{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:13:29Z","timestamp":1787238809843,"version":"build-2736575974"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004329","name":"Slovenian Research Agency","doi-asserted-by":"publisher","award":["P1-0403, J1-2457, N1-0232, J3-3077, J5-3114, P1-0055, I0-0029"],"award-info":[{"award-number":["P1-0403, J1-2457, N1-0232, J3-3077, J5-3114, P1-0055, I0-0029"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Type 2 diabetes mellitus (T2DM) is a complex, chronic disease affecting multiple organs with varying symptoms and comorbidities. Profiling patients helps identify those with unfavorable disease progression, allowing for tailored therapy and addressing special needs. This study aims to uncover different T2DM profiles based on medication intake records and laboratory measurements, with a focus on how individuals with diabetes move through disease phases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We use medical records from databases of the last 20\u2009years from the Department of Endocrinology and Diabetology of the University Medical Center in Maribor. Using the standard ATC medication classification system, we created a patient-specific drug profile, created using advanced natural language processing methods combined with data mining and hierarchical clustering.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40\u201350 age group, and the same is true for the last 80+ age group. One of these profiles includes individuals with diabetes with very low use of various medications, while the other profile includes individuals with diabetes with much higher use. The number in both groups is reciprocal. Conversely, the middle-aged groups are characterized by several distinct profiles with a wide range of medications that are associated with the distinct concomitant complications of T2DM. It is intuitive that the number of profiles increases in the later age groups, but it is not obvious why it is reduced later in the 80+ age group. In this context, further studies are needed to evaluate the contributions of a range of factors, such as drug development, drug adoption, and the impact of mortality associated with all T2DM-related diseases, which characterize these middle-aged groups, particularly those aged 55\u201375.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our approach aligns with existing studies and can be widely implemented without complex or expensive analyses. Treatment and drug use data are readily available in healthcare facilities worldwide, allowing for profiling insights into individuals with diabetes. Integrating data from other departments, such as cardiology and renal disease, may provide a more sophisticated understanding of T2DM patient profiles.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2023.1209809","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T10:43:11Z","timestamp":1688640191000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Profiling of patients with type 2 diabetes based on medication adherence data"],"prefix":"10.3389","volume":"11","author":[{"given":"Rene","family":"Markovi\u010d","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladimir","family":"Grubelnik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tadej","family":"Zavr\u0161nik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helena","family":"Bla\u017eun Vo\u0161ner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Kokol","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matja\u017e","family":"Perc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marko","family":"Marhl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matej","family":"Zavr\u0161nik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jernej","family":"Zavr\u0161nik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"ref1","unstructured":"Statistics about diabetes2021"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1038\/s41387-022-00216-0","article-title":"Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study","volume":"12","author":"Li","year":"2022","journal-title":"Nutr Diabetes"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"101898","DOI":"10.1016\/j.artmed.2020.101898","article-title":"Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks","volume":"107","author":"Pellicer-Valero","year":"2020","journal-title":"Artif Intell Med"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"001","DOI":"10.1055\/s-0038-1676067","article-title":"Total knee arthroplasty in patients of advanced age: a look at outcomes and complications","volume":"33","author":"Passias","year":"2020","journal-title":"J Knee Surg"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s10067-022-06471-x","article-title":"JAK inhibitors, cardiovascular and thromboembolic events: what we know and what we would like to know","volume":"42","author":"Evangelatos","year":"2023","journal-title":"Clin Rheumatol"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1002\/ejhf.2206","article-title":"Patient profiling in heart failure for tailoring medical therapy. 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