What question did this study set out to answer?

The research aims to develop a machine learning-driven digital twin framework for real-time monitoring of estradiol levels using hormone biosensors.

Machine Learning‐Driven Digital Twin of a Field‐Effect Transistor‐Based Hormone Biosensor for Real‐Time Estradiol Monitoring

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Demonstrates a machine learning approach to real-time estradiol monitoring in infertility, suggesting potential for personalized health care.

The research aims to develop a machine learning-driven digital twin framework for real-time monitoring of estradiol levels using hormone biosensors.
Developed a digital twin framework for a field-effect transistor-based hormone biosensor.
Used machine learning techniques to analyze real-time estradiol data.
Applied a row-level learning strategy treating each timepoint as a training sample.
Evaluated model performance using leave-one-biosensor-out validation with CatBoost.
CatBoost model achieved the best performance with RMSE of 0.240 in log scale and 85.28 pg/mL in original concentration.
Prediction relied on temporal and signal-morphology features, indicating meaningful learning dynamics.
Synthetic signal generation was successfully demonstrated using real biosensor data for forward modeling.

Gorelova et al. (2026) studied this question.

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View Full Paper

Ask AI

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Authors

Demonstrates a machine learning approach to real-time estradiol monitoring in infertility, suggesting potential for personalized health care.

The research aims to develop a machine learning-driven digital twin framework for real-time monitoring of estradiol levels using hormone biosensors.
Developed a digital twin framework for a field-effect transistor-based hormone biosensor.
Used machine learning techniques to analyze real-time estradiol data.
Applied a row-level learning strategy treating each timepoint as a training sample.
Evaluated model performance using leave-one-biosensor-out validation with CatBoost.
CatBoost model achieved the best performance with RMSE of 0.240 in log scale and 85.28 pg/mL in original concentration.
Prediction relied on temporal and signal-morphology features, indicating meaningful learning dynamics.
Synthetic signal generation was successfully demonstrated using real biosensor data for forward modeling.

Gorelova et al. (2026) studied this question.

View Full Paper

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Bookmark