NEAR-INFRARED SPECTROSCOPY (NIR) BASED GLUCOSE PREDICTION USING DEEP LEARNING
A recurrent neural network that predicts blood glucose level includes a first long short-term memory (LSTM) network and a second LSTM network. The first LSTM network may include an input to receive near-infrared (NIR) radiation data and includes an output. The second LSTM network may include an inpu...
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| creator | LIU, Liu SAKHAEE, Elham GEORGIADIS, Georgios DENG, Weiran |
| description | A recurrent neural network that predicts blood glucose level includes a first long short-term memory (LSTM) network and a second LSTM network. The first LSTM network may include an input to receive near-infrared (NIR) radiation data and includes an output. The second LSTM network may include an input to receive the output of the first LSTM network and an output to output blood glucose level data based on the NIR radiation data input to the first LSTM network. |
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The first LSTM network may include an input to receive near-infrared (NIR) radiation data and includes an output. 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | NEAR-INFRARED SPECTROSCOPY (NIR) BASED GLUCOSE PREDICTION USING DEEP LEARNING |
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