Predicting diabetes mellitus metabolic goals and chronic complications transitions—analysis based on natural language processing and machine learning models Article Swipe
Claudia Carolina Colmenares Mejía
,
Andrés García-Suaza
,
Paul Rodríguez‐Lesmes
,
Christian Lochmüller
,
Sara Atehortúa
,
Javier Enrique Camacho-Cogollo
,
Juan Pablo Martínez
,
J. M. Castro Rincón
,
Yohan R Céspedes
,
Esteban Morales-Mendoza
,
Mario A. Isaza-Ruget
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0321258
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0321258
Objective To estimate Diabetes mellitus (DM) progression at one and two years in terms of glycemic targets and development of complications. Research design and methods We analyzed a retrospective cohort of adult DM patients treated in a Health Maintenance Organization in Colombia, including those with at least one glycosylated hemoglobin (HbA1c) measurement in 2018, 2019, and 2020. We defined four disease transition stages based on metabolic goals according to HbA1c levels and complications: 1. Within HbA1c goals and without complications; 2. Outside goals and without complications, 3. Within goals, but with complications, and 4. Outside goals and with complications. We applied Natural Language Processing (NLP) techniques to extract relevant clinical information from Electronic Health Records. Machine learning (ML) models were used to predict patient progression. Results A total of 23,802 patients were included. Despite achieving initial glycemic control, more than 60% of patients who started within HbA1c targets and without complications developed chronic complications within two years. Our models, which achieved up to 80% accuracy and F1 scores above 74%, identified key predictors of disease progression. Adherence to dyslipidemia treatment guidelines significantly reduced the likelihood of HbA1c deterioration and complications, whereas non-adherence to pharmacological treatments increased the risk of complications. These findings suggest that HbA1c control alone is insufficient to prevent disease progression and that a more comprehensive management approach—including lipid control, kidney function monitoring, and improved adherence to clinical guidelines—is necessary. Conclusions Patient compliance with pharmacological treatments, professional adherence to clinical practice guidelines, and lifestyle interventions play a crucial role in diabetes progression. While our models provide strong predictive capabilities, improving data quality and integration remains essential for better forecasting and intervention strategies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0321258
- OA Status
- gold
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409480876
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4409480876Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0321258Digital Object Identifier
- Title
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Predicting diabetes mellitus metabolic goals and chronic complications transitions—analysis based on natural language processing and machine learning modelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-15Full publication date if available
- Authors
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Claudia Carolina Colmenares Mejía, Andrés García-Suaza, Paul Rodríguez‐Lesmes, Christian Lochmüller, Sara Atehortúa, Javier Enrique Camacho-Cogollo, Juan Pablo Martínez, J. M. Castro Rincón, Yohan R Céspedes, Esteban Morales-Mendoza, Mario A. Isaza-RugetList of authors in order
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https://doi.org/10.1371/journal.pone.0321258Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1371/journal.pone.0321258Direct OA link when available
- Concepts
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Glycemic, Medicine, Dyslipidemia, Diabetes mellitus, Disease, Intensive care medicine, Kidney disease, Cohort, Psychological intervention, Retrospective cohort study, Internal medicine, Endocrinology, NursingTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.treatments, | 237 |
| abstract_inverted_index.Organization | 39 |
| abstract_inverted_index.dyslipidemia | 178 |
| abstract_inverted_index.glycosylated | 48 |
| abstract_inverted_index.insufficient | 208 |
| abstract_inverted_index.intervention | 272 |
| abstract_inverted_index.professional | 238 |
| abstract_inverted_index.progression. | 124, 175, 253 |
| abstract_inverted_index.capabilities, | 260 |
| abstract_inverted_index.complications | 150, 153 |
| abstract_inverted_index.comprehensive | 217 |
| abstract_inverted_index.deterioration | 187 |
| abstract_inverted_index.interventions | 246 |
| abstract_inverted_index.non-adherence | 191 |
| abstract_inverted_index.retrospective | 28 |
| abstract_inverted_index.significantly | 181 |
| abstract_inverted_index.complications, | 85, 91, 189 |
| abstract_inverted_index.complications. | 20, 98, 199 |
| abstract_inverted_index.complications: | 72 |
| abstract_inverted_index.complications; | 79 |
| abstract_inverted_index.guidelines—is | 230 |
| abstract_inverted_index.pharmacological | 193, 236 |
| abstract_inverted_index.approach—including | 219 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.15742315 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |