Profiling of patients with type 2 diabetes based on medication adherence data
IntroductionType 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 un...
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1209809/full |
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| author | Rene Markovič Rene Markovič Vladimir Grubelnik Tadej Završnik Tadej Završnik Helena Blažun Vošner Helena Blažun Vošner Helena Blažun Vošner Peter Kokol Matjaž Perc Matjaž Perc Matjaž Perc Matjaž Perc Matjaž Perc Marko Marhl Marko Marhl Marko Marhl Matej Završnik Jernej Završnik Jernej Završnik Jernej Završnik Jernej Završnik |
| author_facet | Rene Markovič Rene Markovič Vladimir Grubelnik Tadej Završnik Tadej Završnik Helena Blažun Vošner Helena Blažun Vošner Helena Blažun Vošner Peter Kokol Matjaž Perc Matjaž Perc Matjaž Perc Matjaž Perc Matjaž Perc Marko Marhl Marko Marhl Marko Marhl Matej Završnik Jernej Završnik Jernej Završnik Jernej Završnik Jernej Završnik |
| author_sort | Rene Markovič |
| collection | DOAJ |
| description | IntroductionType 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.MethodsWe use medical records from databases of the last 20 years 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.ResultsOur results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40–50 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 55–75.ConclusionOur 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. |
| first_indexed | 2024-03-13T00:58:32Z |
| format | Article |
| id | doaj.art-e5eb955c3bb84a07a031eee54ea1536e |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-13T00:58:32Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-e5eb955c3bb84a07a031eee54ea1536e2023-07-06T14:42:20ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-07-011110.3389/fpubh.2023.12098091209809Profiling of patients with type 2 diabetes based on medication adherence dataRene Markovič0Rene Markovič1Vladimir Grubelnik2Tadej Završnik3Tadej Završnik4Helena Blažun Vošner5Helena Blažun Vošner6Helena Blažun Vošner7Peter Kokol8Matjaž Perc9Matjaž Perc10Matjaž Perc11Matjaž Perc12Matjaž Perc13Marko Marhl14Marko Marhl15Marko Marhl16Matej Završnik17Jernej Završnik18Jernej Završnik19Jernej Završnik20Jernej Završnik21Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaUniversity Clinical Medical Centre Maribor, Maribor, SloveniaFaculty of Medicine, University of Maribor, Maribor, SloveniaCommunity Healthcare Center Dr. Adolf Drolc Maribor, Maribor, SloveniaFaculty of Health and Social Sciences, Slovenj Gradec, SloveniaAlma Mater Europaea - ECM, Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaFaculty of Natural Sciences and Mathematics, University of Maribor, Maribor, SloveniaAlma Mater Europaea - ECM, Maribor, SloveniaComplexity Science Hub Vienna, Vienna, AustriaDepartment of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan0Department of Physics, Kyung Hee University, Seoul, Republic of KoreaFaculty of Natural Sciences and Mathematics, University of Maribor, Maribor, SloveniaFaculty of Medicine, University of Maribor, Maribor, Slovenia1Faculty of Education, University of Maribor, Maribor, Slovenia2Department of Endocrinology and Diabetology, University Medical Center Maribor, Maribor, SloveniaFaculty of Natural Sciences and Mathematics, University of Maribor, Maribor, SloveniaCommunity Healthcare Center Dr. Adolf Drolc Maribor, Maribor, SloveniaAlma Mater Europaea - ECM, Maribor, Slovenia3Science and Research Center Koper, Koper, SloveniaIntroductionType 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.MethodsWe use medical records from databases of the last 20 years 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.ResultsOur results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40–50 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 55–75.ConclusionOur 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.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1209809/fullmedication managementtype 2 diabetes mellituspatient profilescluster analysiselectronic health recordsmedication usage patterns |
| spellingShingle | Rene Markovič Rene Markovič Vladimir Grubelnik Tadej Završnik Tadej Završnik Helena Blažun Vošner Helena Blažun Vošner Helena Blažun Vošner Peter Kokol Matjaž Perc Matjaž Perc Matjaž Perc Matjaž Perc Matjaž Perc Marko Marhl Marko Marhl Marko Marhl Matej Završnik Jernej Završnik Jernej Završnik Jernej Završnik Jernej Završnik Profiling of patients with type 2 diabetes based on medication adherence data Frontiers in Public Health medication management type 2 diabetes mellitus patient profiles cluster analysis electronic health records medication usage patterns |
| title | Profiling of patients with type 2 diabetes based on medication adherence data |
| title_full | Profiling of patients with type 2 diabetes based on medication adherence data |
| title_fullStr | Profiling of patients with type 2 diabetes based on medication adherence data |
| title_full_unstemmed | Profiling of patients with type 2 diabetes based on medication adherence data |
| title_short | Profiling of patients with type 2 diabetes based on medication adherence data |
| title_sort | profiling of patients with type 2 diabetes based on medication adherence data |
| topic | medication management type 2 diabetes mellitus patient profiles cluster analysis electronic health records medication usage patterns |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1209809/full |
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