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Diabetology
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Diabetes Management in the Hospital: Applications of Artificial Intelligence
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Diabetes Management in the Hospital: Applications of Artificial Intelligence

A special issue of Diabetology (ISSN 2673-4540).

Deadline for manuscript submissions: 15 March 2026 | Viewed by 764

Special Issue Editors

Dr. Daniel J. Rubin

E-Mail Website
Guest Editor
Section of Endocrinology, Diabetes, and Metabolism, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA
Interests: inpatient diabetes; prediction and prevention of acute care use by people with diabetes; machine learning and informatics
Dr. Nestoras Mathioudakis

E-Mail Website
Guest Editor
School of Medicine, Johns Hopkins University, Baltimore, MD, USA
Interests: clinical decision support systems; diabetes technology; diabetic foot ulcers; inpatient glucose management; machine learning; patient safety and quality; type 1 diabetes; type 2 diabetes mellitus

Special Issue Information

Dear Colleagues,

As Guest Editors of a Diabetology Special Issue, entitled “Diabetes Management in the Hospital: Applications of Artificial Intelligence”, we invite you to submit an original research article, systematic review, narrative review, or short communication on this topic. In this Special Issue, papers will present research using artificial intelligence/machine learning (AI/ML) approaches to investigate diabetes management within the hospital setting. Research areas may include (but are not limited to) the following:

  • AI-based clinical decision support tools. Studies that evaluate the effectiveness and implementation of AI-driven tools to assist healthcare providers in making informed decisions regarding diabetes management during hospitalization.
  • Predictive Analytics. Research employing AI/ML approaches to predict health outcomes relevant to acute care settings, such as inpatient mortality, hypoglycemia, hyperglycemia, and other complications related to diabetes.
  • Patient Flow and Healthcare utilization. Investigations into how AI/ML can optimize patient flow, reduce healthcare utilization, and decrease readmission rates among patients with diabetes in healthcare settings.
  • Personalized Treatment Plans: Studies focusing on the development of AI algorithms that created personalized treatment plans based on individual patient data, improving glycemic control and overall health outcomes during hospitalization.
  • Integration of Wearable Technology. Research examining how AI can utilize data from wearable devices (CGMs, automated insulin delivery systems) to monitor and manage diabetes in real time, enhancing patient safety and treatment efficacy in acute care environments.
  • Ethical Considerations and Implementation Challenges. Research discussing the ethical implications and challenges associated with implementing AI/ML technologies in diabetes management, including patient privacy, data security, and the need for clinical training.

Diabetology (ISSN 2673-4540) is an international, open access journal. The official deadline for paper submission is 15 September 2025, and the instructions on how to proceed with writing will be available on a dedicated web page.

Dr. Daniel J. Rubin
Dr. Nestoras Mathioudakis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diabetology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • artificial intelligence
  • machine learning
  • diabetes
  • inpatient management
  • clinical decision support
  • risk prediction
  • glucose
  • predictive analytics
  • insulin
  • hospital

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

23 pages, 2049 KB  
Article
Machine Learning for Causal Inference in Hospital Diabetes Care: TMLE Analysis of Selection Bias in Diabetic Foot Infection Treatment—A Cautionary Tale
by Rim Hur and Robert Rushakoff
Diabetology 2025, 6(11), 122; https://doi.org/10.3390/diabetology6110122 - 28 Oct 2025
Abstract
Background/Objectives: Diabetic foot infections (DFIs) are a leading cause of hospitalization, amputation, and costs among patients with diabetes. Although early treatment is assumed to reduce complications, its real-world effects remain uncertain. We applied a causal machine-learning (ML) approach to investigate whether early DFI [...] Read more.
Background/Objectives: Diabetic foot infections (DFIs) are a leading cause of hospitalization, amputation, and costs among patients with diabetes. Although early treatment is assumed to reduce complications, its real-world effects remain uncertain. We applied a causal machine-learning (ML) approach to investigate whether early DFI treatment improves hospitalization and clinical outcomes. Methods: We conducted an observational study using de-identified UCSF electronic health record (EHR) data from 1434 adults with DFI (2015–2024). Early treatment (<3 days after diagnosis) was compared to delayed/no treatment (≥3 days or none). Outcomes included DFI-related hospitalization and lower-extremity amputation (LEA). Confounders included demographics, comorbidities, antidiabetic medication use, and laboratory values. We applied Targeted Maximum Likelihood Estimation (TMLE) with SuperLearner, a machine-learning ensemble. Results: Early treatment was associated with higher hospitalization risk (TMLE risk difference [RD]: 0.293; 95% CI: 0.220–0.367), reflecting the triage of clinically sicker patients. In contrast, early treatment showed a protective trend against amputation (TMLE RD: −0.040; 95% CI: −0.098 to 0.066). Results were consistent across estimation methods and robust to bootstrap validation. A major limitation is that many patients likely received treatment outside UCSF, introducing uncertainty around exposure classification. Conclusions: Early treatment of DFIs increased hospitalization but reduced amputation risk, a paradox reflecting appropriate clinical triage and systematic exposure misclassification from fragmented healthcare records. Providers prioritize the sickest patients for early intervention, leading to greater short-term utilization but potentially preventing irreversible complications. These findings highlight a cautionary tale; even with causal ML, single-institution analyses may misrepresent treatment effects, underscoring the need for causally informed decision support and unified EHR data. Full article
(This article belongs to the Special Issue Diabetes Management in the Hospital: Applications of Artificial Intelligence)
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