arXiv subjects

Tanmoy Sarkar Pias

Publications and source records attributed to Tanmoy Sarkar Pias.

6 recordsLinked to original sources

arXiv2026

CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constrained by privacy regulation, institutional review, data-use agreements, and the risk of re-identification. Synthetic data promises a practical alternative: it can preserve useful statistical and clinical structure while reducing exposure of sensitive patient records. Prior studies often evaluate a single generator, one dataset, or a narrow downstream task, making it difficult to know when synthetic data can support model development and when it fails to preserve task-critical signal. We introduce CoMedBench, a reproducible benchmark that evaluates a family of generators under a common clinical-validity framework and one shared training and evaluation engine, spanning static tabular and temporal downstream tasks on established critical-care datasets. In total the benchmark spans 37 dataset-task pairs across two modalities consists of 20 static tabular and 17 temporal ICU time-series-drawn from seven public data sources: three intensive-care databases (MIMIC-III, MIMIC-IV, and eICU) together with the UCI Machine Learning Repository, the CDC BRFSS diabetes cohort (2015), NHANES (1999-2014), and the pycox survival datasets (GBSG and METABRIC). The benchmark evaluates both statistical fidelity and task utility by comparing models trained and tested across real and synthetic data. In these settings, synthetic training data preserves most of the downstream signal: on tabular tasks the reference generator CoMed-CTGAN retains a mean AUROC utility (the synthetic-to-real performance ratio) of 90.6%, rising to 97.3% for the strongest generator, CoMed-TVAE. Temporal ICU tasks are harder and more generator-sensitive: CoMed-CTGAN retains 81.6% (AUROC) and only 64.0% under the imbalance-sensitive AUPRC, whereas CoMed-TVAE still retains ~95% (AUROC).

cs.LG↗

arXiv2026

Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease

Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes. We present a two-part study that combines large-scale telehealth data with advanced machine learning to both classify self-reported CKD status and identify key drivers of disease. Using selected features from the Behavioral Risk Factor Surveillance System (BRFSS 2021: 438,693 samples; BRFSS 2019: 418,268 samples) and the National Health Interview Survey (NHIS 2021: 29,482 samples; NHIS 2020: 31,568 samples), we addressed missing data with nine state-of-the-art imputation methods and mitigated class imbalance via sampling strategies. Our customized stacked ensemble model achieved balanced accuracy of 72.56-76.12%, with corresponding AUROC scores of 79.59-82.29%. SHapley Additive exPlanations (SHAP) analysis, followed by clinical review, highlighted critical predictors, including regular medical check-ups, age, blood pressure, and indicators of mental health stress. These findings deliver a robust and interpretable framework for CKD risk stratification and provide actionable insights into its associated factors.

cs.LG↗

arXiv2025

Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025

The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to catalyze collaborative, small-group dialogue around critical, timely topics at the intersection of machine learning and healthcare. Each roundtable was moderated by a team of senior and junior chairs who fostered open exchange, intellectual curiosity, and inclusive engagement. The sessions emphasized rigorous discussion of key challenges, exploration of emerging opportunities, and collective ideation toward actionable directions in the field. In total, eight roundtables were held by 19 roundtable chairs on topics of "Explainability, Interpretability, and Transparency," "Uncertainty, Bias, and Fairness," "Causality," "Domain Adaptation," "Foundation Models," "Learning from Small Medical Data," "Multimodal Methods," and "Scalable, Translational Healthcare Solutions."

cs.LG↗

arXiv2025

Enhanced Pediatric Dental Segmentation Using a Custom SegUNet with VGG19 Backbone on Panoramic Radiographs

Pediatric dental segmentation is critical in dental diagnostics, presenting unique challenges due to variations in dental structures and the lower number of pediatric X-ray images. This study proposes a custom SegUNet model with a VGG19 backbone, designed explicitly for pediatric dental segmentation and applied to the Children's Dental Panoramic Radiographs dataset. The SegUNet architecture with a VGG19 backbone has been employed on this dataset for the first time, achieving state-of-the-art performance. The model reached an accuracy of 97.53%, a dice coefficient of 92.49%, and an intersection over union (IOU) of 91.46%, setting a new benchmark for this dataset. These results demonstrate the effectiveness of the VGG19 backbone in enhancing feature extraction and improving segmentation precision. Comprehensive evaluations across metrics, including precision, recall, and specificity, indicate the robustness of this approach. The model's ability to generalize across diverse dental structures makes it a valuable tool for clinical applications in pediatric dental care. It offers a reliable and efficient solution for automated dental diagnostics.

eess.IV↗

arXiv2023

Optimal EEG Electrode Set for Emotion Recognition From Brain Signals: An Empirical Quest

The human brain is a complex organ, still completely undiscovered, that controls almost all the parts of the body. Apart from survival, the human brain stimulates emotions. Recent research indicates that brain signals can be very effective for emotion recognition. However, which parts of the brain exhibit most of the emotions is still under-explored. In this study, we empirically analyze the contribution of each part of the brain in exhibiting emotions. We use the DEAP dataset to find the most optimal electrode set which eventually leads to the effective brain part associated with emotions. We use Fast Fourier Transformation for effective feature extraction and a 1D-CNN with residual connection for classification. Though 32 electrodes from the DEAP dataset got an accuracy of 97.34%, only 12 electrodes (F7, P8, O1, F8, C4, T7, PO3, Fp1, Fp2, O2, P3, and Fz) achieve 95.81% accuracy. This study also shows that adding more than 10 electrodes does not improve performance significantly. Moreover, the frontal lobe is the most important for recognizing emotion.

cs.LG↗

arXiv2023

Exploring Emotions in EEG: Deep Learning Approach with Feature Fusion

Emotion is an intricate physiological response that plays a crucial role in how we respond and cooperate with others in our daily affairs. Numerous experiments have been evolved to recognize emotion, however still require exploration to intensify the performance. To enhance the performance of effective emotion recognition, this study proposes a subject-dependent robust end-to-end emotion recognition system based on a 1D convolutional neural network (1D-CNN). We evaluate the SJTU\footnote{\href{https://en.wikipedia.org/wiki/Shanghai_Jiao_Tong_University}{Shanghai Jiao Tong University(SJTU)}} Emotion EEG Dataset SEED-V with five emotions (happy, sad, neural, fear, and disgust). To begin with, we utilize the Fast Fourier Transform (FFT) to decompose the raw EEG signals into six frequency bands and extract the power spectrum feature from the frequency bands. After that, we combine the extracted power spectrum feature with eye movement and differential entropy (DE) features. Finally, for classification, we apply the combined data to our proposed system. Consequently, it attains 99.80\% accuracy which surpasses each prior state-of-the-art system.

cs.HC↗