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Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes
AIMS: The objective of this study is to establish a predictive model using transparent machine learning (ML) to identify any drivers that characterize therapeutic inertia. METHODS: Data in the form of both descriptive and dynamic variables collected from electronic records of 1.5 million patients se...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wiley Publishing Asia Pty Ltd
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036260/ https://www.ncbi.nlm.nih.gov/pubmed/36889912 http://dx.doi.org/10.1111/1753-0407.13361 |
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author | Musacchio, Nicoletta Zilich, Rita Ponzani, Paola Guaita, Giacomo Giorda, Carlo Heidbreder, Rebeca Santin, Pierluigi Di Cianni, Graziano |
author_facet | Musacchio, Nicoletta Zilich, Rita Ponzani, Paola Guaita, Giacomo Giorda, Carlo Heidbreder, Rebeca Santin, Pierluigi Di Cianni, Graziano |
author_sort | Musacchio, Nicoletta |
collection | PubMed |
description | AIMS: The objective of this study is to establish a predictive model using transparent machine learning (ML) to identify any drivers that characterize therapeutic inertia. METHODS: Data in the form of both descriptive and dynamic variables collected from electronic records of 1.5 million patients seen at clinics within the Italian Association of Medical Diabetologists between 2005–2019 were analyzed using logic learning machine (LLM), a “clear box” ML technique. Data were subjected to a first stage of modeling to allow ML to automatically select the most relevant factors related to inertia, and then four further modeling steps individuated key variables that discriminated the presence or absence of inertia. RESULTS: The LLM model revealed a key role for average glycated hemoglobin (HbA1c) threshold values correlated with the presence or absence of insulin therapeutic inertia with an accuracy of 0.79. The model indicated that a patient's dynamic rather than static glycemic profile has a greater effect on therapeutic inertia. Specifically, the difference in HbA1c between two consecutive visits, what we call the HbA1c gap, plays a crucial role. Namely, insulin therapeutic inertia is correlated with an HbA1c gap of <6.6 mmol/mol (0.6%), but not with an HbA1c gap of >11 mmol/mol (1.0%). CONCLUSIONS: The results reveal, for the first time, the interrelationship between a patient's glycemic trend defined by sequential HbA1c measurements and timely or delayed initiation of insulin therapy. The results further demonstrate that LLM can provide insight in support of evidence‐based medicine using real world data. |
format | Online Article Text |
id | pubmed-10036260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Wiley Publishing Asia Pty Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-100362602023-03-25 Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes Musacchio, Nicoletta Zilich, Rita Ponzani, Paola Guaita, Giacomo Giorda, Carlo Heidbreder, Rebeca Santin, Pierluigi Di Cianni, Graziano J Diabetes Original Articles AIMS: The objective of this study is to establish a predictive model using transparent machine learning (ML) to identify any drivers that characterize therapeutic inertia. METHODS: Data in the form of both descriptive and dynamic variables collected from electronic records of 1.5 million patients seen at clinics within the Italian Association of Medical Diabetologists between 2005–2019 were analyzed using logic learning machine (LLM), a “clear box” ML technique. Data were subjected to a first stage of modeling to allow ML to automatically select the most relevant factors related to inertia, and then four further modeling steps individuated key variables that discriminated the presence or absence of inertia. RESULTS: The LLM model revealed a key role for average glycated hemoglobin (HbA1c) threshold values correlated with the presence or absence of insulin therapeutic inertia with an accuracy of 0.79. The model indicated that a patient's dynamic rather than static glycemic profile has a greater effect on therapeutic inertia. Specifically, the difference in HbA1c between two consecutive visits, what we call the HbA1c gap, plays a crucial role. Namely, insulin therapeutic inertia is correlated with an HbA1c gap of <6.6 mmol/mol (0.6%), but not with an HbA1c gap of >11 mmol/mol (1.0%). CONCLUSIONS: The results reveal, for the first time, the interrelationship between a patient's glycemic trend defined by sequential HbA1c measurements and timely or delayed initiation of insulin therapy. The results further demonstrate that LLM can provide insight in support of evidence‐based medicine using real world data. Wiley Publishing Asia Pty Ltd 2023-03-08 /pmc/articles/PMC10036260/ /pubmed/36889912 http://dx.doi.org/10.1111/1753-0407.13361 Text en © 2023 Associazione Medici Diabetologi (AMD). Journal of Diabetes published by Ruijin Hospital, Shanghai JiaoTong University School of Medicine and John Wiley & Sons Australia, Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Musacchio, Nicoletta Zilich, Rita Ponzani, Paola Guaita, Giacomo Giorda, Carlo Heidbreder, Rebeca Santin, Pierluigi Di Cianni, Graziano Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes |
title | Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes |
title_full | Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes |
title_fullStr | Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes |
title_full_unstemmed | Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes |
title_short | Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes |
title_sort | transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036260/ https://www.ncbi.nlm.nih.gov/pubmed/36889912 http://dx.doi.org/10.1111/1753-0407.13361 |
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