A machine-learning approach to predict postprandial hypoglycemia
2019
Background
For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set.
Keywords:
- Data mining
- Hypoglycemia
- Nocturnal hypoglycemia
- Artificial pancreas
- Diabetes mellitus
- Postprandial Hypoglycemia
- Intensive care medicine
- Medicine
- continuous glucose monitoring
- feature set
- Postprandial
- Logistic regression
- Machine learning
- Support vector machine
- F1 score
- Artificial intelligence
- Receiver operating characteristic
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
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References
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Citations
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