Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Abstract

Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.

The effectiveness of digital twins in promoting precision health across the entire population: a systematic review

“…Further scrutiny resulted in the exclusion of one study (1/17, 5.88%) lacking healthrelated outcomes and four studies (4/17, 23.53%) with overlapping data. Ultimately, 12 (12/17, 70.59%) original studies [13][14][15][16][17][18][19][20][21][22][23][24] were included in the systematic review. Supplementary Table 1 provides a summary of the reasons for exclusion at the full-text reading phase.…”

“…The studies included in this systematic review were published between 2021 (2/12, 16.67%) 23,24 and 2023 (8/12, 66.67%) [13][14][15][16][17][18][19][20] . Originating from diverse regions, 4/12 studies (33.33%) were from Asia 13,14,21,24 , 5/12 (41.67%) from America 15,17,19,20,22 , and 3/12 (25.00%) from Europe 16,18,23 .…”

“…The studies included in this systematic review were published between 2021 (2/12, 16.67%) 23,24 and 2023 (8/12, 66.67%) [13][14][15][16][17][18][19][20] . Originating from diverse regions, 4/12 studies (33.33%) were from Asia 13,14,21,24 , 5/12 (41.67%) from America 15,17,19,20,22 , and 3/12 (25.00%) from Europe 16,18,23 . The review encompassed various study designs, including randomized controlled trials (1/12, 8.33%) 14 , quasi-experiments (6/12, 50.00%) 13,15,16,18,19,21 , and cohort studies (5/12, 41.67%) 17,20,[22][23][24] .…”

Personalized Diabetes Management with Digital Twins: A Patient-Centric Knowledge Graph Approach

“…These virtual replicas facilitate highly individualized care plans by integrating patient-specific data (genomic, medical history, lifestyle, and real-time sensor data). Studies like Thamotharan et al [16] showcase how digital twins can optimize medication regimens (e.g., insulin administration), while others explore their use in surgery planning [17] and tailored device development [18,19]. Beyond individual care, digital twins can inform larger-scale public health interventions.…”

The potential of the Medical Digital Twin in diabetes management: a review

“…MeDigiT plays a crucial role in DM management through diet, exercise, and insulin function. Thamotharan et al ( 25 ) proposed a human digital twin (HDT) framework and IoT architecture for personalized management of T2DM in older adults. The framework combines deep learning (DL) models and mathematical models based on various patient data and can personalize insulin administration according to the patient's different statuses.…”