Large Language Models to Identify Social Determinants of Health in Electronic Health Records

Using Large Language Models to Identify Social Determinants of Health from Electronic Health Records Background and Research Motivation Social Determinants of Health (SDOH) have a significant impact on patient health outcomes. However, these factors are often incompletely recorded or missing in the structured data of Electronic Health Records (EHR)...

Generation of Synthetic Whole-Slide Image Tiles of Tumours from RNA-Sequencing Data via Cascaded Diffusion Models

Generation of Synthetic Whole-Slide Image Tiles of Tumours from RNA-Sequencing Data via Cascaded Diffusion Models

Generation of Synthetic Whole-Slide Images of Tumors from RNA Sequencing Data via Cascaded Diffusion Models A recent study published in Nature Biomedical Engineering, titled “Generation of Synthetic Whole-Slide Image Tiles of Tumours from RNA-Sequencing Data via Cascaded Diffusion Models,” has garnered significant attention. This research, conducte...

Deep Geometric Learning with Monotonicity Constraints for Alzheimer’s Disease Progression

Using Monotonicity-Constrained Deep Geometric Learning to Predict Alzheimer’s Disease Progression Background Introduction Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder that gradually leads to irreversible cognitive decline, eventually resulting in dementia. Early identification and progression prediction of this disease are c...

Modelling Dataset Bias in Machine-Learned Theories of Economic Decision-Making

Background Introduction Over the long term, normative and descriptive models have been trying to explain and predict human decision-making behavior in the face of risk choices such as products or gambling. A recent study discovered a more accurate human decision model by training Neural Networks (NNs) on a new large-scale online dataset called choi...

Using Deep Neural Networks to Disentangle Visual and Semantic Information in Human Perception and Memory

Differentiating Visual and Semantic Information in Human Perception and Memory Using Deep Neural Networks Introduction In cognitive science, the study of how humans recognize individuals and objects during perception and memory processes has long been of interest. Successful recognition of people and objects relies on matching representations gener...

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning

Background Introduction Cancer of Unknown Primary (CUP) is a type of malignant disease that is confirmed to be metastatic through histopathology but whose primary site cannot be identified using conventional baseline diagnostic methods. CUP presents significant diagnostic and therapeutic challenges in clinical practice and is believed to account fo...