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Keywords {🔍}

glucose, prediction, data, article, diabetes, learning, google, scholar, model, deep, pubmed, cgm, insulin, hypoglycemia, events, type, wristband, hyperglycemia, models, fig, glycemic, measurements, physiological, levels, arises, app, performance, table, time, nature, daily, results, clinical, cas, continuous, monitoring, study, machine, neural, supplementary, personalized, adverse, sensor, algorithm, input, set, analysis, selfmanagement, realtime, rmse,

Topics {✒️}

$$\begin{array}{ll}{{{\rm{grmse}}}}\qquad\qquad=\sqrt{\frac{1}{ nature portfolio privacy policy amazon web services proprietary nature pre-processed multi-modal data \qquad\qquad\qquad\qquad+ registered research databases open-source programming languages advertising nature time index cloud services natural language processing70 social media natural language processing $$\begin{array}{ll}{\hat{ research design author information authors recommend solutions reprints open-source algorithm multi-modal feature engineering data visualization tools middle professional band-pass butterworth filter scientific rep }=\sqrt{\frac{{\beta }_{ original author closed-loop glycemic control8 uva/padova t1d simulator75 long short-term memory physiologically-based kinetic model long-term glucose forecasting full size image pancreatic β-cell physiology long-term temporal dependencies model-agnostic meta-learning long-term diabetes complications higher-order evidential distribution41 rule-based prediction models pancreatic β-cell loss permissions real-time glucose measurements real-time glucose prediction initial storage size real-time physiological measurements blood volume pulse

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• Is there more to blood volume pulse than heart rate variability, respiratory sinus arrhythmia, and cardiorespiratory synchrony?
• Rapid learning or feature reuse?

Schema {🗺️}

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      headline:Enhancing self-management in type 1 diabetes with wearables and deep learning
      description:People living with type 1 diabetes (T1D) require lifelong self-management to maintain glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with short and long-term complications. Continuous glucose monitoring (CGM) is widely used in T1D self-management for real-time glucose measurements, while smartphone apps are adopted as basic electronic diaries, data visualization tools, and simple decision support tools for insulin dosing. Applying a mixed effects logistic regression analysis to the outcomes of a six-week longitudinal study in 12 T1D adults using CGM and a clinically validated wearable sensor wristband (NCT ID: NCT03643692), we identified several significant associations between physiological measurements and hypo- and hyperglycemic events measured an hour later. We proceeded to develop a new smartphone-based platform, ARISES (Adaptive, Real-time, and Intelligent System to Enhance Self-care), with an embedded deep learning algorithm utilizing multi-modal data from CGM, daily entries of meal and bolus insulin, and the sensor wristband to predict glucose levels and hypo- and hyperglycemia. For a 60-minute prediction horizon, the proposed algorithm achieved the average root mean square error (RMSE) of 35.28 ± 5.77 mg/dL with the Matthews correlation coefficients for detecting hypoglycemia and hyperglycemia of 0.56 ± 0.07 and 0.70 ± 0.05, respectively. The use of wristband data significantly reduced the RMSE by 2.25 mg/dL (p < 0.01). The well-trained model is implemented on the ARISES app to provide real-time decision support. These results indicate that the ARISES has great potential to mitigate the risk of severe complications and enhance self-management for people with T1D.
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