Federico Ramallo

May 5, 2024

This New AI Can Outsmart Diabetes. See How

Federico Ramallo

May 5, 2024

This New AI Can Outsmart Diabetes. See How

Federico Ramallo

May 5, 2024

This New AI Can Outsmart Diabetes. See How

Federico Ramallo

May 5, 2024

This New AI Can Outsmart Diabetes. See How

Federico Ramallo

May 5, 2024

This New AI Can Outsmart Diabetes. See How

This New AI Can Outsmart Diabetes

Let’s talk about the integration of AI and deep learning into the control loop for designing automated insulin delivery systems for Type 1 Diabetes management. By leveraging LSTM networks for predicting glucose levels and formulating a model predictive control (MPC) strategy, this approach aims to enhance the accuracy and efficiency of insulin delivery, addressing the complex dynamics of human glucose regulation.

This is what you need to know:

Diabetes affects over 400 million people globally, with a significant impact on healthcare.

Automated insulin delivery systems represent a critical advancement for Type 1 Diabetes management.

The human body's glucose regulation is complex, involving both insulin and glucagon.

Current management methods require constant decision-making by patients.

Model Predictive Control (MPC) is identified as a suitable strategy for automated insulin delivery.

MPC utilizes a model of the system's dynamics for future output prediction.

Deep learning, specifically LSTM networks, is employed to predict glucose levels accurately.

The approach allows for efficient online optimization, crucial for real-time control.

The study utilized a computer simulator approved as a substitute for animal trials.

A dual-component predictor was developed, combining a non-linear function with an affine component related to control inputs.

The proposed method efficiently solves the MPC problem as a quadratic programming problem.

Preliminary results show a significant improvement in maintaining glucose levels within a safe range.

The controller managed to keep glucose levels in the desired range 73% of the time.

Compared to optimal clinical practices, the new method shows comparable or better performance.

The computational burden lies mainly in the offline training of the LSTM model.

Future steps include refining the controller tuning and testing in more varied scenarios.

Real-world applications could greatly benefit from this advanced control strategy.

The method’s efficiency and effectiveness could lead to a paradigm shift in diabetes management.

Further research will explore the potential for real-time model retraining and adaptation.

This work paves the way for more autonomous and precise healthcare solutions.

I'm excited to share these findings and explore how deep learning can revolutionize chronic disease management, particularly Type 1 Diabetes.

This New AI Can Outsmart Diabetes

Let’s talk about the integration of AI and deep learning into the control loop for designing automated insulin delivery systems for Type 1 Diabetes management. By leveraging LSTM networks for predicting glucose levels and formulating a model predictive control (MPC) strategy, this approach aims to enhance the accuracy and efficiency of insulin delivery, addressing the complex dynamics of human glucose regulation.

This is what you need to know:

Diabetes affects over 400 million people globally, with a significant impact on healthcare.

Automated insulin delivery systems represent a critical advancement for Type 1 Diabetes management.

The human body's glucose regulation is complex, involving both insulin and glucagon.

Current management methods require constant decision-making by patients.

Model Predictive Control (MPC) is identified as a suitable strategy for automated insulin delivery.

MPC utilizes a model of the system's dynamics for future output prediction.

Deep learning, specifically LSTM networks, is employed to predict glucose levels accurately.

The approach allows for efficient online optimization, crucial for real-time control.

The study utilized a computer simulator approved as a substitute for animal trials.

A dual-component predictor was developed, combining a non-linear function with an affine component related to control inputs.

The proposed method efficiently solves the MPC problem as a quadratic programming problem.

Preliminary results show a significant improvement in maintaining glucose levels within a safe range.

The controller managed to keep glucose levels in the desired range 73% of the time.

Compared to optimal clinical practices, the new method shows comparable or better performance.

The computational burden lies mainly in the offline training of the LSTM model.

Future steps include refining the controller tuning and testing in more varied scenarios.

Real-world applications could greatly benefit from this advanced control strategy.

The method’s efficiency and effectiveness could lead to a paradigm shift in diabetes management.

Further research will explore the potential for real-time model retraining and adaptation.

This work paves the way for more autonomous and precise healthcare solutions.

I'm excited to share these findings and explore how deep learning can revolutionize chronic disease management, particularly Type 1 Diabetes.

This New AI Can Outsmart Diabetes

Let’s talk about the integration of AI and deep learning into the control loop for designing automated insulin delivery systems for Type 1 Diabetes management. By leveraging LSTM networks for predicting glucose levels and formulating a model predictive control (MPC) strategy, this approach aims to enhance the accuracy and efficiency of insulin delivery, addressing the complex dynamics of human glucose regulation.

This is what you need to know:

Diabetes affects over 400 million people globally, with a significant impact on healthcare.

Automated insulin delivery systems represent a critical advancement for Type 1 Diabetes management.

The human body's glucose regulation is complex, involving both insulin and glucagon.

Current management methods require constant decision-making by patients.

Model Predictive Control (MPC) is identified as a suitable strategy for automated insulin delivery.

MPC utilizes a model of the system's dynamics for future output prediction.

Deep learning, specifically LSTM networks, is employed to predict glucose levels accurately.

The approach allows for efficient online optimization, crucial for real-time control.

The study utilized a computer simulator approved as a substitute for animal trials.

A dual-component predictor was developed, combining a non-linear function with an affine component related to control inputs.

The proposed method efficiently solves the MPC problem as a quadratic programming problem.

Preliminary results show a significant improvement in maintaining glucose levels within a safe range.

The controller managed to keep glucose levels in the desired range 73% of the time.

Compared to optimal clinical practices, the new method shows comparable or better performance.

The computational burden lies mainly in the offline training of the LSTM model.

Future steps include refining the controller tuning and testing in more varied scenarios.

Real-world applications could greatly benefit from this advanced control strategy.

The method’s efficiency and effectiveness could lead to a paradigm shift in diabetes management.

Further research will explore the potential for real-time model retraining and adaptation.

This work paves the way for more autonomous and precise healthcare solutions.

I'm excited to share these findings and explore how deep learning can revolutionize chronic disease management, particularly Type 1 Diabetes.

This New AI Can Outsmart Diabetes

Let’s talk about the integration of AI and deep learning into the control loop for designing automated insulin delivery systems for Type 1 Diabetes management. By leveraging LSTM networks for predicting glucose levels and formulating a model predictive control (MPC) strategy, this approach aims to enhance the accuracy and efficiency of insulin delivery, addressing the complex dynamics of human glucose regulation.

This is what you need to know:

Diabetes affects over 400 million people globally, with a significant impact on healthcare.

Automated insulin delivery systems represent a critical advancement for Type 1 Diabetes management.

The human body's glucose regulation is complex, involving both insulin and glucagon.

Current management methods require constant decision-making by patients.

Model Predictive Control (MPC) is identified as a suitable strategy for automated insulin delivery.

MPC utilizes a model of the system's dynamics for future output prediction.

Deep learning, specifically LSTM networks, is employed to predict glucose levels accurately.

The approach allows for efficient online optimization, crucial for real-time control.

The study utilized a computer simulator approved as a substitute for animal trials.

A dual-component predictor was developed, combining a non-linear function with an affine component related to control inputs.

The proposed method efficiently solves the MPC problem as a quadratic programming problem.

Preliminary results show a significant improvement in maintaining glucose levels within a safe range.

The controller managed to keep glucose levels in the desired range 73% of the time.

Compared to optimal clinical practices, the new method shows comparable or better performance.

The computational burden lies mainly in the offline training of the LSTM model.

Future steps include refining the controller tuning and testing in more varied scenarios.

Real-world applications could greatly benefit from this advanced control strategy.

The method’s efficiency and effectiveness could lead to a paradigm shift in diabetes management.

Further research will explore the potential for real-time model retraining and adaptation.

This work paves the way for more autonomous and precise healthcare solutions.

I'm excited to share these findings and explore how deep learning can revolutionize chronic disease management, particularly Type 1 Diabetes.

This New AI Can Outsmart Diabetes

Let’s talk about the integration of AI and deep learning into the control loop for designing automated insulin delivery systems for Type 1 Diabetes management. By leveraging LSTM networks for predicting glucose levels and formulating a model predictive control (MPC) strategy, this approach aims to enhance the accuracy and efficiency of insulin delivery, addressing the complex dynamics of human glucose regulation.

This is what you need to know:

Diabetes affects over 400 million people globally, with a significant impact on healthcare.

Automated insulin delivery systems represent a critical advancement for Type 1 Diabetes management.

The human body's glucose regulation is complex, involving both insulin and glucagon.

Current management methods require constant decision-making by patients.

Model Predictive Control (MPC) is identified as a suitable strategy for automated insulin delivery.

MPC utilizes a model of the system's dynamics for future output prediction.

Deep learning, specifically LSTM networks, is employed to predict glucose levels accurately.

The approach allows for efficient online optimization, crucial for real-time control.

The study utilized a computer simulator approved as a substitute for animal trials.

A dual-component predictor was developed, combining a non-linear function with an affine component related to control inputs.

The proposed method efficiently solves the MPC problem as a quadratic programming problem.

Preliminary results show a significant improvement in maintaining glucose levels within a safe range.

The controller managed to keep glucose levels in the desired range 73% of the time.

Compared to optimal clinical practices, the new method shows comparable or better performance.

The computational burden lies mainly in the offline training of the LSTM model.

Future steps include refining the controller tuning and testing in more varied scenarios.

Real-world applications could greatly benefit from this advanced control strategy.

The method’s efficiency and effectiveness could lead to a paradigm shift in diabetes management.

Further research will explore the potential for real-time model retraining and adaptation.

This work paves the way for more autonomous and precise healthcare solutions.

I'm excited to share these findings and explore how deep learning can revolutionize chronic disease management, particularly Type 1 Diabetes.

Guadalajara

Werkshop - Av. Acueducto 6050, Lomas del bosque, Plaza Acueducto. 45116,

Zapopan, Jalisco. México.

Texas
17350 State Hwy 249, Ste 220 #20807,

Houston, Texas 77064 US.

© Density Labs. All Right reserved. Privacy policy and Terms of Use.

Guadalajara

Werkshop - Av. Acueducto 6050, Lomas del bosque, Plaza Acueducto. 45116,

Zapopan, Jalisco. México.

Texas
17350 State Hwy 249, Ste 220 #20807,

Houston, Texas 77064 US.

© Density Labs. All Right reserved. Privacy policy and Terms of Use.

Guadalajara

Werkshop - Av. Acueducto 6050, Lomas del bosque, Plaza Acueducto. 45116,

Zapopan, Jalisco. México.

Texas
17350 State Hwy 249, Ste 220 #20807,

Houston, Texas 77064 US.

© Density Labs. All Right reserved. Privacy policy and Terms of Use.