Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach
The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profile...
| Authors: | ; ; ; ; ; ; |
|---|---|
| Format: | Article |
| Status: | Published version |
| Publication date: | 2021 |
| Country: | España |
| Institution: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repository: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/19604 |
| Online Access: | http://hdl.handle.net/10256/19604 |
| Access Level: | Open access |
| Keyword: | Diabetis Diabetes Intel·ligència artificial -- Aplicacions a la medicina Artificial intelligence -- Medical applications Sistemes d'ajuda a la decisió Decision support systems |
| _version_ | 1878739882017292288 |
|---|---|
| author | Biagi, Lyvia |
| author2 | Bertachi, Arthur Hirata Giménez, Marga Conget, Ignacio Bondia, Jorge Martín Fernández, Josep Antoni Vehí, Josep |
| author2_role | author author author author author author |
| author_browse | Bertachi, Arthur Hirata Biagi, Lyvia Bondia, Jorge Conget, Ignacio Giménez, Marga Martín Fernández, Josep Antoni Vehí, Josep |
| author_facet | Biagi, Lyvia Bertachi, Arthur Hirata Giménez, Marga Conget, Ignacio Bondia, Jorge Martín Fernández, Josep Antoni Vehí, Josep |
| author_role | author |
| collection | Recercat. Dipósit de la Recerca de Catalunya |
| description | The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients’ outcomes and then tailor their therapies |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_c9b0c44cd00a933f7a662e64d0b35a19 |
| institution | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| instname_str | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:recercat.cat:10256/19604 |
| publishDate | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI (Multidisciplinary Digital Publishing Institute) |
| publisherStr | MDPI (Multidisciplinary Digital Publishing Institute) |
| reponame_str | Recercat. Dipósit de la Recerca de Catalunya |
| spelling | Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach Biagi, Lyvia Bertachi, Arthur Hirata Giménez, Marga Conget, Ignacio Bondia, Jorge Martín Fernández, Josep Antoni Vehí, Josep Diabetis Diabetes Intel·ligència artificial -- Aplicacions a la medicina Artificial intelligence -- Medical applications Sistemes d'ajuda a la decisió Decision support systems The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients’ outcomes and then tailor their therapies This project has been partially supported by the Spanish Government, Ministerio de Economía y Competitividad (MINECO) (Grants DPI2016-78831-C2-1-R, DPI2016-78831-C2-2-R, RTI2018-095518-B-C21), Agencia Estatal de Investigación (PID2019-107722RB-C21/ AEI/10.13039/501100011033), the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7, 207688/2014-1 and EU through FEDER funds MDPI (Multidisciplinary Digital Publishing Institute) http://hdl.handle.net/10256/19604 |
| spellingShingle | Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach Biagi, Lyvia Diabetis Diabetes Intel·ligència artificial -- Aplicacions a la medicina Artificial intelligence -- Medical applications Sistemes d'ajuda a la decisió Decision support systems |
| status_str | publishedVersion |
| title | Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach |
| title_full | Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach |
| title_fullStr | Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach |
| title_full_unstemmed | Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach |
| title_short | Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach |
| title_sort | Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach |
| topic | Diabetis Diabetes Intel·ligència artificial -- Aplicacions a la medicina Artificial intelligence -- Medical applications Sistemes d'ajuda a la decisió Decision support systems |
| topic_facet | Diabetis Diabetes Intel·ligència artificial -- Aplicacions a la medicina Artificial intelligence -- Medical applications Sistemes d'ajuda a la decisió Decision support systems |
| url | http://hdl.handle.net/10256/19604 |