{rfName}

Indexed in

License and use

Icono OpenAccess

Citations

43

Altmetrics

Analysis of institutional authors

Conget IAuthorViñals CAuthorGiménez MAuthor

Share

Publications
>
Article

A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin

Publicated to:Sensors. 22 (4): 1665- - 2022-02-01 22(4), DOI: 10.3390/s22041665

Authors: Parcerisas, Adria; Contreras, Ivan; Delecourt, Alexia; Bertachi, Arthur; Beneyto, Aleix; Conget, Ignacio; Vinals, Clara; Gimenez, Marga; Vehi, Josep

Affiliations

Ctr Invest Biomed Red Diabet & Enfermedad Metab A, Madrid 28029, Spain - Author
Fed Univ Technol Parana UTFPR, Campus Guarapuava, BR-85053525 Guarapuava, Brazil - Author
Hosp Clin Barcelona, Endocrinol & Diabet Unit, Barcelona 08036, Spain - Author
Inst Invest Biomed August Pi & Sunye, Barcelona 08036, Spain - Author
Univ Girona, Inst Informat & Aplicc, Girona 17003, Spain - Author
See more

Abstract

Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives.

Keywords

machine learningmultiple daily injectionsprediction modelsupport vector machinetype 1 diabetesBloodBlood glucoseBlood glucose self-monitoringDecision support systemsDiabetes mellitus, type 1Disease controlForecastingHumansHypoglycaemiaHypoglycaemicHypoglycemiaHypoglycemic agentsInsulinInsulin infusion systemsInsulin therapyMachine learningMachine learning approachesMultiple daily injectionMultiple daily injectionsMultiple-dosePatient treatmentPhysical activityPrediction modelPrediction modellingSupport vector machineSupport vector machinesSupport vectors machineType 1 diabetes

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Sensors due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Analytical Chemistry.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.73. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 8.77 (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-13, the following number of citations:

  • WoS: 17
  • Scopus: 21
  • Europe PMC: 5
  • OpenCitations: 23

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-13:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 55.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 55 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 12.95.
  • The number of mentions on the social network X (formerly Twitter): 5 (Altmetric).
  • The number of mentions in news outlets: 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Brazil.