The use of artificial intelligence within the healthcare sector is consistently growing. However, the majority of deep learning-based AI systems are of a black box nature, causing these systems to suffer from a lack of transparency and credibility. Due to the widespread adoption of medical imaging for diagnostic purposes, the healthcare industry frequently relies on methods that provide visual explanations, enhancing interpretability. Existing research has summarized and explored the usage of visual explanation methods in the healthcare domain, providing introductions to the methods that have been employed. However, existing reviews are frequently used for interpretable analysis in the medical field ignoring comprehensive reviews on Class Activation Mapping (CAM) methods because researchers typically categorize CAM under the broader umbrella of visual explanations without delving into specific applications in the healthcare sector. Therefore, this study primarily aims to analyze the specific applications of CAM-based deep explainable methods in the healthcare industry, following the PICO (Population, Intervention, Comparison, Outcome) framework. Specifically, we selected 45 articles for systematic review and comparative analysis from three databases—PubMed, Science Direct, and Web of Science—and then compared eight advanced CAM-based methods using five datasets to assist in method selection. Finally, we summarized current hotspots and future challenges in the application of CAM in the healthcare field.

In recent years, the knowledge graph has developed rapidly, but a single knowledge graph often fails to meet people's needs for actual use. Therefore, the multi-source knowledge embedding of knowledge graph has received extensive attention. Multi-source knowledge embedding was mainly divided into three steps: knowledge search, knowledge evaluation and knowledge fusion. The knowledge search was the basis of multi-source knowledge embedding. Knowledge evaluation was the guarantee of multi-source knowledge embedding. Knowledge fusion was the main means of multi-source knowledge embedding. Multi-source knowledge embedding can not only improve the performance of knowledge representation, but also improve the completeness of knowledge graph. At present, people mainly paid attention to the embedding of knowledge, ignoring the knowledge search and knowledge evaluation. This paper studied the whole process of knowledge embedding from the perspective of knowledge source, which was of great significance.

Missing values are an unavoidable problem for classification tasks of machine learning in medical data. With the rapid development of the medical system, large scale medical data is increasing. Missing values increase the difficulty of mining hidden but useful information in these medical datasets. Deletion and imputation methods are the most popular methods for dealing with missing values. Existing studies ignored to compare and discuss the deletion and imputation methods of missing values under the row missing rate and the total missing rate. Meanwhile, they rarely used experiment data sets that are mixed-type and large scale. In this work, medical data sets of various sizes and mixed-type are used. At the same time, performance differences of deletion and imputation methods are compared under the MCAR (Missing Completely At Random) mechanism in the baseline task using LR (Linear Regression) and SVM (Support Vector Machine) classifiers for classification with the same row and total missing rates. Experimental results show that under the MCAR missing mechanism, the performance of two types of processing methods is related to the size of datasets and missing rates. As the increasing of missing rate, the performance of two types for processing missing values decreases, but the deletion method decreases faster, and the imputation methods based on machine learning have more stable and better classification performance on average. In addition, small data sets are easily affected by processing methods of missing values.