With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

Abstract Machine learning is disruptive. At the same time, machine learning can only
succeed by collaboration among many parties in multiple steps naturally as pipelines in an …

Deep learning methods especially recurrent neural network based models have
demonstrated early success in disease risk prediction on longitudinal patient data. Existing …

Multimodal electronic health record (EHR) data are widely used in clinical applications.
Conventional methods usually assume that each sample (patient) is associated with the …

Deep learning-based health status representation learning and clinical prediction have
raised much research interest in recent years. Existing models have shown superior …

While recent developments of deep learning models have led to record-breaking
achievements in many areas, the lack of sufficient interpretation remains a problem for many …

We present Clinical Prediction with Large Language Models (CPLLM), a method that
involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical …

Accurate and explainable health event predictions are becoming crucial for healthcare
providers to develop care plans for patients. The availability of electronic health records …

One unintended consequence of the Electronic Health Records (EHR) implementation is the
overuse of content-importing technology, such as copy-and-paste, that creates “bloated” …

Clinical predictive models often rely on patients' electronic health records (EHR), but
integrating medical knowledge to enhance predictions and decision-making is challenging …