Machine learning for non‐invasive sensing of hypoglycaemia while driving in people with diabetes

Aim: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. Materials and Methods: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced ypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1). Results: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). Conclusions: Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia

Time requirements for perioperative glucose management using fully closed-loop versus standard insulin therapy: A proof of concept time-motion study.

AIMS To compare the time required for perioperative glucose management using fully automated closed-loop versus standard insulin therapy. METHODS We performed a time-motion study to quantify the time requirements for perioperative glucose management with fully closed-loop (FCL) and standard insulin therapy applied to theoretical scenarios. Following an analysis of workflows in different periods of perioperative care in elective surgery patients receiving FCL or standard insulin therapy upon hospital admission ( pre- and intra-operatively, at the intermediate care unit and general wards), the time of process-specific tasks were measured by shadowing hospital staff. Each task was measured 20 times and its average duration in combination with its frequency according to guidelines was used to calculate the cumulative staff time required for blood glucose management. Cumulative time were calculated for theoretical scenarios consisting of elective minor and major abdominal surgeries (pancreatic surgery and sleeve gastrectomy, respectively) to account for the different care settings and length of stay. RESULTS FCL insulin therapy reduced the time required for perioperative glucose management compared to standard insulin therapy, across all assessed care periods and for both perioperative pathways (range 2.1-4.5). For a major abdominal surgery, total time required was 248.5 min using FCL vs. 753.9 min using standard insulin therapy. For a minor abdominal surgery, total time required was 68.6 min and 133.2 min for FCL and standard insulin therapy, respectively. CONCLUSIONS The use of fully automated closed-loop insulin delivery for inpatient glucose management has the potential to alleviate the workload of diabetes management in an environment with adequately trained staff

Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data.

OBJECTIVE To develop a noninvasive hypoglycemia detection approach using smartwatch data. RESEARCH DESIGN AND METHODS We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) noninvasively in unseen individuals and solely based on wearable data. RESULTS Twenty-two individuals were included in the final analysis (age 54.5 ± 15.2 years, HbA1c 6.9 ± 0.6%, 16 males). Hypoglycemia was detected with an area under the receiver operating characteristic curve of 0.76 ± 0.07 solely based on wearable data. Feature analysis revealed that the ML model associated increased heart rate, decreased heart rate variability, and increased tonic electrodermal activity with hypoglycemia. CONCLUSIONS Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning

Machine learning for non-invasive sensing of hypoglycemia while driving in people with diabetes.

AIMS Hypoglycemia is one of the most dangerous acute complications of diabetes mellitus and is associated with an increased risk of driving mishaps. Current approaches to detect hypoglycemia are limited by invasiveness, availability, costs, and technical restrictions. In this work, we developed and evaluated the concept of a non-invasive machine learning (ML) approach detecting hypoglycemia based exclusively on combined driving (CAN) and eye tracking (ET) data. MATERIALS AND METHODS We first developed and tested our ML approach in pronounced hypoglycemia, and, then, we applied it to mild hypoglycemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes mellitus. In study 1 (n=18), we collected CAN and ET data in a driving simulator during eu- and pronounced hypoglycemia (blood glucose [BG] 2.0 - 2.5 mmol L-1 ). In study 2 (n=9), we collected CAN and ET data in the same simulator but in eu- and mild hypoglycemia (BG 3.0 - 3.5 mmol L-1 ). RESULTS Here, we show that our ML approach detects pronounced and mild hypoglycemia with high accuracy (area under the receiver operating characteristics curve [AUROC] 0.88±0.10 and 0.83±0.11, respectively). CONCLUSIONS Our findings suggest that an ML approach based on CAN and ET data, exclusively, allows for detection of hypoglycemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycemia. This article is protected by copyright. All rights reserved

Machine Learning to Infer a Health State Using Biomedical Signals — Detection of Hypoglycemia in People with Diabetes while Driving Real Cars

ISSN:2836-938

Noninvasive Hypoglycemia Detection during Real Car Driving Using In-Vehicle Data

Aim: To develop a non-invasive machine learning (ML) approach to detect hypoglycemia during real car driving based on driving (CAN) , and eye and head motion (EHM) data. Methods: We logged CAN and EHM data in 21 subjects with type 1 diabetes (18 male, 41 ± yrs, A1c 6.8 ± 0.7 % [51 ± 7 mmol/mol]) during driving in eu- (EU) and hypoglycemia (< 3.0 mmol/L, HYPO) . Participants drove in a car (Volkswagen Touran) supervised by a driving instructor on a closed test-track. Using CAN and EHM data, we built ML models to predict the probability of the driver being in HYPO. To make our approach applicable to different generations of cars, we present 3 ML models: first, a model combining CAN+EHM, representing the modern car with integrated camera. Second, a CAN model using driving data only, since modern cars are not generally equipped with EHM tracking. Third, anticipating that autonomous driving will limit the role of CAN data in the future, we tested a model solely based on EHM. Results: Mean BG in EU and HYPO was 6.3 ± 0.8 mmol/L and 2.5 ± 0.5 mmol/L (p< 0.001) , respectively. The model CAN+EHM achieved an area under the receiver operating characteristic curve of 0.88 ± 0.05, sensitivity of 0.70 ± 0.30, and specificity of 0.83 ± 0.in detecting HYPO. Further results are in Fig. 1. Conclusion: We propose ML-based approaches to non-invasively detect HYPO from driver behavior, applicable to contemporary cars and anticipating developments in automotive technology.ISSN:0012-1797ISSN:1939-327

Machine Learning to Infer a Health State Using Biomedical Signals — Detection of Hypoglycemia in People with Diabetes while Driving Real Cars

BACKGROUND Hypoglycemia, one of the most dangerous acute complications of diabetes, poses a substantial risk for vehicle accidents. To date, both reliable detection and warning of hypoglycemia while driving remain unmet needs, as current sensing approaches are restricted by diagnostic delay, invasiveness, low availability, and high costs. This research aimed to develop and evaluate a machine learning (ML) approach for the detection of hypoglycemia during driving through data collected on driving characteristics and gaze/head motion. METHODS We collected driving and gaze/head motion data (47,998 observations) during controlled euglycemia and hypoglycemia from 30 individuals with type 1 diabetes (24 male participants; mean ±SD age, 40.1±10.3 years; mean glycated hemoglobin value, 6.9±0.7% [51.9±8.0 mmol/mol]) while participants drove a real car. ML models were built and evaluated to detect hypoglycemia solely on the basis of data regarding driving characteristics and gaze/head motion. RESULTS The ML approach detected hypoglycemia with high accuracy (area under the receiver-operating characteristic curve [AUROC], 0.80±0.11). When restricted to either driving characteristics or gaze/head motion data only, the detection performance remained high (AUROC, 0.73±0.07 and 0.70±0.16, respectively). CONCLUSIONS Hypoglycemia could be detected noninvasively during real car driving with an ML approach that used only data on driving characteristics and gaze/head motion, thus improving driving safety and self-management for people with diabetes. Interpretable ML also provided novel insights into behavioral changes in people driving while hypoglycemic. (Funded by the Swiss National Science Foundation and others; ClinicalTrials.gov numbers, NCT04569630 and NCT05308095.