Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the
challenges in drug discovery. Although traditional AI techniques generally have high …

Explainable AI (XAI) is a methodology that complements the black box of artificial
intelligence, and its necessity has recently been highlighted in various fields. The purpose of …

Objective The manual recording of electronic health records (EHRs) by clinicians in the
emergency department (ED) is time-consuming and challenging. In light of recent …

Objectives: Ischemic heart disease (IHD) is a significant contributor to global mortality and
disability, imposing a substantial social and economic burden on individuals and healthcare …

A computer-assisted coding tool could alleviate the burden on medical staff to assign ICD
diagnosis codes to discharge summaries by utilising deep learning models to generate …

To address the problems of the limited prediction accuracy and poor explainability of the
deep learning (DL) ena-bled spectrum prediction models, a spectrum prediction method …

Local explanation of machine learning (ML) models has recently received significant
attention due to its ability to reduce ambiguities about why the models make specific …

Large, varied, and time-evolving data sources can be observed across many domains and
present a unique challenge for classification problems, in which traditional machine learning …

Abstract The Anatomical Therapeutic Chemical (ATC) system assigns unique codes to drugs
for tracking, aiding in prescription, public health policies, and research. Different classifiers …

Abstract The Anatomical Therapeutic Chemical (ATC) system assigns unique codes to drugs
for tracking, aiding in prescription, public health policies, and research. Different classifiers …