methods to examine risk factors among three groups. The top three clusters were presented using comorbidities and medical procedures network graphs, and matched between two methods to find similarities and dissimilarities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec029\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 58,876 CKD patients, 10.2% (5,981) and 11.5% (6,762) had HA-AKI and CA-AKI, respectively. The No-AKI group had a higher comorbidity burden compared to AKI groups, with average comorbidities of 2.84 vs. 2.04. Commonly risk factors observed in both AKI cohorts included long-term opiate analgesic use, atelectasis, history of ischemic heart disease, and lactic acidosis. The comorbidity network in HA-AKI patients was more complex compared to CA-AKI and No-AKI groups with higher number of diagnosis (64 vs 62 vs 55). The HA-AKI cohort had several conditions with higher degree (mean number of edges connected to each diagnosis) and betweenness centrality (bridges connecting different diagnosis clusters) including high cholesterol (34, 91.10), chronic pain (33, 103.38), tricuspid insufficiency (38, 113.37), osteoarthritis (34, 56.14), and removal of GI tract components (37, 68.66) compared to the CA-AKI cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec030\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our proposed patient profiling algorithm successfully identifies AKI phenotypes toward CKD progression, offering a promising approach to identify early risk factors for CKD in improving targeted prevention strategies and reducing healthcare expenditures.<\/jats:p>\n                  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Poonia","year":"2022","journal-title":"Healthcare"},{"issue":"4","key":"pone.0310749.ref031","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1021\/acs.jcim.9b01008","article-title":"HNet-DNN: Inferring new drug-disease associations with deep neural network based on heterogeneous network features","volume":"60","author":"H Liu","year":"2020","journal-title":"J Chem Inf Model"},{"key":"pone.0310749.ref032","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijmedinf.2018.04.001","article-title":"Comorbidity network for chronic disease: a novel approach to understand type 2 diabetes progression","volume":"115","author":"A Khan","year":"2018","journal-title":"Int J Med Inform"},{"issue":"1","key":"pone.0310749.ref033","doi-asserted-by":"crossref","DOI":"10.3390\/healthcare10010080","article-title":"Phenotypic disease network analysis to identify comorbidity patterns in hospitalized patients with ischemic heart disease using large-scale administrative data","volume":"10","author":"D Zhou","year":"2022","journal-title":"Healthcare"},{"key":"pone.0310749.ref034","author":"V Carolan","year":"2014","journal-title":"Social network analysis and education: theory, methods and applications"},{"issue":"1","key":"pone.0310749.ref035","doi-asserted-by":"crossref","DOI":"10.1186\/s12911-024-02497-0","article-title":"Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis","volume":"24","author":"C Martins","year":"2024","journal-title":"BMC Med Inform Decis Mak"},{"key":"pone.0310749.ref036","doi-asserted-by":"crossref","first-page":"036103","DOI":"10.1103\/PhysRevE.83.036103","article-title":"Unfolding communities in large complex networks: combining defensive and offensive label propagation for core extraction","volume":"83","author":"L Subelj","year":"2011","journal-title":"Phys Rev E Stat Nonlin Soft Matter Phys"},{"key":"pone.0310749.ref037","first-page":"361","article-title":"Gephi: an open source software for exploring and manipulating networks.","volume-title":"Proceedings of the International AAAI conference on web and social media","author":"M Bastian","year":"2009"},{"key":"pone.0310749.ref038","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.knosys.2014.06.016","article-title":"ClustOfVar and the segmentation of cruise passengers from mixed data: some managerial implications","volume":"70","author":"JG Brida","year":"2014","journal-title":"Knowl Based Syst"},{"key":"pone.0310749.ref039","author":"BD Ripley"},{"key":"pone.0310749.ref040","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.2147\/DMSO.S307776","article-title":"Diabetes mellitus as a risk factor for progression from acute kidney injury to acute kidney disease: a specific prediction model","volume":"14","author":"H Zhao","year":"2021","journal-title":"Diabetes Metab Syndr 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