High-quality real-world datasets are essential for advancing data-driven approaches in type 1 diabetes (T1D) management, including personalized therapy design, digital twin systems, and glucose prediction models. However, progress in this area has been limited by the scarcity of publicly available datasets that offer detailed and comprehensive patient data. To address this gap, we present$A Z T 1 D$, a dataset containing data collected from 25 individuals with T1D on automated insulin delivery (AID) systems. AZT1D includes continuous glucose monitoring (CGM) data, insulin pump and insulin administration data, carbohydrate intake, and device mode (regular, sleep, and exercise) obtained over 6-8 weeks for each patient. Notably, the dataset provides granular details on bolus insulin delivery (i.e., total dose, bolus type, correction-specific amounts) features that are rarely found in existing datasets. By offering rich, naturalistic data, AZT1D supports a wide range of artificial intelligence and machine learning applications aimed at improving clinical decision-making and individualized care in T1D.

Accurate blood glucose forecasting enables proactive management of metabolic health, particularly when leveraging data from wearable sensors that capture data about physiological and behavioral health. However, existing models struggle with integrating multimodal time-series data with inconsistent sampling rates. This paper proposes a novel forecasting framework that incorporates a time-aware cross-attention mechanism with an LSTM architecture to predict blood glucose levels using continuous glucose monitoring (CGM) data alongside physiological and behavioral signals, such as heart rate (HR), electrodermal activity, accelerometry, and dietary intake. The proposed method dynamically encodes temporal features without the need for preprocessing and employs gated multi-head cross-attention layers to fuse sensor modalities effectively. We evaluate our approach on a newly constructed dataset involving$\mathbf{1 2}$participants. Our method outperforms the baseline and state-of-the-art GlySim models across multiple prediction horizons ranging from 5 minutes to 90 minutes, achieving up to 17.8% improvement in Root Mean Squared Error (RMSE) values.

Counterfactual explanations (CFs) offer human-centric insights into machine learning predictions by highlighting minimal changes required to alter an outcome. Therefore, CFs can be used as (i) interventions for abnormality prevention and (ii) augmented data for training robust models. In this work, we explore large language models (LLMs), specifically GPT-4o-mini, for generating CFs in a zero-shot and three-shot setting. We evaluate our approach on two datasets: the AI-Readi flagship dataset for stress prediction and a public dataset for heart disease detection. Compared to traditional methods such as DiCE, CFNOW, and NICE, our few-shot LLM-based approach achieves high plausibility (up to 99%), strong validity (up to 0.99), and competitive sparsity. Moreover, using LLM-generated CFs as augmented samples improves downstream classifier performance (an average accuracy gain of 5%), especially in low-data regimes. This demonstrates the potential of prompt-based generative techniques to enhance explainability and robustness in clinical and physiological prediction tasks. Code base: github.com/shovito66/SenseCF.

Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turn, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging since the morphology of the signal gets distorted in the presence of noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30 dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state-of-the-art techniques.

Regulating blood glucose concentration is crucial for every individual, particularly for patients with diabetes or prediabetes to manage their metabolic health. Poor glucose control results in dysglycemia. Frequent dysglycemia exposure increases the risk of cardiovascular disease, seizures, loss of consciousness, and potentially death. Patients often struggle with glucose control due to a multitude of interrelated behavioral, physiological, and biological factors such as food, insulin intake, and metabolism rate. There is a need for a solution that can accurately predict future adverse dysglycemic events and important parameters such as the area under the glucose curve (AUC). However, current research uses limited input parameters, lacks potential meal-based predictions, is data-hungry and computationally expensive, and predicts a single health outcome. In this research, GlucoseAssist1, a novel, personalized, AI-driven system was developed to predict glucose response and area under the glucose curve in real-time and identify dysglycemic events based on diet, health, and medication data. Importantly, the devised tiered architecture uses a multimodal convolutional neural network and random forest classifier with time series data from a clinical dataset with 20,040 Continuous Glucose Monitor (CGM) records. GlucoseAssist accurately predicts blood glucose response for the next 30 minutes with a Root Mean Squared Error of 1.23, Mean Absolute Error of 0.920, and an accuracy of 97.07% for the identification of dysglycemic events.

Postprandial hyperglycemia (PPHG) is detrimental to health and increases risk of cardiovascular diseases, reduced eyesight, and life-threatening conditions like cancer. Detecting PPHG events before they occur can potentially help with providing early interventions. Prior research suggests that PPHG events can be predicted based on information about diet. However, such computational approaches (1) are data hungry requiring significant amounts of data for algorithm training; and (2) work as a black-box and lack interpretability, thus limiting the adoption of these technologies for use in clinical interventions. Motivated by these shortcomings, we propose, DietNudge1, a machine learning based framework that integrates multi-modal data about diet, insulin, and blood glucose to predict PPHG events before they occur. Using data from patients with diabetes, we demonstrate that our model can predict PPHG events with up to 90% classification accuracy and an average F1 score of 0.93. The proposed decision-tree-based approach also identifies modifiable factors that contribute to an impending PPHG event while providing personalized thresholds to prevent such events. Our results suggest that we can develop simple, yet effective, computational algorithms that can be used as preventative mechanisms for diabetes and obesity management.

Effective diagnosis of neuro-degenerative diseases is critical to providing early treatments, which in turn can lead to substantial savings in medical costs. Machine learning models can help with the diagnosis of such diseases like Parkinson’s and aid in assessing disease symptoms. This work introduces a novel system that integrates pervasive computing, mobile sensing, and machine learning to classify hand-drawn images and provide diagnostic insights for the screening of Parkinson’s disease patients. We designed a computational framework that combines data augmentation techniques with optimized convolutional neural network design for on-device and real-time image classification. We assess the performance of the proposed system using two datasets of images of Archimedean spirals drawn by hand and demonstrate that our approach achieves 76% and 83% accuracy respectively. Thanks to 4x memory reduction via integer quantization, our system can run fast on an Android smartphone. Our study demonstrates that pervasive computing may offer an inexpensive and effective tool for early diagnosis of Parkinson’s disease1.