Machine learning is increasingly integrated into clinical practice, with applications ranging
from pre-clinical data processing, bedside diagnosis assistance, patient stratification …

Medical data refers to health-related information associated with regular patient care or as
part of a clinical trial program. There are many categories of such data, such as clinical …

Pre-training (PT) followed by fine-tuning (FT) is an effective method for training neural
networks, and has led to significant performance improvements in many domains. PT can …

Pre-training (PT) has been used successfully in many areas of machine learning. One area
where PT would be extremely impactful is over electronic health record (EHR) data …

Abstract Machine learning-aided medical decision making presents three major challenges:
achieving model parsimony, ensuring credible predictions, and providing real-time …

Time series extrinsic regression (TSER) aims at predicting numeric values based on the
knowledge of the entire time series. The key to solving the TSER problem is to extract and …

Objective The accuracy of deep learning models for many disease prediction problems is
affected by time-varying covariates, rare incidence, covariate imbalance and delayed …

Disease progression trajectories can greatly affect the quality and efficacy of clinical
diagnosis, prognosis, and treatment. However, one major challenge is the lack of …

The use of artificial intelligence to target clinical problems has been applied as a revolution
in clinical decision-making. Also, artificial intelligence has been made possible in all parts by …

Deep learning models have demonstrated impressive capabilities in many settings including
computer vision, natural language generation, and speech processing. However, an …