A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management Article Swipe
Muhammad Salman Haleem
,
Daphne N. Katsarou
,
Eleni I. Georga
,
George Dafoulas
,
Αlexandra Bargiota
,
Laura Lopez-Perez
,
Miguel Rujas
,
Giuseppe Fico
,
Leandro Pecchia
,
Dimitrios I. Fotiadis
,
Claudio Caimi
,
Christian Tamporale
,
Mirko Manea
,
Chiara Bonferini
,
Eugenio Gaeta
,
Gloria Cea
,
Ioanna Drympeta
,
Konstantinos Votis
,
Frans Folkvord
,
J. Battle
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-07272-3
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-07272-3
The accurate prediction of blood glucose is critical for the effective management of diabetes. Modern continuous glucose monitoring (CGM) technology enables real-time acquisition of interstitial glucose concentrations, which can be calibrated against blood glucose measurements. However, a key challenge in the effective management of type 2 diabetes lies in forecasting critical events driven by glucose variability. While recent advances in deep learning enable modeling of temporal patterns in glucose fluctuations, most of the existing methods rely on unimodal inputs and fail to account for individual physiological differences that influence interstitial glucose dynamics. These limitations highlight the need for multimodal approaches that integrate additional personalized physiological information. One of the primary reasons for multimodal approaches not being widely studied in this field is the bottleneck associated with the availability of subjects’ health records. In this paper, we propose a multimodal approach trained on sequences of CGM values and enriched with physiological context derived from health records of 40 individuals with type 2 diabetes. The CGM time series were processed using a stacked Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network followed by an attention mechanism. The BiLSTM learned long-term temporal dependencies, while the CNN captured local sequential features. Physiological heterogeneity was incorporated through a separate pipeline of neural networks that processed baseline health records and was later fused with the CGM modeling stream. To validate our model, we utilized CGM values of 30 min sampled with a moving window of 5 min to predict the CGM values with a prediction horizon of (a) 15 min, (b) 30 min, and (c) 60 min. We achieved the multimodal architecture prediction results with Mean Absolute Point Error (MAPE) between 14 and 24 mg/dL, 19–22 mg/dL, 25–26 mg/dL in case of Menarini sensor and 6–11 mg/dL, 9–14 mg/dL, 12–18 mg/dL in case of Abbot sensor for 15, 30 and 60 min prediction horizon respectively. The results suggested that the proposed multimodal model achieved higher prediction accuracy compared to unimodal approaches; with upto 96.7% prediction accuracy; supporting its potential as a generalizable solution for interstitial glucose prediction and personalized management in the type 2 diabetes population.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-07272-3
- OA Status
- gold
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412785209
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4412785209Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-025-07272-3Digital Object Identifier
- Title
-
A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes managementWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-29Full publication date if available
- Authors
-
Muhammad Salman Haleem, Daphne N. Katsarou, Eleni I. Georga, George Dafoulas, Αlexandra Bargiota, Laura Lopez-Perez, Miguel Rujas, Giuseppe Fico, Leandro Pecchia, Dimitrios I. Fotiadis, Claudio Caimi, Christian Tamporale, Mirko Manea, Chiara Bonferini, Eugenio Gaeta, Gloria Cea, Ioanna Drympeta, Konstantinos Votis, Frans Folkvord, J. BattleList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-07272-3Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1038/s41598-025-07272-3Direct OA link when available
- Concepts
-
Computer science, Type 2 diabetes, Architecture, Deep learning, Artificial intelligence, Diabetes mellitus, Data science, Bioinformatics, Medicine, Endocrinology, Biology, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4412785209 |
|---|---|
| doi | https://doi.org/10.1038/s41598-025-07272-3 |
| ids.doi | https://doi.org/10.1038/s41598-025-07272-3 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40730832 |
| ids.openalex | https://openalex.org/W4412785209 |
| fwci | 0.0 |
| mesh[0].qualifier_ui | Q000097 |
| mesh[0].descriptor_ui | D003924 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | blood |
| mesh[0].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[1].qualifier_ui | Q000628 |
| mesh[1].descriptor_ui | D003924 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | therapy |
| mesh[1].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D006801 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Humans |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D000077321 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Deep Learning |
| mesh[4].qualifier_ui | Q000032 |
| mesh[4].descriptor_ui | D001786 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | analysis |
| mesh[4].descriptor_name | Blood Glucose |
| mesh[5].qualifier_ui | Q000379 |
| mesh[5].descriptor_ui | D015190 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | methods |
| mesh[5].descriptor_name | Blood Glucose Self-Monitoring |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D016571 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Neural Networks, Computer |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D008875 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Middle Aged |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D008297 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Male |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D005260 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Female |
| mesh[10].qualifier_ui | Q000097 |
| mesh[10].descriptor_ui | D003924 |
| mesh[10].is_major_topic | True |
| mesh[10].qualifier_name | blood |
| mesh[10].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[11].qualifier_ui | Q000628 |
| mesh[11].descriptor_ui | D003924 |
| mesh[11].is_major_topic | True |
| mesh[11].qualifier_name | therapy |
| mesh[11].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D006801 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Humans |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D000077321 |
| mesh[13].is_major_topic | True |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Deep Learning |
| mesh[14].qualifier_ui | Q000032 |
| mesh[14].descriptor_ui | D001786 |
| mesh[14].is_major_topic | True |
| mesh[14].qualifier_name | analysis |
| mesh[14].descriptor_name | Blood Glucose |
| mesh[15].qualifier_ui | Q000379 |
| mesh[15].descriptor_ui | D015190 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | methods |
| mesh[15].descriptor_name | Blood Glucose Self-Monitoring |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D016571 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Neural Networks, Computer |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D008875 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Middle Aged |
| mesh[18].qualifier_ui | |
| mesh[18].descriptor_ui | D008297 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | |
| mesh[18].descriptor_name | Male |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D005260 |
| mesh[19].is_major_topic | False |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Female |
| mesh[20].qualifier_ui | Q000097 |
| mesh[20].descriptor_ui | D003924 |
| mesh[20].is_major_topic | True |
| mesh[20].qualifier_name | blood |
| mesh[20].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[21].qualifier_ui | Q000628 |
| mesh[21].descriptor_ui | D003924 |
| mesh[21].is_major_topic | True |
| mesh[21].qualifier_name | therapy |
| mesh[21].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[22].qualifier_ui | |
| mesh[22].descriptor_ui | D006801 |
| mesh[22].is_major_topic | False |
| mesh[22].qualifier_name | |
| mesh[22].descriptor_name | Humans |
| mesh[23].qualifier_ui | |
| mesh[23].descriptor_ui | D000077321 |
| mesh[23].is_major_topic | True |
| mesh[23].qualifier_name | |
| mesh[23].descriptor_name | Deep Learning |
| mesh[24].qualifier_ui | Q000032 |
| mesh[24].descriptor_ui | D001786 |
| mesh[24].is_major_topic | True |
| mesh[24].qualifier_name | analysis |
| mesh[24].descriptor_name | Blood Glucose |
| mesh[25].qualifier_ui | Q000379 |
| mesh[25].descriptor_ui | D015190 |
| mesh[25].is_major_topic | False |
| mesh[25].qualifier_name | methods |
| mesh[25].descriptor_name | Blood Glucose Self-Monitoring |
| mesh[26].qualifier_ui | |
| mesh[26].descriptor_ui | D016571 |
| mesh[26].is_major_topic | False |
| mesh[26].qualifier_name | |
| mesh[26].descriptor_name | Neural Networks, Computer |
| mesh[27].qualifier_ui | |
| mesh[27].descriptor_ui | D008875 |
| mesh[27].is_major_topic | False |
| mesh[27].qualifier_name | |
| mesh[27].descriptor_name | Middle Aged |
| mesh[28].qualifier_ui | |
| mesh[28].descriptor_ui | D008297 |
| mesh[28].is_major_topic | False |
| mesh[28].qualifier_name | |
| mesh[28].descriptor_name | Male |
| mesh[29].qualifier_ui | |
| mesh[29].descriptor_ui | D005260 |
| mesh[29].is_major_topic | False |
| mesh[29].qualifier_name | |
| mesh[29].descriptor_name | Female |
| mesh[30].qualifier_ui | Q000097 |
| mesh[30].descriptor_ui | D003924 |
| mesh[30].is_major_topic | True |
| mesh[30].qualifier_name | blood |
| mesh[30].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[31].qualifier_ui | Q000628 |
| mesh[31].descriptor_ui | D003924 |
| mesh[31].is_major_topic | True |
| mesh[31].qualifier_name | therapy |
| mesh[31].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[32].qualifier_ui | |
| mesh[32].descriptor_ui | D006801 |
| mesh[32].is_major_topic | False |
| mesh[32].qualifier_name | |
| mesh[32].descriptor_name | Humans |
| mesh[33].qualifier_ui | |
| mesh[33].descriptor_ui | D000077321 |
| mesh[33].is_major_topic | True |
| mesh[33].qualifier_name | |
| mesh[33].descriptor_name | Deep Learning |
| mesh[34].qualifier_ui | Q000032 |
| mesh[34].descriptor_ui | D001786 |
| mesh[34].is_major_topic | True |
| mesh[34].qualifier_name | analysis |
| mesh[34].descriptor_name | Blood Glucose |
| mesh[35].qualifier_ui | Q000379 |
| mesh[35].descriptor_ui | D015190 |
| mesh[35].is_major_topic | False |
| mesh[35].qualifier_name | methods |
| mesh[35].descriptor_name | Blood Glucose Self-Monitoring |
| mesh[36].qualifier_ui | |
| mesh[36].descriptor_ui | D016571 |
| mesh[36].is_major_topic | False |
| mesh[36].qualifier_name | |
| mesh[36].descriptor_name | Neural Networks, Computer |
| mesh[37].qualifier_ui | |
| mesh[37].descriptor_ui | D008875 |
| mesh[37].is_major_topic | False |
| mesh[37].qualifier_name | |
| mesh[37].descriptor_name | Middle Aged |
| mesh[38].qualifier_ui | |
| mesh[38].descriptor_ui | D008297 |
| mesh[38].is_major_topic | False |
| mesh[38].qualifier_name | |
| mesh[38].descriptor_name | Male |
| mesh[39].qualifier_ui | |
| mesh[39].descriptor_ui | D005260 |
| mesh[39].is_major_topic | False |
| mesh[39].qualifier_name | |
| mesh[39].descriptor_name | Female |
| mesh[40].qualifier_ui | Q000097 |
| mesh[40].descriptor_ui | D003924 |
| mesh[40].is_major_topic | True |
| mesh[40].qualifier_name | blood |
| mesh[40].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[41].qualifier_ui | Q000628 |
| mesh[41].descriptor_ui | D003924 |
| mesh[41].is_major_topic | True |
| mesh[41].qualifier_name | therapy |
| mesh[41].descriptor_name | Diabetes Mellitus, Type 2 |
| mesh[42].qualifier_ui | |
| mesh[42].descriptor_ui | D006801 |
| mesh[42].is_major_topic | False |
| mesh[42].qualifier_name | |
| mesh[42].descriptor_name | Humans |
| mesh[43].qualifier_ui | |
| mesh[43].descriptor_ui | D000077321 |
| mesh[43].is_major_topic | True |
| mesh[43].qualifier_name | |
| mesh[43].descriptor_name | Deep Learning |
| mesh[44].qualifier_ui | Q000032 |
| mesh[44].descriptor_ui | D001786 |
| mesh[44].is_major_topic | True |
| mesh[44].qualifier_name | analysis |
| mesh[44].descriptor_name | Blood Glucose |
| mesh[45].qualifier_ui | Q000379 |
| mesh[45].descriptor_ui | D015190 |
| mesh[45].is_major_topic | False |
| mesh[45].qualifier_name | methods |
| mesh[45].descriptor_name | Blood Glucose Self-Monitoring |
| mesh[46].qualifier_ui | |
| mesh[46].descriptor_ui | D016571 |
| mesh[46].is_major_topic | False |
| mesh[46].qualifier_name | |
| mesh[46].descriptor_name | Neural Networks, Computer |
| mesh[47].qualifier_ui | |
| mesh[47].descriptor_ui | D008875 |
| mesh[47].is_major_topic | False |
| mesh[47].qualifier_name | |
| mesh[47].descriptor_name | Middle Aged |
| mesh[48].qualifier_ui | |
| mesh[48].descriptor_ui | D008297 |
| mesh[48].is_major_topic | False |
| mesh[48].qualifier_name | |
| mesh[48].descriptor_name | Male |
| mesh[49].qualifier_ui | |
| mesh[49].descriptor_ui | D005260 |
| mesh[49].is_major_topic | False |
| mesh[49].qualifier_name | |
| mesh[49].descriptor_name | Female |
| type | article |
| title | A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management |
| biblio.issue | 1 |
| biblio.volume | 15 |
| biblio.last_page | 27625 |
| biblio.first_page | 27625 |
| topics[0].id | https://openalex.org/T10560 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9994000196456909 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2712 |
| topics[0].subfield.display_name | Endocrinology, Diabetes and Metabolism |
| topics[0].display_name | Diabetes Management and Research |
| topics[1].id | https://openalex.org/T10745 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9850000143051147 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2705 |
| topics[1].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[1].display_name | Heart Rate Variability and Autonomic Control |
| topics[2].id | https://openalex.org/T10836 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9740999937057495 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | Metabolomics and Mass Spectrometry Studies |
| is_xpac | False |
| apc_list.value | 1890 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2190 |
| apc_paid.value | 1890 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2190 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.5716811418533325 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C2777180221 |
| concepts[1].level | 3 |
| concepts[1].score | 0.5504079461097717 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3025883 |
| concepts[1].display_name | Type 2 diabetes |
| concepts[2].id | https://openalex.org/C123657996 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5319491028785706 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q12271 |
| concepts[2].display_name | Architecture |
| concepts[3].id | https://openalex.org/C108583219 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5168197751045227 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[3].display_name | Deep learning |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.442534863948822 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C555293320 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4337678551673889 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q12206 |
| concepts[5].display_name | Diabetes mellitus |
| concepts[6].id | https://openalex.org/C2522767166 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3536919355392456 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[6].display_name | Data science |
| concepts[7].id | https://openalex.org/C60644358 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3307563364505768 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q128570 |
| concepts[7].display_name | Bioinformatics |
| concepts[8].id | https://openalex.org/C71924100 |
| concepts[8].level | 0 |
| concepts[8].score | 0.26316773891448975 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[8].display_name | Medicine |
| concepts[9].id | https://openalex.org/C134018914 |
| concepts[9].level | 1 |
| concepts[9].score | 0.1519630253314972 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q162606 |
| concepts[9].display_name | Endocrinology |
| concepts[10].id | https://openalex.org/C86803240 |
| concepts[10].level | 0 |
| concepts[10].score | 0.12634167075157166 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[10].display_name | Biology |
| concepts[11].id | https://openalex.org/C205649164 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0642659068107605 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[11].display_name | Geography |
| concepts[12].id | https://openalex.org/C166957645 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q23498 |
| concepts[12].display_name | Archaeology |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.5716811418533325 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/type-2-diabetes |
| keywords[1].score | 0.5504079461097717 |
| keywords[1].display_name | Type 2 diabetes |
| keywords[2].id | https://openalex.org/keywords/architecture |
| keywords[2].score | 0.5319491028785706 |
| keywords[2].display_name | Architecture |
| keywords[3].id | https://openalex.org/keywords/deep-learning |
| keywords[3].score | 0.5168197751045227 |
| keywords[3].display_name | Deep learning |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.442534863948822 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/diabetes-mellitus |
| keywords[5].score | 0.4337678551673889 |
| keywords[5].display_name | Diabetes mellitus |
| keywords[6].id | https://openalex.org/keywords/data-science |
| keywords[6].score | 0.3536919355392456 |
| keywords[6].display_name | Data science |
| keywords[7].id | https://openalex.org/keywords/bioinformatics |
| keywords[7].score | 0.3307563364505768 |
| keywords[7].display_name | Bioinformatics |
| keywords[8].id | https://openalex.org/keywords/medicine |
| keywords[8].score | 0.26316773891448975 |
| keywords[8].display_name | Medicine |
| keywords[9].id | https://openalex.org/keywords/endocrinology |
| keywords[9].score | 0.1519630253314972 |
| keywords[9].display_name | Endocrinology |
| keywords[10].id | https://openalex.org/keywords/biology |
| keywords[10].score | 0.12634167075157166 |
| keywords[10].display_name | Biology |
| keywords[11].id | https://openalex.org/keywords/geography |
| keywords[11].score | 0.0642659068107605 |
| keywords[11].display_name | Geography |
| language | en |
| locations[0].id | doi:10.1038/s41598-025-07272-3 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S196734849 |
| locations[0].source.issn | 2045-2322 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2045-2322 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Scientific Reports |
| locations[0].source.host_organization | https://openalex.org/P4310319908 |
| locations[0].source.host_organization_name | Nature Portfolio |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Scientific Reports |
| locations[0].landing_page_url | https://doi.org/10.1038/s41598-025-07272-3 |
| locations[1].id | pmid:40730832 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Scientific reports |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40730832 |
| locations[2].id | pmh:oai:doaj.org/article:4dc320c173be4ddda7f5cde1663a1e1c |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Scientific Reports, Vol 15, Iss 1, Pp 1-16 (2025) |
| locations[2].landing_page_url | https://doaj.org/article/4dc320c173be4ddda7f5cde1663a1e1c |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:12307872 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sci Rep |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12307872 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5054889019 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-5946-6567 |
| authorships[0].author.display_name | Muhammad Salman Haleem |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I39555362 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Engineering, University of Warwick, Coventry, CV4 7AL, UK |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I166337079 |
| authorships[0].affiliations[1].raw_affiliation_string | School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK |
| authorships[0].institutions[0].id | https://openalex.org/I166337079 |
| authorships[0].institutions[0].ror | https://ror.org/026zzn846 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I124357947, https://openalex.org/I166337079 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | Queen Mary University of London |
| authorships[0].institutions[1].id | https://openalex.org/I39555362 |
| authorships[0].institutions[1].ror | https://ror.org/01a77tt86 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I39555362 |
| authorships[0].institutions[1].country_code | GB |
| authorships[0].institutions[1].display_name | University of Warwick |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Muhammad Salman Haleem |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK, School of Engineering, University of Warwick, Coventry, CV4 7AL, UK |
| authorships[1].author.id | https://openalex.org/A5025156113 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Daphne N. Katsarou |
| authorships[1].countries | GR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I194019607 |
| authorships[1].affiliations[0].raw_affiliation_string | Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece |
| authorships[1].institutions[0].id | https://openalex.org/I194019607 |
| authorships[1].institutions[0].ror | https://ror.org/01qg3j183 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I194019607 |
| authorships[1].institutions[0].country_code | GR |
| authorships[1].institutions[0].display_name | University of Ioannina |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Daphne Katsarou |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece |
| authorships[2].author.id | https://openalex.org/A5026778314 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3607-0727 |
| authorships[2].author.display_name | Eleni I. Georga |
| authorships[2].countries | GR |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I194019607 |
| authorships[2].affiliations[0].raw_affiliation_string | Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece |
| authorships[2].institutions[0].id | https://openalex.org/I194019607 |
| authorships[2].institutions[0].ror | https://ror.org/01qg3j183 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I194019607 |
| authorships[2].institutions[0].country_code | GR |
| authorships[2].institutions[0].display_name | University of Ioannina |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Eleni I. Georga |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece |
| authorships[3].author.id | https://openalex.org/A5052020242 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-3060-8888 |
| authorships[3].author.display_name | George Dafoulas |
| authorships[3].countries | GR |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I145722265 |
| authorships[3].affiliations[0].raw_affiliation_string | Faculty of Medicine, University of Thessaly, Volos, Greece |
| authorships[3].institutions[0].id | https://openalex.org/I145722265 |
| authorships[3].institutions[0].ror | https://ror.org/04v4g9h31 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I145722265 |
| authorships[3].institutions[0].country_code | GR |
| authorships[3].institutions[0].display_name | University of Thessaly |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | George E. Dafoulas |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Faculty of Medicine, University of Thessaly, Volos, Greece |
| authorships[4].author.id | https://openalex.org/A5015534459 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-2694-5929 |
| authorships[4].author.display_name | Αlexandra Bargiota |
| authorships[4].countries | GR |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210107217 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Endocrinology and Metabolic Diseases, University Hospital of Larisa, Larissa, Greece |
| authorships[4].institutions[0].id | https://openalex.org/I4210107217 |
| authorships[4].institutions[0].ror | https://ror.org/01s5dt366 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210107217 |
| authorships[4].institutions[0].country_code | GR |
| authorships[4].institutions[0].display_name | University Hospital of Larissa |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Alexandra Bargiota |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Endocrinology and Metabolic Diseases, University Hospital of Larisa, Larissa, Greece |
| authorships[5].author.id | https://openalex.org/A5088935367 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-1536-1090 |
| authorships[5].author.display_name | Laura Lopez-Perez |
| authorships[5].countries | ES |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I88060688 |
| authorships[5].affiliations[0].raw_affiliation_string | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[5].institutions[0].id | https://openalex.org/I88060688 |
| authorships[5].institutions[0].ror | https://ror.org/03n6nwv02 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I88060688 |
| authorships[5].institutions[0].country_code | ES |
| authorships[5].institutions[0].display_name | Universidad Politécnica de Madrid |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Laura Lopez-Perez |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[6].author.id | https://openalex.org/A5092438672 |
| authorships[6].author.orcid | https://orcid.org/0009-0005-8198-3356 |
| authorships[6].author.display_name | Miguel Rujas |
| authorships[6].countries | ES |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I88060688 |
| authorships[6].affiliations[0].raw_affiliation_string | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[6].institutions[0].id | https://openalex.org/I88060688 |
| authorships[6].institutions[0].ror | https://ror.org/03n6nwv02 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I88060688 |
| authorships[6].institutions[0].country_code | ES |
| authorships[6].institutions[0].display_name | Universidad Politécnica de Madrid |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Miguel Rujas |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[7].author.id | https://openalex.org/A5034633748 |
| authorships[7].author.orcid | https://orcid.org/0000-0003-1551-4613 |
| authorships[7].author.display_name | Giuseppe Fico |
| authorships[7].countries | ES |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I88060688 |
| authorships[7].affiliations[0].raw_affiliation_string | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[7].institutions[0].id | https://openalex.org/I88060688 |
| authorships[7].institutions[0].ror | https://ror.org/03n6nwv02 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I88060688 |
| authorships[7].institutions[0].country_code | ES |
| authorships[7].institutions[0].display_name | Universidad Politécnica de Madrid |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Giuseppe Fico |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[8].author.id | https://openalex.org/A5021472133 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-7900-5415 |
| authorships[8].author.display_name | Leandro Pecchia |
| authorships[8].countries | IT |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I155125353 |
| authorships[8].affiliations[0].raw_affiliation_string | Università Campus Bio-Medico, Via Álvaro del Portillo, 21, 00128, Roma, Italy |
| authorships[8].institutions[0].id | https://openalex.org/I155125353 |
| authorships[8].institutions[0].ror | https://ror.org/04gqx4x78 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I155125353 |
| authorships[8].institutions[0].country_code | IT |
| authorships[8].institutions[0].display_name | Università Campus Bio-Medico |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Leandro Pecchia |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Università Campus Bio-Medico, Via Álvaro del Portillo, 21, 00128, Roma, Italy |
| authorships[9].author.id | https://openalex.org/A5057894101 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-5987-9350 |
| authorships[9].author.display_name | Dimitrios I. Fotiadis |
| authorships[9].countries | GR |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I194019607 |
| authorships[9].affiliations[0].raw_affiliation_string | Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece |
| authorships[9].institutions[0].id | https://openalex.org/I194019607 |
| authorships[9].institutions[0].ror | https://ror.org/01qg3j183 |
| authorships[9].institutions[0].type | education |
| authorships[9].institutions[0].lineage | https://openalex.org/I194019607 |
| authorships[9].institutions[0].country_code | GR |
| authorships[9].institutions[0].display_name | University of Ioannina |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Dimitrios Fotiadis |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece |
| authorships[10].author.id | https://openalex.org/A5075366286 |
| authorships[10].author.orcid | |
| authorships[10].author.display_name | Claudio Caimi |
| authorships[10].countries | IT |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I4210118406 |
| authorships[10].affiliations[0].raw_affiliation_string | Hewlett-Packard Italiana, Milan, Italy |
| authorships[10].institutions[0].id | https://openalex.org/I4210118406 |
| authorships[10].institutions[0].ror | https://ror.org/01r4g9k61 |
| authorships[10].institutions[0].type | company |
| authorships[10].institutions[0].lineage | https://openalex.org/I1324840837, https://openalex.org/I4210118406 |
| authorships[10].institutions[0].country_code | IT |
| authorships[10].institutions[0].display_name | Hewlett-Packard (Italy) |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Claudio Caimi |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | Hewlett-Packard Italiana, Milan, Italy |
| authorships[11].author.id | https://openalex.org/A5119360869 |
| authorships[11].author.orcid | |
| authorships[11].author.display_name | Christian Tamporale |
| authorships[11].countries | IT |
| authorships[11].affiliations[0].institution_ids | https://openalex.org/I4210118406 |
| authorships[11].affiliations[0].raw_affiliation_string | Hewlett-Packard Italiana, Milan, Italy |
| authorships[11].institutions[0].id | https://openalex.org/I4210118406 |
| authorships[11].institutions[0].ror | https://ror.org/01r4g9k61 |
| authorships[11].institutions[0].type | company |
| authorships[11].institutions[0].lineage | https://openalex.org/I1324840837, https://openalex.org/I4210118406 |
| authorships[11].institutions[0].country_code | IT |
| authorships[11].institutions[0].display_name | Hewlett-Packard (Italy) |
| authorships[11].author_position | middle |
| authorships[11].raw_author_name | Christian Tamporale |
| authorships[11].is_corresponding | False |
| authorships[11].raw_affiliation_strings | Hewlett-Packard Italiana, Milan, Italy |
| authorships[12].author.id | https://openalex.org/A5070466200 |
| authorships[12].author.orcid | |
| authorships[12].author.display_name | Mirko Manea |
| authorships[12].countries | IT |
| authorships[12].affiliations[0].institution_ids | https://openalex.org/I4210118406 |
| authorships[12].affiliations[0].raw_affiliation_string | Hewlett-Packard Italiana, Milan, Italy |
| authorships[12].institutions[0].id | https://openalex.org/I4210118406 |
| authorships[12].institutions[0].ror | https://ror.org/01r4g9k61 |
| authorships[12].institutions[0].type | company |
| authorships[12].institutions[0].lineage | https://openalex.org/I1324840837, https://openalex.org/I4210118406 |
| authorships[12].institutions[0].country_code | IT |
| authorships[12].institutions[0].display_name | Hewlett-Packard (Italy) |
| authorships[12].author_position | middle |
| authorships[12].raw_author_name | Mirko Manea |
| authorships[12].is_corresponding | False |
| authorships[12].raw_affiliation_strings | Hewlett-Packard Italiana, Milan, Italy |
| authorships[13].author.id | https://openalex.org/A5119360870 |
| authorships[13].author.orcid | |
| authorships[13].author.display_name | Chiara Bonferini |
| authorships[13].countries | IT |
| authorships[13].affiliations[0].institution_ids | https://openalex.org/I4210118406 |
| authorships[13].affiliations[0].raw_affiliation_string | Hewlett-Packard Italiana, Milan, Italy |
| authorships[13].institutions[0].id | https://openalex.org/I4210118406 |
| authorships[13].institutions[0].ror | https://ror.org/01r4g9k61 |
| authorships[13].institutions[0].type | company |
| authorships[13].institutions[0].lineage | https://openalex.org/I1324840837, https://openalex.org/I4210118406 |
| authorships[13].institutions[0].country_code | IT |
| authorships[13].institutions[0].display_name | Hewlett-Packard (Italy) |
| authorships[13].author_position | middle |
| authorships[13].raw_author_name | Chiara Bonferini |
| authorships[13].is_corresponding | False |
| authorships[13].raw_affiliation_strings | Hewlett-Packard Italiana, Milan, Italy |
| authorships[14].author.id | https://openalex.org/A5088507117 |
| authorships[14].author.orcid | https://orcid.org/0000-0001-9500-9283 |
| authorships[14].author.display_name | Eugenio Gaeta |
| authorships[14].countries | ES |
| authorships[14].affiliations[0].institution_ids | https://openalex.org/I88060688 |
| authorships[14].affiliations[0].raw_affiliation_string | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[14].institutions[0].id | https://openalex.org/I88060688 |
| authorships[14].institutions[0].ror | https://ror.org/03n6nwv02 |
| authorships[14].institutions[0].type | education |
| authorships[14].institutions[0].lineage | https://openalex.org/I88060688 |
| authorships[14].institutions[0].country_code | ES |
| authorships[14].institutions[0].display_name | Universidad Politécnica de Madrid |
| authorships[14].author_position | middle |
| authorships[14].raw_author_name | Eugenio Gaeta |
| authorships[14].is_corresponding | False |
| authorships[14].raw_affiliation_strings | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[15].author.id | https://openalex.org/A5020518280 |
| authorships[15].author.orcid | https://orcid.org/0000-0002-6947-4672 |
| authorships[15].author.display_name | Gloria Cea |
| authorships[15].countries | ES |
| authorships[15].affiliations[0].institution_ids | https://openalex.org/I88060688 |
| authorships[15].affiliations[0].raw_affiliation_string | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[15].institutions[0].id | https://openalex.org/I88060688 |
| authorships[15].institutions[0].ror | https://ror.org/03n6nwv02 |
| authorships[15].institutions[0].type | education |
| authorships[15].institutions[0].lineage | https://openalex.org/I88060688 |
| authorships[15].institutions[0].country_code | ES |
| authorships[15].institutions[0].display_name | Universidad Politécnica de Madrid |
| authorships[15].author_position | middle |
| authorships[15].raw_author_name | Gloria Cea Sánchez |
| authorships[15].is_corresponding | False |
| authorships[15].raw_affiliation_strings | Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain |
| authorships[16].author.id | https://openalex.org/A5093400173 |
| authorships[16].author.orcid | |
| authorships[16].author.display_name | Ioanna Drympeta |
| authorships[16].countries | GR |
| authorships[16].affiliations[0].institution_ids | https://openalex.org/I4210093649, https://openalex.org/I4210134249 |
| authorships[16].affiliations[0].raw_affiliation_string | Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece |
| authorships[16].institutions[0].id | https://openalex.org/I4210134249 |
| authorships[16].institutions[0].ror | https://ror.org/03bndpq63 |
| authorships[16].institutions[0].type | facility |
| authorships[16].institutions[0].lineage | https://openalex.org/I4210134249 |
| authorships[16].institutions[0].country_code | GR |
| authorships[16].institutions[0].display_name | Centre for Research and Technology Hellas |
| authorships[16].institutions[1].id | https://openalex.org/I4210093649 |
| authorships[16].institutions[1].ror | https://ror.org/0069akp70 |
| authorships[16].institutions[1].type | nonprofit |
| authorships[16].institutions[1].lineage | https://openalex.org/I4210093649 |
| authorships[16].institutions[1].country_code | GR |
| authorships[16].institutions[1].display_name | Information Technologies Institute |
| authorships[16].author_position | middle |
| authorships[16].raw_author_name | Ioanna Drympeta |
| authorships[16].is_corresponding | False |
| authorships[16].raw_affiliation_strings | Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece |
| authorships[17].author.id | https://openalex.org/A5083028256 |
| authorships[17].author.orcid | |
| authorships[17].author.display_name | Konstantinos Votis |
| authorships[17].countries | GR |
| authorships[17].affiliations[0].institution_ids | https://openalex.org/I4210093649, https://openalex.org/I4210134249 |
| authorships[17].affiliations[0].raw_affiliation_string | Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece |
| authorships[17].institutions[0].id | https://openalex.org/I4210134249 |
| authorships[17].institutions[0].ror | https://ror.org/03bndpq63 |
| authorships[17].institutions[0].type | facility |
| authorships[17].institutions[0].lineage | https://openalex.org/I4210134249 |
| authorships[17].institutions[0].country_code | GR |
| authorships[17].institutions[0].display_name | Centre for Research and Technology Hellas |
| authorships[17].institutions[1].id | https://openalex.org/I4210093649 |
| authorships[17].institutions[1].ror | https://ror.org/0069akp70 |
| authorships[17].institutions[1].type | nonprofit |
| authorships[17].institutions[1].lineage | https://openalex.org/I4210093649 |
| authorships[17].institutions[1].country_code | GR |
| authorships[17].institutions[1].display_name | Information Technologies Institute |
| authorships[17].author_position | middle |
| authorships[17].raw_author_name | Konstantinos Votis |
| authorships[17].is_corresponding | False |
| authorships[17].raw_affiliation_strings | Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece |
| authorships[18].author.id | https://openalex.org/A5088772695 |
| authorships[18].author.orcid | https://orcid.org/0000-0001-7602-3792 |
| authorships[18].author.display_name | Frans Folkvord |
| authorships[18].affiliations[0].raw_affiliation_string | Tilburg School of Humanities and Digital Sciences, Tilburg, The Netherlands |
| authorships[18].author_position | middle |
| authorships[18].raw_author_name | Frans Folkvord |
| authorships[18].is_corresponding | False |
| authorships[18].raw_affiliation_strings | Tilburg School of Humanities and Digital Sciences, Tilburg, The Netherlands |
| authorships[19].author.id | https://openalex.org/A5030245271 |
| authorships[19].author.orcid | |
| authorships[19].author.display_name | J. Battle |
| authorships[19].countries | ES |
| authorships[19].affiliations[0].institution_ids | https://openalex.org/I4210099160 |
| authorships[19].affiliations[0].raw_affiliation_string | Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain |
| authorships[19].affiliations[1].institution_ids | https://openalex.org/I2802050926 |
| authorships[19].affiliations[1].raw_affiliation_string | Hospital Universitari Arnau de Vilanova and Santa Maria, Lleida, Spain |
| authorships[19].institutions[0].id | https://openalex.org/I4210099160 |
| authorships[19].institutions[0].ror | https://ror.org/0119pby33 |
| authorships[19].institutions[0].type | facility |
| authorships[19].institutions[0].lineage | https://openalex.org/I2801357902, https://openalex.org/I4210099160, https://openalex.org/I4210115082, https://openalex.org/I4387152914 |
| authorships[19].institutions[0].country_code | ES |
| authorships[19].institutions[0].display_name | Centro de Investigación Biomédica en Red de Enfermedades Respiratorias |
| authorships[19].institutions[1].id | https://openalex.org/I2802050926 |
| authorships[19].institutions[1].ror | https://ror.org/01p3tpn79 |
| authorships[19].institutions[1].type | healthcare |
| authorships[19].institutions[1].lineage | https://openalex.org/I157913058, https://openalex.org/I2802050926, https://openalex.org/I4210153292 |
| authorships[19].institutions[1].country_code | ES |
| authorships[19].institutions[1].display_name | Hospital Universitari Arnau de Vilanova |
| authorships[19].author_position | last |
| authorships[19].raw_author_name | Jordi de Battle |
| authorships[19].is_corresponding | False |
| authorships[19].raw_affiliation_strings | Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain, Hospital Universitari Arnau de Vilanova and Santa Maria, Lleida, Spain |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1038/s41598-025-07272-3 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10560 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9994000196456909 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2712 |
| primary_topic.subfield.display_name | Endocrinology, Diabetes and Metabolism |
| primary_topic.display_name | Diabetes Management and Research |
| related_works | https://openalex.org/W4375867731, https://openalex.org/W2611989081, https://openalex.org/W4390515809, https://openalex.org/W2348573424, https://openalex.org/W2731899572, https://openalex.org/W2003316325, https://openalex.org/W2982442175, https://openalex.org/W3080453316, https://openalex.org/W1992007649, https://openalex.org/W4230611425 |
| cited_by_count | 0 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1038/s41598-025-07272-3 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S196734849 |
| best_oa_location.source.issn | 2045-2322 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2045-2322 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Scientific Reports |
| best_oa_location.source.host_organization | https://openalex.org/P4310319908 |
| best_oa_location.source.host_organization_name | Nature Portfolio |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Scientific Reports |
| best_oa_location.landing_page_url | https://doi.org/10.1038/s41598-025-07272-3 |
| primary_location.id | doi:10.1038/s41598-025-07272-3 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S196734849 |
| primary_location.source.issn | 2045-2322 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2045-2322 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Scientific Reports |
| primary_location.source.host_organization | https://openalex.org/P4310319908 |
| primary_location.source.host_organization_name | Nature Portfolio |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Scientific Reports |
| primary_location.landing_page_url | https://doi.org/10.1038/s41598-025-07272-3 |
| publication_date | 2025-07-29 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2460530547, https://openalex.org/W3081655479, https://openalex.org/W4200026682, https://openalex.org/W4388111256, https://openalex.org/W3173483806, https://openalex.org/W2295534702, https://openalex.org/W3155016301, https://openalex.org/W2039183257, https://openalex.org/W1599923190, https://openalex.org/W2893810824, https://openalex.org/W2150092204, https://openalex.org/W4317787019, https://openalex.org/W4366335632, https://openalex.org/W2587847154, https://openalex.org/W3175738155, https://openalex.org/W4387585322, https://openalex.org/W4238022947, https://openalex.org/W2297152540, https://openalex.org/W2183659962, https://openalex.org/W2054705093, https://openalex.org/W3089652905, https://openalex.org/W4382344450, https://openalex.org/W2923829929, https://openalex.org/W3167277098, https://openalex.org/W2993720025, https://openalex.org/W4401544965, https://openalex.org/W4283785752, https://openalex.org/W3183804512, https://openalex.org/W3178109510, https://openalex.org/W4403022233, https://openalex.org/W4407080057, https://openalex.org/W3157562741, https://openalex.org/W4407173084, https://openalex.org/W2565017937, https://openalex.org/W4387721637, https://openalex.org/W4225740406, https://openalex.org/W3199420312 |
| referenced_works_count | 37 |
| abstract_inverted_index.2 | 46, 161, 352 |
| abstract_inverted_index.5 | 244 |
| abstract_inverted_index.a | 37, 138, 170, 177, 207, 240, 252, 339 |
| abstract_inverted_index.14 | 280 |
| abstract_inverted_index.15 | 257 |
| abstract_inverted_index.24 | 282 |
| abstract_inverted_index.30 | 236, 260, 307 |
| abstract_inverted_index.40 | 157 |
| abstract_inverted_index.60 | 264, 309 |
| abstract_inverted_index.In | 133 |
| abstract_inverted_index.To | 227 |
| abstract_inverted_index.We | 266 |
| abstract_inverted_index.an | 186 |
| abstract_inverted_index.as | 338 |
| abstract_inverted_index.be | 30 |
| abstract_inverted_index.by | 54, 185 |
| abstract_inverted_index.in | 40, 49, 60, 68, 119, 288, 300, 349 |
| abstract_inverted_index.is | 7, 122 |
| abstract_inverted_index.of | 4, 13, 24, 44, 65, 72, 108, 129, 144, 156, 210, 235, 243, 255, 290, 302 |
| abstract_inverted_index.on | 77, 142 |
| abstract_inverted_index.to | 82, 246, 327 |
| abstract_inverted_index.we | 136, 231 |
| abstract_inverted_index.(a) | 256 |
| abstract_inverted_index.(b) | 259 |
| abstract_inverted_index.(c) | 263 |
| abstract_inverted_index.15, | 306 |
| abstract_inverted_index.CGM | 145, 164, 224, 233, 249 |
| abstract_inverted_index.CNN | 197 |
| abstract_inverted_index.One | 107 |
| abstract_inverted_index.The | 1, 163, 189, 314 |
| abstract_inverted_index.and | 80, 147, 176, 218, 262, 281, 293, 308, 346 |
| abstract_inverted_index.can | 29 |
| abstract_inverted_index.for | 9, 84, 98, 112, 305, 342 |
| abstract_inverted_index.its | 336 |
| abstract_inverted_index.key | 38 |
| abstract_inverted_index.min | 237, 245, 310 |
| abstract_inverted_index.not | 115 |
| abstract_inverted_index.our | 229 |
| abstract_inverted_index.the | 10, 41, 73, 96, 109, 123, 127, 196, 223, 248, 268, 318, 350 |
| abstract_inverted_index.was | 204, 219 |
| abstract_inverted_index.Long | 179 |
| abstract_inverted_index.Mean | 274 |
| abstract_inverted_index.case | 289, 301 |
| abstract_inverted_index.deep | 61 |
| abstract_inverted_index.fail | 81 |
| abstract_inverted_index.from | 153 |
| abstract_inverted_index.lies | 48 |
| abstract_inverted_index.min, | 258, 261 |
| abstract_inverted_index.min. | 265 |
| abstract_inverted_index.most | 71 |
| abstract_inverted_index.need | 97 |
| abstract_inverted_index.rely | 76 |
| abstract_inverted_index.that | 88, 101, 213, 317 |
| abstract_inverted_index.this | 120, 134 |
| abstract_inverted_index.time | 165 |
| abstract_inverted_index.type | 45, 160, 351 |
| abstract_inverted_index.upto | 331 |
| abstract_inverted_index.were | 167 |
| abstract_inverted_index.with | 126, 149, 159, 222, 239, 251, 273, 330 |
| abstract_inverted_index.(CGM) | 19 |
| abstract_inverted_index.(CNN) | 175 |
| abstract_inverted_index.96.7% | 332 |
| abstract_inverted_index.Abbot | 303 |
| abstract_inverted_index.Error | 277 |
| abstract_inverted_index.Point | 276 |
| abstract_inverted_index.These | 93 |
| abstract_inverted_index.While | 57 |
| abstract_inverted_index.being | 116 |
| abstract_inverted_index.blood | 5, 33 |
| abstract_inverted_index.field | 121 |
| abstract_inverted_index.fused | 221 |
| abstract_inverted_index.later | 220 |
| abstract_inverted_index.local | 199 |
| abstract_inverted_index.mg/dL | 287, 299 |
| abstract_inverted_index.model | 321 |
| abstract_inverted_index.using | 169 |
| abstract_inverted_index.which | 28 |
| abstract_inverted_index.while | 195 |
| abstract_inverted_index.(MAPE) | 278 |
| abstract_inverted_index.6–11 | 294 |
| abstract_inverted_index.9–14 | 296 |
| abstract_inverted_index.BiLSTM | 190 |
| abstract_inverted_index.Memory | 181 |
| abstract_inverted_index.Modern | 15 |
| abstract_inverted_index.Neural | 173 |
| abstract_inverted_index.driven | 53 |
| abstract_inverted_index.enable | 63 |
| abstract_inverted_index.events | 52 |
| abstract_inverted_index.health | 131, 154, 216 |
| abstract_inverted_index.higher | 323 |
| abstract_inverted_index.inputs | 79 |
| abstract_inverted_index.mg/dL, | 283, 285, 295, 297 |
| abstract_inverted_index.model, | 230 |
| abstract_inverted_index.moving | 241 |
| abstract_inverted_index.neural | 211 |
| abstract_inverted_index.paper, | 135 |
| abstract_inverted_index.recent | 58 |
| abstract_inverted_index.sensor | 292, 304 |
| abstract_inverted_index.series | 166 |
| abstract_inverted_index.values | 146, 234, 250 |
| abstract_inverted_index.widely | 117 |
| abstract_inverted_index.window | 242 |
| abstract_inverted_index.12–18 | 298 |
| abstract_inverted_index.19–22 | 284 |
| abstract_inverted_index.25–26 | 286 |
| abstract_inverted_index.Network | 174 |
| abstract_inverted_index.account | 83 |
| abstract_inverted_index.against | 32 |
| abstract_inverted_index.between | 279 |
| abstract_inverted_index.context | 151 |
| abstract_inverted_index.derived | 152 |
| abstract_inverted_index.enables | 21 |
| abstract_inverted_index.glucose | 6, 17, 26, 34, 55, 69, 91, 344 |
| abstract_inverted_index.horizon | 254, 312 |
| abstract_inverted_index.learned | 191 |
| abstract_inverted_index.methods | 75 |
| abstract_inverted_index.network | 183 |
| abstract_inverted_index.predict | 247 |
| abstract_inverted_index.primary | 110 |
| abstract_inverted_index.propose | 137 |
| abstract_inverted_index.reasons | 111 |
| abstract_inverted_index.records | 155, 217 |
| abstract_inverted_index.results | 272, 315 |
| abstract_inverted_index.sampled | 238 |
| abstract_inverted_index.stacked | 171 |
| abstract_inverted_index.stream. | 226 |
| abstract_inverted_index.studied | 118 |
| abstract_inverted_index.through | 206 |
| abstract_inverted_index.trained | 141 |
| abstract_inverted_index.(BiLSTM) | 182 |
| abstract_inverted_index.Absolute | 275 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.Menarini | 291 |
| abstract_inverted_index.accuracy | 325 |
| abstract_inverted_index.accurate | 2 |
| abstract_inverted_index.achieved | 267, 322 |
| abstract_inverted_index.advances | 59 |
| abstract_inverted_index.approach | 140 |
| abstract_inverted_index.baseline | 215 |
| abstract_inverted_index.captured | 198 |
| abstract_inverted_index.compared | 326 |
| abstract_inverted_index.critical | 8, 51 |
| abstract_inverted_index.diabetes | 47, 353 |
| abstract_inverted_index.enriched | 148 |
| abstract_inverted_index.existing | 74 |
| abstract_inverted_index.followed | 184 |
| abstract_inverted_index.learning | 62 |
| abstract_inverted_index.modeling | 64, 225 |
| abstract_inverted_index.networks | 212 |
| abstract_inverted_index.patterns | 67 |
| abstract_inverted_index.pipeline | 209 |
| abstract_inverted_index.proposed | 319 |
| abstract_inverted_index.records. | 132 |
| abstract_inverted_index.separate | 208 |
| abstract_inverted_index.solution | 341 |
| abstract_inverted_index.temporal | 66, 193 |
| abstract_inverted_index.unimodal | 78, 328 |
| abstract_inverted_index.utilized | 232 |
| abstract_inverted_index.validate | 228 |
| abstract_inverted_index.accuracy; | 334 |
| abstract_inverted_index.attention | 187 |
| abstract_inverted_index.challenge | 39 |
| abstract_inverted_index.diabetes. | 14, 162 |
| abstract_inverted_index.dynamics. | 92 |
| abstract_inverted_index.effective | 11, 42 |
| abstract_inverted_index.features. | 201 |
| abstract_inverted_index.highlight | 95 |
| abstract_inverted_index.influence | 89 |
| abstract_inverted_index.integrate | 102 |
| abstract_inverted_index.long-term | 192 |
| abstract_inverted_index.potential | 337 |
| abstract_inverted_index.processed | 168, 214 |
| abstract_inverted_index.real-time | 22 |
| abstract_inverted_index.sequences | 143 |
| abstract_inverted_index.suggested | 316 |
| abstract_inverted_index.Short-Term | 180 |
| abstract_inverted_index.additional | 103 |
| abstract_inverted_index.approaches | 100, 114 |
| abstract_inverted_index.associated | 125 |
| abstract_inverted_index.bottleneck | 124 |
| abstract_inverted_index.calibrated | 31 |
| abstract_inverted_index.continuous | 16 |
| abstract_inverted_index.individual | 85 |
| abstract_inverted_index.management | 12, 43, 348 |
| abstract_inverted_index.mechanism. | 188 |
| abstract_inverted_index.monitoring | 18 |
| abstract_inverted_index.multimodal | 99, 113, 139, 269, 320 |
| abstract_inverted_index.prediction | 3, 253, 271, 311, 324, 333, 345 |
| abstract_inverted_index.sequential | 200 |
| abstract_inverted_index.supporting | 335 |
| abstract_inverted_index.technology | 20 |
| abstract_inverted_index.acquisition | 23 |
| abstract_inverted_index.approaches; | 329 |
| abstract_inverted_index.differences | 87 |
| abstract_inverted_index.forecasting | 50 |
| abstract_inverted_index.individuals | 158 |
| abstract_inverted_index.limitations | 94 |
| abstract_inverted_index.population. | 354 |
| abstract_inverted_index.subjects’ | 130 |
| abstract_inverted_index.architecture | 270 |
| abstract_inverted_index.availability | 128 |
| abstract_inverted_index.incorporated | 205 |
| abstract_inverted_index.information. | 106 |
| abstract_inverted_index.interstitial | 25, 90, 343 |
| abstract_inverted_index.personalized | 104, 347 |
| abstract_inverted_index.variability. | 56 |
| abstract_inverted_index.Bidirectional | 178 |
| abstract_inverted_index.Convolutional | 172 |
| abstract_inverted_index.Physiological | 202 |
| abstract_inverted_index.dependencies, | 194 |
| abstract_inverted_index.fluctuations, | 70 |
| abstract_inverted_index.generalizable | 340 |
| abstract_inverted_index.heterogeneity | 203 |
| abstract_inverted_index.measurements. | 35 |
| abstract_inverted_index.physiological | 86, 105, 150 |
| abstract_inverted_index.respectively. | 313 |
| abstract_inverted_index.concentrations, | 27 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5054889019 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 20 |
| corresponding_institution_ids | https://openalex.org/I166337079, https://openalex.org/I39555362 |
| citation_normalized_percentile.value | 0.41465223 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |