A machine learning model for week-ahead hypoglycemia prediction from continuous glucose monitoring data

This repository contains the code used in the paper "A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data", published in the Journal of Diabetes Science and Technology, doi: 10.1177/19322968241236208.

Model description

The model takes as input the patient's continuous glucose monitoring (CGM) readings over a given week, and outputs the probability that the patient will experience a hypoglycemic event over the subsequent week. The model consists of two components: - an unsupervised feature extraction algorithm which uses random convolutional kernels to derive a large number of features from the patient's CGM time series; - a regularized linear classifier which takes as input the extracted features and outputs the patient's predicted hypoglycemic event probability.

The feature extraction algorithm is the MiniRocket [1] algorithm for variable length inputs, and the code is taken directly from the official code repository. The linear classifier is an L1 and L2 regularized logistic regression trained with gradient descent in TensorFlow, and the code is provided in this repository.

Model training

The training algorithm takes as input the CGM time series of one or more patients $i \in \{1, 2, \ldots, N\}$. It then splits the patients' CGM time series into non-overlapping one-week sequences and derives the $(X^{i}_{t}, y^{i}_{t + 1})$ training pairs, where

• $X^{i}_{t}$ is the time series of CGM readings of patient $i$ on week $t$ (e.g. 2,016 readings for a patient wearing a 5-minute CGM sensor 100% of the time),
• $y^{i}_{t + 1}$ is the binary label of patient $i$ on week $t + 1$, which is equal to 1 if patient $i$ experienced a hypoglycemic event during week $t + 1$ and equal to 0 otherwise.

The input sequences $X^{i}_{t}$ are fed to the MiniRocket algorithm which transforms them into 10,000 features $Z^{i}_{t}$. The extracted features $Z^{i}_{t}$ are then used together with the binary labels $y^{i}_{t + 1}$ for training the linear classifier.

Note that the $(X^{i}_{t}, y^{i}_{t + 1})$ training pairs of different patients are pooled together before being fed to the model, i.e. the training algorithm fits a unique model for the entire cohort of patients, as opposed to fitting a distinct model for each patient.

In addition to learning the model parameters, the training algorithms also finds the optimal decision threshold $c$ on the linear classifier's predicted probabilities, which is obtained by minimizing the difference between sensitivity and specificity.

Model inference

The inference algorithm takes as input the one-week sequences $X^{i}_{t}$ of one or more patients, which are defined as outlined above. The input sequences $X^{i}_{t}$ are fed to the MiniRocket algorithm, which transforms them into 10,000 features $Z^{i}_{t}$. The extracted features $Z^{i}_{t}$ are then passed to the linear classifier which outputs the predicted hypoglycemic event probability $\hat{p}^{i}_{t + 1}$ for the subsequent week $t + 1$.

The predicted binary labels are obtained by comparing the predicted probability $\hat{p}^{i}_{t + 1}$ with the decision threshold $c$ previously estimated on the training set. If $\hat{p}^{i}_{t + 1} > c$, then the model predicts that patient $i$ is likely to experience a hypoglycemic event in the subsequent week $t + 1$ ($y^{i}_{t + 1} = 1$), while if $\hat{p}^{i}_{t + 1} \le c$, then the model predicts that patient $i$ is unlikely to experience a hypoglycemic event in the subsequent week $t + 1$ ($y^{i}_{t + 1} = 0$).

Code

Dependencies

bash pandas==2.0.3 numpy==1.23.5 scipy==1.10.1 numba==0.56.4 statsmodels==0.13.5 scikit-learn==1.2.2 tensorflow==2.13.0

Hyperparameters

The MiniRocket algorithm uses the default hyperparameters recommended by the authors [1] and their values are not exposed in the code.

The linear classifier has the following hyperparameters:

• l1_penalty: (float). The L1 penalty.
• l2_penalty: (float). The L2 penalty.
• learning_rate: (float). The learning rate used for training.
• batch_size: (int). The batch size used for training.
• epochs: (int). The maximum number of training epochs.

Note that the linear classifier is trained with early stopping by monitoring the binary cross-entropy loss on a held-out 30% validation set with patience of 10 epochs.

The following additional hyperparameters are used for deriving the model's input sequences and output labels:

• time_worn_threshold: (float, default = 0.7). The minimum percentage of time that the patient must have worn the CGM device over a given week.
• glucose_threshold: (int, default = 54). The glucose level below which we detect the onset of hypoglycemia, in mg/dL.
• event_duration_threshold: (int, default = 15). The minimum length of a hypoglycemic event, in minutes.

Note that the one-week periods during which the patient has worn the device for a fraction of time lower than time_worn_threshold are not used at any stage, neither for training nor for inference.

Examples

The examples below show how to use the code for training and inference on a set of patients' CGM time series, which for this purpose are artificially generated.

Model training

```python from src.model import Model from src.simulation import simulatepatients from src.utils import getlabelled_sequences

minimum percentage of time that the patient must have worn the device over a given week

timewornthreshold = 0.7

glucose threshold below which we detect the onset of hypoglycemia, in mg/dL

glucose_threshold = 54

minimum length of a hypoglycemic event, in minutes

eventdurationthreshold = 15

generate a dummy dataset

data = simulate_patients( freq=5, # sampling frequency of the time series, in minutes length=84, # length of the time series, in days num=100, # number of time series )

reshape the dataset from long to wide

data = data.pivot(index='ts', columns=['id'], values=['gl']) data.columns = data.columns.getlevelvalues(level='id')

split the dataset into sequences

sequences = getlabelledsequences( data=data, timewornthreshold=timewornthreshold, glucosethreshold=glucosethreshold, eventdurationthreshold=eventdurationthreshold, )

train the model

model = Model()

model.fit( sequences=sequences, sequencelength=int(7 * 24 * 60 // 5), l1penalty=0.005, l2penalty=0.05, learningrate=0.00001, batch_size=32, epochs=1000, seed=42, verbose=1 )

save the model

model.save(directory='model') ```

Model inference

```python from src.model import Model from src.simulation import simulatepatients from src.utils import getunlabelled_sequences

minimum percentage of time that the patient must have worn the device over a given week

timewornthreshold = 0.7

generate a dummy dataset

data = simulate_patients( freq=5, # sampling frequency of the time series, in minutes length=7, # length of the time series, in days num=100, # number of time series )

reshape the dataset from long to wide

data = data.pivot(index='ts', columns=['id'], values=['gl']) data.columns = data.columns.getlevelvalues(level='id')

split the dataset into sequences

sequences = getunlabelledsequences( data=data, timewornthreshold=timewornthreshold, )

load the model

model = Model() model.load(directory='model')

generate the model predictions

predictions = model.predict(sequences=sequences) print(predictions.head()) ```

References

[1] Dempster, A., Schmidt, D.F. and Webb, G.I., 2021. MiniRocket: A very fast (almost) deterministic transform for time series classification. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 248-257).