AIMC Topic: Humans

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CareSleepNet: A Hybrid Deep Learning Network for Automatic Sleep Staging.

IEEE journal of biomedical and health informatics
Sleep staging is essential for sleep assessment and plays an important role in disease diagnosis, which refers to the classification of sleep epochs into different sleep stages. Polysomnography (PSG), consisting of many different physiological signal...

Identifying Associations Between Small Nucleolar RNAs and Diseases via Graph Convolutional Network and Attention Mechanism.

IEEE journal of biomedical and health informatics
Research has shown that small nucleolar RNAs (snoRNAs) play crucial roles in various biological processes, and understanding disease pathogenesis by studying their relationship with diseases is beneficial. Currently, known associations are insufficie...

Self-Supervised Pre-Training via Multi-View Graph Information Bottleneck for Molecular Property Prediction.

IEEE journal of biomedical and health informatics
Molecular representation learning has remarkably accelerated the development of drug analysis and discovery. It implements machine learning methods to encode molecule embeddings for diverse downstream drug-related tasks. Due to the scarcity of labele...

Weakly Supervised Classification for Nasopharyngeal Carcinoma With Transformer in Whole Slide Images.

IEEE journal of biomedical and health informatics
Pathological examination of nasopharyngeal carcinoma (NPC) is an indispensable factor for diagnosis, guiding clinical treatment and judging prognosis. Traditional and fully supervised NPC diagnosis algorithms require manual delineation of regions of ...

BP-Net: Monitoring "Changes" in Blood Pressure Using PPG With Self-Contrastive Masking.

IEEE journal of biomedical and health informatics
Estimating blood pressure (BP) values from physiological signals (e.g., photoplethysmogram (PPG)) using deep learning models has recently received increased attention, yet challenges remain in terms of models' generalizability. Here, we propose takin...

tDKI-Net: A Joint q-t Space Learning Network for Diffusion-Time-Dependent Kurtosis Imaging.

IEEE journal of biomedical and health informatics
Time-dependent diffusion magnetic resonance imaging (TDDMRI) is useful for the non-invasive characterization of tissue microstructure. These models require densely sampled q-t space data for microstructural fitting, leading to very time-consuming acq...

Dietary Assessment With Multimodal ChatGPT: A Systematic Analysis.

IEEE journal of biomedical and health informatics
Conventional approaches to dietary assessment are primarily grounded in self-reporting methods or structured interviews conducted under the supervision of dietitians. These methods, however, are often subjective, inaccurate, and time-intensive. Altho...

In-Home Gait Abnormality Detection Through Footstep-Induced Floor Vibration Sensing and Person-Invariant Contrastive Learning.

IEEE journal of biomedical and health informatics
Detecting gait abnormalities is crucial for assessing fall risks and early identification of neuromusculoskeletal disorders such as Parkinson's and stroke. Traditional assessments in gait clinics are infrequent and pose barriers, particularly for dis...

Predicting adverse pregnancy outcome in Rwanda using machine learning techniques.

PloS one
BACKGROUND: Adverse pregnancy outcomes pose significant risk to maternal and neonatal health, contributing to morbidity, mortality, and long-term developmental challenges. This study aimed to predict these outcomes in Rwanda using supervised machine ...