Making medication prescriptions in response to the patient's diagnosis is a challenging task.
The number of pharmaceutical companies, their inventory of medicines, and the …

Background Clinical medicine offers a promising arena for applying Machine Learning (ML)
models. However, despite numerous studies employing ML in medical data analysis, only a …

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 …

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

Exploiting the correlations between medical features is essential to the success of
healthcare data analysis. However, most existing methods are either suffering large …

Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a
few visits per patient and a few observations per disease can be exploited. Although meta …

With the wide application of electronic health records (EHR) in healthcare facilities, health
event prediction with deep learning has gained more and more attention. A common feature …

We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on
Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources …

Disease prediction based on patients' historical admission records is an essential task in the
medical field, but current predictive models often lack interpretability, which is a critical …

Due to the insufficiency of electronic health records (EHR) data utilized in practical diagnosis
prediction scenarios, most works are devoted to learning powerful patient representations …