OBJECTIVE: The full fine-tuning paradigm becomes impractical when applying pre-trained models to downstream tasks due to significant computational and storage costs. Parameter-efficient fine-tuning (PEFT) methods can alleviate the issue. However, sol...
OBJECTIVE: In the attempt of early diagnosis of Alzheimer's Disease, varying forms of medical records of multiple modalities are gathered to seize the interaction of multiple factors. However, the heterogeneity of multimodal data brings a challenge. ...
OBJECTIVE: Deep learning approaches have demonstrated significant potential in predicting temporal health events in recent years. However, existing methods have not fully leveraged the complex interactions among comorbidities and have overlooked imba...
Adverse Drug Reactions (ADRs) during pregnancy pose significant risks to both the mother and the fetus. Conventional approaches to predict ADR are inadequate due to ethical restrictions that prevent performing medication studies in pregnant women, le...
OBJECTIVE: This study uses probabilistic independence to disentangle patient-specific sources of disease and their signatures in Electronic Health Record (EHR) data.
BACKGROUND: Understanding how clinicians arrive at decisions in actual practice settings is vital for advancing personalized, evidence-based care. However, systematic analysis of qualitative decision data poses challenges.
OBJECTIVE: Considering the radiation hazard of X-ray, safer, more convenient and cost-effective ultrasound methods are gradually becoming new diagnostic approaches for scoliosis. For ultrasound images of spine regions, it is challenging to accurately...
OBJECTIVE: Scientific publications are essential for uncovering insights, testing new drugs, and informing healthcare policies. Evaluating the quality of these publications often involves assessing their Risk of Bias (RoB), a task traditionally perfo...
OBJECTIVE: Encoder-only transformer-based language models have shown promise in automating critical appraisal of clinical literature. However, a comprehensive evaluation of the models for classifying the methodological rigor of randomized controlled ...
OBJECTIVES: Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based cancer subtyping, often simp...