Evidence map›Paper›PMID 41907406›Full record

ArticleiScience2026

Glucose forecasting and hypoglycemia forewarning in type 1 and type 2 diabetes using deep learning.

Siyi Fang, Haowei Zhang, Die Hu, Xuefeng Yu, Zhelong Liu, Delin Ma, Weijie Xu, Fan Lin, Qiang Xie, Fang Liu and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Siyi FangThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Haowei ZhangThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Die HuThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Xuefeng YuDepartment of Endocrinology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Zhelong LiuDepartment of Endocrinology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Delin MaDepartment of Endocrinology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Weijie XuDepartment of Endocrinology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Fan LinDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Qiang XieWuhan United Imaging Surgical Healthcare Co., Ltd, Wuhan 430206, China.
Fang LiuWuhan United Imaging Surgical Healthcare Co., Ltd, Wuhan 430206, China.
Xianlong HuWuhan United Imaging Surgical Healthcare Co., Ltd, Wuhan 430206, China.
Tangdong AoWuhan United Imaging Surgical Healthcare Co., Ltd, Wuhan 430206, China.
Dengshi ZhouWuhan United Imaging Surgical Healthcare Co., Ltd, Wuhan 430206, China.
Qiang LiThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Peng ZhangThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypoglycemia is a major barrier to safe diabetes management. Although deep learning has been widely applied to blood glucose (BG) prediction, most studies provide limited hypoglycemia forewarning and are trained on small type 1 diabetes cohorts with restricted generalizability. We developed MT-HypoNet, a multitask neural network for real-time BG prediction and hypoglycemia forewarning from continuous glucose monitoring data. To improve detection near the hypoglycemia boundary, we introduce a statistically guided soft-label strategy. MT-HypoNet was validated on a multicenter cohort of 1,662 patients with type 1 and type 2 diabetes and prospectively evaluated in 36 perioperative patients with type 2 diabetes. In internal validation, MT-HypoNet achieved an AUC of 0.946 (95% CI: 0.946-0.947) for hypoglycemia forewarning and an RMSE of 19.84 ± 4.92 mg/dL for BG prediction. It generalized well to external datasets and maintained high prospective performance (AUC 0.966; RMSE 16.62 ± 4.01 mg/dL), supporting proactive management and improved safety.

Indexed as

health sciencesinternal medicinemedical specialtymedicine

Identifiers

PMID41907406
PMCPMC13019577

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.