Predicting seizure outcome is essential for tailoring epilepsy treatment. However, accurate prediction remains challenging with traditional methods, particularly in diverse patient populations. This study presents a graph-based deep learning framework for predicting seizure outcomes using stereo-electroencephalography (sEEG) data in pediatric patients with drug-resistant epilepsy and deep thalamic involvement. We analyzed 105 ictal events from sEEG recordings of 10 pediatric patients with documented thalamic seizure networks and evaluated our model in three different cross-validation strategies: seizurewise, windowed segmentation, and patient-wise analysis. Our graph neural network-based model represents each sEEG channel as a node with power spectral density features, while edges capture inter-channel correlations. The windowed segmentation approach, which divides seizure recordings into non-overlapping 2-second temporal windows, demonstrated superior performance across all metrics. This data augmentation technique achieved 93.9% accuracy, significantly outperforming both seizure-wise (82.%) and patient-wise (77.0%) approaches using complete seizure recordings. Network analysis revealed distinct thalamo-cortical connectivity patterns with denser network topology in sample patient with poor outcomes (<50% seizure reduction) as compared to sample patient with favorable outcomes (>50% seizure reduction). These findings demonstrate the potential of connectivity-based deep learning models for enhancing seizure outcome prediction in pediatric epilepsy, particularly in cases involving complex thalamo-cortical networks. This framework advances our understanding of thalamic seizure propagation and offers promise for AI-assisted personalized epilepsy treatment planning.
Protecting healthcare data from inference attacks, where adversaries deduce sensitive information from de-identified data, is critical. This study examines the vulnerability of such datasets, focusing on Tennessee facilities serving predominantly African American populations, while also incorporating analyses based on the MIMIC-III dataset representing Massachusetts. We apply differential privacy with varying epsilon values to assess its impact on statistical integrity and predictive model accuracy. Results show a clear trade-off: lower epsilon enhances privacy but degrades performance, while higher epsilon preserves utility at the cost of increased leakage risk. These findings underscore the importance of carefully balancing privacy and utility when allocating the privacy budget in clinical prediction tasks.
Simultaneous electrocardiography (ECG) and phonocardiogram (PCG) offer a multimodal view of cardiac function by capturing electrical and mechanical activity, respectively. However, their shared and unique information and potential for mutual reconstruction remain poorly understood across different physiological states and individuals.This study analyzes the EPHNOGRAM dataset of simultaneous ECG-PCG recordings during rest and exercise, using linear and nonlinear models—including a non-causal neural network—to reconstruct one modality from the other. Nonlinear models, especially non-causal neural network, outperform others, with ECG reconstruction from PCG proving more feasible. In the within-subject scenario, the non-causal neural network achieved a signal-to-noise ratio (SNR) of 6.5±5.2 dB and a cross-correlation of 0.78 ± 0.19 for PCG-based ECG reconstruction.These findings provide quantitative insight into the electromechanical relationship between cardiac signals and support the development of multimodal cardiac monitoring tools.
Lung adenocarcinoma (LUAD) represents a major global health challenge requiring more accessible and noninvasive screening methods. Traditional diagnostic approaches such as computed tomography or biopsies are effective but costly, resource-intensive, and carry associated risks. This study leverages gut microbiome data and machine learning techniques to develop a non-invasive pre-screening tool for LUAD. Using a dataset of 107 fecal samples (43 LUAD and 64 healthy controls), we explored the performance of nine machine learning algorithms and four distinct feature sets generated through feature selection methods to identify informative microbial biomarkers and construct accurate classification models. Our results demonstrate that feature selection significantly enhances model performance compared to baseline approaches. A Random Forest model combined with Correlation-based Feature Selection achieved an Area Under the Curve of 0.9967. Key taxa including Prevotella, Coprococcus, Phascolarctobacterium, Bilophila, Blautia, Enterococcus, and Bacteroides emerged as potential biomarkers. Functional predictions using PICRUSt2 revealed significant alterations in folate metabolism, methylation cycles, and photosynthetic bacterial activity, highlighting disrupted gut microbiome function in LUAD patients. These findings align with previous studies and suggest promising directions for non-invasive and cost-effective screening methods.
Diabetic foot ulcers, a life-threatening complication of diabetes, take a disproportionate toll on communities of color; however, these communities are currently underrepresented in dermatologic and wound image datasets. Further, many of these datasets were collected under controlled conditions, limiting the transferability of ulcer recognition models to naturalistic settings. In support of more equitable and generalizable computational modeling, we detail our two-year effort to create the first repository of diabetic foot ulcer images collected predominantly from patients of color in naturalistic settings. We conduct an evaluation of state-of-the-art foot ulcer segmentation and classification methods using our dataset of 3,362 foot images collected from 252 patients, and provide evidence that current ulcer recognition models result in insufficient performance: the best performing baseline model (Mask R-CNN) has been previously reported to achieve a Dice score of 90.2%, but achieves only 39.5% on our more naturalistic dataset from patients of color. We propose and evaluate a new pipeline which improves segmentation performance, including an ulcer detection model and a foundational segmentation model (Segment Anything 2 UNet) tailored to communities of color and specifically aiming for naturalistic assessment scenarios. We release our image dataset to support the development of larger, more diverse datasets, and ultimately more equitable models for diabetic foot care.
