AIMC Topic: Diabetes Mellitus, Type 1

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Image-Based Machine Learning Algorithms for Disease Characterization in the Human Type 1 Diabetes Pancreas.

The American journal of pathology
Emerging data suggest that type 1 diabetes affects not only the β-cell-containing islets of Langerhans, but also the surrounding exocrine compartment. Using digital pathology, machine learning algorithms were applied to high-resolution, whole-slide i...

Data-Driven Robust Control for a Closed-Loop Artificial Pancreas.

IEEE/ACM transactions on computational biology and bioinformatics
We present a fully closed-loop design for an artificial pancreas (AP) that regulates the delivery of insulin for the control of Type I diabetes. Our AP controller operates in a fully automated fashion, without requiring any manual interaction with th...

Multi-Hour Blood Glucose Prediction in Type 1 Diabetes: A Patient-Specific Approach Using Shallow Neural Network Models.

Diabetes technology & therapeutics
Considering current insulin action profiles and the nature of glycemic responses to insulin, there is an acute need for longer term, accurate, blood glucose predictions to inform insulin dosing schedules and enable effective decision support for the...

Humanoid robot-assisted interventions among children with diabetes: A systematic scoping review.

International journal of nursing studies
BACKGROUND: Although the humanoid robot is highly engaging for children, whether humanoid robot-assisted interventions could help in diabetes management is still unclear.

A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism.

Journal of medical Internet research
BACKGROUND: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suit...

Deep Physiological Model for Blood Glucose Prediction in T1DM Patients.

Sensors (Basel, Switzerland)
Accurate estimations for the near future levels of blood glucose are crucial for Type 1 Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and hyper-glycemic episodes. Accurate predictions for blood glucose are the ...

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.

Sensors (Basel, Switzerland)
Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1-5 min, the current blood glucose concentration and its rate-of-change, two key pieces of info...

Improving blood glucose level predictability using machine learning.

Diabetes/metabolism research and reviews
This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient-specific supervised-machine-learning (SML) analysis of glucose level based on a continuous-glucose-mo...