Yes—an LSTM can be trained to predict a threshold-defined low-glucose event before it occurs, and a Transformer can forecast future CGM readings. But neither architecture automatically detects every clinically meaningful “anomaly.” First decide whether your model will forecast glucose values, classify a hypo- or hyperglycemia event within a specified time horizon, or assign an anomaly score. Those are different tasks, and a research prototype is not a clinical alarm or treatment recommendation.
Choose what “anomaly” means before building the model
A CGM model needs an explicit target. The target determines how to label examples, what the model learns, and which measures make its performance meaningful.
Forecast a future glucose value
For regression, use past CGM readings to estimate glucose at one or more future times—for example, 30 minutes, one hour, and two hours ahead. This answers “What glucose value might come next?” and is evaluated with measures such as mean absolute error (MAE) or root mean squared error (RMSE), reported separately for each horizon.
Predict a threshold event
For classification, define an event and a prediction window. For example, label a sample positive if glucose is expected to enter a specified low range during the next 30 minutes. The thresholds and horizon must be part of the task definition; “low soon” is not a reproducible label. This answers “Is a defined event likely within this window?” and calls for event measures such as sensitivity, specificity, precision, and false alarms at a stated operating threshold.
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Score an anomaly
An anomaly score requires its own definition of what counts as unusual, such as a deviation from an individualized expected pattern. A value forecast or threshold-event classifier is not automatically a general anomaly detector. The cited work supports glucose forecasting and threshold-event prediction, not automatic discovery of every clinically meaningful abnormal pattern.
What published CGM studies show
Published results demonstrate that both sequence-model approaches are plausible, but their figures are tied to particular populations, datasets, targets, and evaluation setups. They are not a universal LSTM-versus-Transformer leaderboard.
LSTM: threshold-defined hypoglycemia prediction
Shao and colleagues’ 2024 JMIR Medical Informatics study trained an LSTM using data from 192 participants in a Chinese primary dataset and validated it against data from 427 participants in a US cohort. The model used 72 CGM readings spanning six hours, along with age, gender, diabetes type, and HbA1c, to predict mild hypoglycemia (54–70 mg/dL) or severe hypoglycemia (<54 mg/dL) within 30 minutes. The authors reported AUC above 97% for mild hypoglycemia in the primary data and above 93% in validation subgroups.
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AUC describes discrimination across possible thresholds; it does not tell you how many false alarms a deployed system would generate at the threshold you choose. The study also used data from one CGM manufacturer and identified further validation—including on CGM data without missing data—as needed. Its reported results should not be treated as a guarantee for another sensor, population, or implementation.
Transformer: glucose forecasting
A 2026 study of CGM-LSM describes a decoder-only Transformer pretrained on more than 15 million CGM records from 592 people with diabetes, then evaluated on the public OhioT1DM dataset. On that benchmark, it reported rMSE of 9.02 mg/dL at 30 minutes, 15.90 mg/dL at one hour, and 26.88 mg/dL at two hours. The study reported that its one-hour rMSE was 48.51% lower than that of its vanilla Transformer baseline. These are results for that study’s model and benchmark setup, not expected performance for a new implementation. Read the CGM-LSM study.
The same study’s baseline table reported the following rMSE values on OhioT1DM:
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| Model in the study | 30-minute rMSE | 1-hour rMSE | 2-hour rMSE |
|---|---|---|---|
| LSTM baseline | 36.022 mg/dL | 37.17 mg/dL | 38.703 mg/dL |
| Vanilla Transformer baseline | 27.886 mg/dL | 30.869 mg/dL | 36.653 mg/dL |
Those baseline figures are reported for the study’s OhioT1DM comparison; they should not be compared as if they establish a universal ranking across different data splits or task definitions. CGM-LSM also reports higher error in low-glucose (<70 mg/dL) and high-glucose (>250 mg/dL) ranges, particularly at longer horizons. An overall average can therefore conceal weaker performance in ranges that matter most to a threshold-event use case.
Other Transformer examples have narrower settings
The 2023 “Glucose Transformer” paper describes forecasting glucose and hypo- and hyperglycemia events using one week of inpatient CGM data from people with type 2 diabetes. That is evidence for a research approach in that setting, not proof of performance in free-living use. See the paper record.
A 2026 medRxiv preprint describes a residual-gated multimodal Transformer using CGM data and sparse meal logs, with chronological within-person testing and participant-level cross-validation over horizons up to two hours. It is preprint evidence, not independent clinical validation. Read the version 2 preprint.
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Build the experiment in an order that prevents misleading results
The most important design choice is not the neural-network layer: it is whether your test set represents the kind of future use you intend to evaluate. Overlapping CGM windows from one person are highly related. If windows from the same participant appear in both training and test data, the test score can overstate how well the model generalizes.
- Define the target and horizon. Choose a regression target such as glucose at 30 minutes, or an event target such as crossing a specified threshold within 30 minutes. Record the threshold, prediction window, units, and whether the target is a value at a particular time or an event occurring anywhere in the window.
- Choose the input window and covariates. Specify how much history the model receives and which additional features are available at prediction time. Do not include information recorded after the moment the forecast would have been made.
- Audit and regularize the time series. Check timestamps, sampling irregularities, units, missing readings, and duplicated records. GlucoBench describes regularizing CGM sequences, interpolating short gaps, and splitting sequences when gaps exceed a dataset-specific threshold. Treat interpolation and gap handling as explicit preprocessing choices, not as proof that missingness has no effect. See GlucoBench’s dataset and benchmark description.
- Split participants before making sliding windows. For a known-participant future forecast, keep training, validation, and test periods chronological. If the intended use includes new participants, also evaluate on people held out entirely from model development. GlucoBench describes chronological splits and a held-out-subject evaluation set.
- Fit preprocessing on training data only. Any normalization statistics or learned transformations should be estimated from the training partition, then applied unchanged to validation and test data. This avoids allowing held-out values to influence model preparation.
- Train a simple baseline first. A persistence forecast—using the latest reading as the future estimate—is a useful reference for regression. For event prediction, compare against a clearly defined simple rule. A complex model is only useful if it improves on an appropriate baseline under the same split and target definition.
- Train LSTM and Transformer comparisons consistently. Keep participant splits, input history, horizons, covariates, and target labels the same. Tune on validation data, not the final test set. The architecture comparison is otherwise confounded by differences in data or evaluation.
- Evaluate by horizon, participant group, and glucose range. Report the score for each forecast horizon and inspect low, in-range, and high glucose periods separately. For event classifiers, state the operating threshold and show how sensitivity and false-alarm burden change when that threshold moves.
How to compare LSTM and Transformer models fairly
Use the same data and forecasting setup, then compare what each model gets right and where it fails. The reported studies do not establish a general winner on compute cost or interpretability, so measure those properties in your own implementation if they matter to the project.
| Comparison choice | Keep consistent | What to report |
|---|---|---|
| Data | Participants, preprocessing, missing-data policy, and partitions | Whether test periods come from known participants, held-out participants, or both |
| Forecast setup | Lookback window, covariates, target definition, and horizons | Separate results for each horizon |
| Value forecasting | Target units and scoring method | MAE or RMSE overall and by glucose range |
| Event prediction | Thresholds, event window, and decision threshold | Sensitivity, specificity, precision, and false alarms at stated operating points |
| Practical behavior | Hardware and measurement conditions when timing models | Inference latency, training resources, and model size if relevant to the intended use |
Regression error and event detection answer different questions. A model may estimate average glucose reasonably while still missing a threshold crossing, or it may detect many threshold events while producing a high false-alarm burden. If the project claims to predict events, include event metrics rather than presenting regression error alone.
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What a prototype can—and cannot—tell you
A research implementation can test whether recent CGM history contains signal for a defined future value or threshold event in a particular dataset. It cannot establish that predictions are safe to act on, that every device or user will see the same performance, or that the system recognizes all clinically important anomalies. Dataset shift, sensor differences, missing readings, and longer prediction horizons can change performance.
Keep the model’s output framed as an experimental forecast. Do not present it as a clinical alarm, diagnosis, or treatment recommendation. Any intended clinical use requires appropriate validation beyond a favorable benchmark score.
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