---
title: "Machine learning prediction models for hypoglycemia in Chinese patients with diabetes: systematic"
id: "plos-one-2-performance-of-machine-learning-based-prediction-models-for-hypoglycemia-in"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-2-performance-of-machine-learning-based-prediction-models-for-hypoglycemia-in"
content_type: "clinical_feed_article"
specialty: "Endocrinology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876"
published_at: "2026-09-22T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Machine learning prediction models for hypoglycemia in Chinese patients with diabetes: systematic
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-2-performance-of-machine-learning-based-prediction-models-for-hypoglycemia-in
- **Specialty:** [Endocrinology](https://medichelpline.com/clinical-feed/endocrinology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876)
- **Published At:** 2026-09-22T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This systematic review and meta-analysis assessed the performance and methodological quality of **machine learning**–based models predicting **hypoglycemia** in Chinese patients with diabetes, using studies through February 2026. - Thirteen studies (published 2022–2026) conducted in China were included; most were retrospective (11/13) and most were single-center (11/13). Prediction horizons ranged from 30 minutes to 12 months. - The pooled prevalence of hypoglycemia across 12 studies was 25% (95% CI 17%–33%). Reported prevalences in individual studies ranged from 4% to 46.93%. - The overall pooled discrimination (AUC) for ML models was 0.90 (95% CI 0.87–0.93). Subgroup pooled AUCs by algorithm: **XGBoost** 0.89, **random forest** 0.88, SVM 0.85, LightGBM 0.84, logistic regression 0.83, decision tree 0.81. - Sample sizes for modeling datasets varied widely (192 to 255,404); validation datasets ranged 70 to 109,459. Seven studies reported external validation; others used internal validation (k-fold/bootstrapping). - Frequently used predictors included age, insulin use, meal omission, BMI, HbA1c, creatinine, and history of hypoglycemia. Over 40 unique predictors appeared across studies. - Calibration was reported in six studies (calibration curves or Hosmer–Lemeshow); no studies reported decision-curve analysis or formal clinical utility measures. - PROBAST assessment: 4 studies low overall risk of bias, 2 unclear, remainder high risk. Common methodological concerns included outcome definition/assessment and analysis issues; applicability concerns were evaluated but specific domain-level problems varied. - Heterogeneity across studies was high for many pooled estimates (I2 frequently >85%). Sensitivity and subgroup analyses were reported by study design, center type, predictor type (clinical variables vs CGM time-series), and validation method. - Authors conclude the field is nascent: several models show good discrimination, but methodological limitations, limited validation, concerns about robustness and interpretability, and lack of clinical utility assessment limit immediate clinical adoption. More rigorous development, external validation, and interpretable approaches are needed.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0358876) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0358876#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0358876#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0358876#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0358876#citedHeader) * 21 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0358876#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0358876#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0358876#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0358876#discussedHeader) Open Access Peer-reviewed Research Article # Performance of machine learning–based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis * Jinhua Yan, Roles Conceptualization, Data curation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing Affiliation Department of General Practice, the People’s Hospital of Leshan, Leshan, China ⨯ * Yanping Song, Roles Data curation, Writing – review & editing Affiliation Department of General Practice, the People’s Hospital of Leshan, Leshan, China ⨯ * Yangmei Du, Roles Writing – review & editing Affiliation Department of General Practice, the People’s Hospital of Leshan, Leshan, China ⨯ * Fanmin Li Roles Conceptualization, Supervision, Writing – original draft, Writing – review & editing * E-mail: 85168664@qq.com Affiliation Department of General Practice, the People’s Hospital of Leshan, Leshan, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0003-6309-8121 ](https://orcid.org/0009-0003-6309-8121 "ORCID Registry") ⨯ # Performance of machine learning–based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis * Jinhua Yan, * Yanping Song, * Yangmei Du, * Fanmin Li ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: September 22, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0358876) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0358876) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0358876) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0358876) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#abstract0) * [1 Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#sec005) * [2 Material](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#sec006) * [3 Results](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#sec014) * [4 Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#sec025) * [5 Conclusion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#sec030) * [Supporting information](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#sec031) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0358876) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876) ## Abstract ### Objectives This study aimed to systematically evaluate the predictive performance of machine learning (ML)–based models for predicting hypoglycemia in Chinese patients with diabetes. ### Methods We systematically searched PubMed, Embase, Web of Science, the Cochrane Library, CINAHL, CNKI, and Wanfang databases from inception to February 2026. Eligible studies focused on the development or validation of ML–based models for predicting hypoglycemia in Chinese patients with diabetes. Study selection and data extraction were performed independently by two reviewers. Information on study characteristics, modeling approaches, predictors, validation methods, and model performance was collected. The area under the receiver operating characteristic curve (AUC) was synthesized using a random-effects model. Study quality was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). ### Results A total of 13 studies were included, and the pooled prevalence of hypoglycemia was 25% (95% CI: 17%–33%). The overall pooled area under the receiver operating characteristic curve (AUC) was 0.90 (95% CI: 0.87–0.93). Subgroup analyses by modeling algorithms showed pooled AUCs of 0.89 for extreme gradient boosting (XGBoost), 0.88 for random forest (RF), 0.85 for support vector machine (SVM), 0.84 for Light Gradient Boosting Machine (LightGBM), 0.83 for logistic regression (LR), and 0.81 for decision tree (DT) models. Common predictors included age, insulin use, body mass index, HbA1c, creatinine, and history of hypoglycemia. ### Conclusion We attempted to provide a comprehensive overview of machine learning–based prediction models for hypoglycemia in patients with diabetes. Research in this field remains at an early stage, although several models with good discriminatory performance have been reported. Methodological limitations and insufficient validation were observed in many studies. Concerns regarding model robustness and interpretability also exist. More efforts to develop reliable and interpretable models and to promote their application in clinical practice for early risk identification are needed. ## Figures ![Table 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.t005) ![Fig 6](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g006) ![Fig 7](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g007) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g001) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.t001) ![Table 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.t002) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g002) ![Table 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.t003) ![Fig 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g003) ![Fig 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g004) ![Fig 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g005) ![Table 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.t004) ![Table 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.t005) ![Fig 6](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g006) ![Fig 7](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g007) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.g001) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.t001) ![Table 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0358876.t002) **Citation:** Yan J, Song Y, Du Y, Li F (2026) Performance of machine learning–based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis. PLoS One 21(9): e0358876. https://doi.org/10.1371/journal.pone.0358876 **Editor:** Yuzhen Xu, The Second Affiliated Hospital of Shandong First Medical University, CHINA **Received:** April 3, 2026; **Accepted:** September 6, 2026; **Published:** September 22, 2026 **Copyright:** © 2026 Yan et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** All data analyzed in this study were extracted from previously published studies, which are cited within the article. The extracted data used for this meta-analysis are available in the Supplementary Materials. **Funding:** The author(s) received no specific funding for this work. **Competing interests:** The authors have declared that no competing interests exist. ## 1 Introduction Diabetes mellitus is one of the most prevalent chronic metabolic diseases worldwide [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref001)]. According to the International Diabetes Federation (IDF) Diabetes Atlas, approximately 148 million adults in China are living with diabetes, representing the largest population of individuals with diabetes worldwide [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref002),[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref003)]. China has the largest population of individuals with diabetes worldwide, with clinical characteristics and diabetes management contexts that may differ from those reported in Western countries. Chinese patients with type 2 diabetes often develop diabetes at relatively lower BMI levels and exhibit distinct metabolic characteristics [[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref004)]. In addition, differences in dietary patterns, glucose monitoring accessibility, and glucose-lowering medication use [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref005)] may contribute to variations in hypoglycemia risk profiles and predictive variable distributions. Hypoglycemia is one of the most common and potentially underrecognized complications during glucose-lowering therapy [[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref006)]. Among patients receiving insulin or oral hypoglycemic agents, the incidence of mild-to-moderate hypoglycemia has been reported to reach 50% and 45%, respectively, while severe hypoglycemia occurs in approximately 21% and 6% of these patients [[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref007)]. Mild hypoglycemia can impair quality of life and treatment adherence, whereas severe hypoglycemia is associated with increased risks of falls, cardiovascular events, and mortality [[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref008),[9](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref009)]. The reported case fatality rate of hypoglycemia is approximately 12.9%, rising to 24.9% in severe cases [[10](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref010)]. Early identification of individuals at high risk is therefore essential to prevent hypoglycemic events and improve diabetes management [[11](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref011)]. Risk prediction models have been developed to assist clinicians in identifying patients vulnerable to hypoglycemia. Conventional models generally incorporate demographic characteristics, clinical, and treatment-related variables [[12](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref012)]. However, their predictive performance and generalizability across populations remain limited [[13](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref013)]. As diabetes management often involves complex treatment adjustments and heterogeneous patient characteristics, more refined approaches to risk assessment are needed. With the increasing availability of complex clinical data, machine learning (ML)–based models have been applied to hypoglycemia prediction. By incorporating diverse clinical information, these models can capture complex patterns and improve risk stratification in diabetes care [[14](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref014)–[16](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref016)]. Previous systematic reviews, such as that conducted by Liu et al.[[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref017)], have summarized the overall evidence. However, the performance and clinical applicability of ML-based prediction models among Chinese patients with diabetes have not been evaluated. Therefore, this systematic review focuses on Chinese patients with diabetes to to evaluate the performance, methodological quality, and clinical applicability of ML-based prediction models for hypoglycemia. ## 2 Material ### 2.1 Protocol and registration This meta-analysis was registered in PROSPERO (CRD420261338933) and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines [[18](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref018)]. ### 2.2 Literature search A comprehensive search of both English and Chinese literature was conducted in the databases PubMed, Embase, Web of Science, the Cochrane Library, CINAHL, China National Knowledge Infrastructure (CNKI), and the Wanfang Database. In addition, the reference lists of all included studies were manually screened to identify any potentially relevant articles. The search strategy combined Medical Subject Headings (MeSH) terms and free-text keywords related to hypoglycemia, diabetes, and ML –based prediction models. Key search terms included “hypoglycemia”, “diabetes mellitus”, “machine learning”, “artificial intelligence”, “deep learning”, “prediction model”, “risk factors”, and “risk prediction”. Boolean operators (“OR” and “AND”) were used to combine search terms across different concept groups. The search covered studies from database inception to February 2026, and only full-text original research articles were considered. All retrieved records were imported into NoteExpress software for literature management and duplicate removal. ### 2.3 Eligibility criteria We applied the Population, Index, Comparator, Outcome, Timing, and Setting (PICOTS) framework [[19](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref019)] to define the eligibility criteria for this review: P (Chinese patients with diabetes mellitus), I (Prediction models), C (not applicable), O (hypoglycemia), T (prediction during the course of diabetes), and S (clinical settings, including inpatient and outpatient care). The inclusion criteria were as follows: [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref001)] studies involving Chinese patients with type 1 or type 2 diabetes; [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref002)] studies developing ML–based models for hypoglycemia prediction; [[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref003)] studies reporting the area under the receiver operating characteristic curve (AUC) as a measure of model performance; [[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref004)] observational study designs (e.g., cohort or case-control studies); and [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref005)] articles published in English or Chinese. The exclusion criteria were: [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref001)] studies assessing risk factors without developing prediction models; [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref002)] studies with incomplete data or unavailable full texts; and [[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref003)] reviews, editorials, letters, case reports, conference abstracts, or other non-original studies. ### 2.4 Study selection Two reviewers independently screened the titles and abstracts of all retrieved records to identify potentially eligible studies and remove duplicate entries. The full texts of the remaining articles were then assessed for eligibility according to the predefined inclusion and exclusion criteria. Reasons for exclusion were documented during the full-text screening stage. Any disagreements were resolved through discussion, and a third reviewer was consulted when consensus could not be reached. ### 2.5 Data extraction Two authors (Yan and Song) independently extracted data from all eligible studies using a standardized data extraction form developed based on the CHARMS checklist [[20](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref020)](Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies). The extracted information included the first author, publication year, study design, study setting, dataset characteristics, modeling methods, validation approach, model performance, and predictors. All extracted data were cross-checked against the original articles. Any discrepancies were resolved through discussion, and if consensus could not be reached, a third reviewer was consulted for adjudication. ### 2.6 Quality appraisal The risk of bias and applicability of the included studies were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST), a methodological instrument designed for studies developing or validating prediction models [[21](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358876#pone.0358876.ref021)].
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