# XAI-Based Clinical Decision Support Systems: A Systematic Review
## 2 Related Work
### Non-Knowledge-Based CDSS  
With the explosion of data and specialized knowledge, the amount of information that must be processed to make clinical decisions is growing astronomically. By learning \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Paper**} & \multicolumn{2}{c}{**Functions**} & \multirow{2}{*}{**Applied Domain**} \\ \cline{5-6}  & & & \begin{tabular}{c} **Diagnosis and** \\ **Treatment** \\ \end{tabular} & & \begin{tabular}{c} **Alert and** \\ **Monitoring** \\ \end{tabular} & & & **Applied Domain** \\ \hline \multirow{8}{*}{BRB} & & [18] & ✓ & & Tuberculosis \\ \cline{2-6}  & & [16] & ✓ & & Heart Failure \\ \cline{2-6}  & \begin{tabular}{c} An extension of if–then rules utilizing \\ confidence rules that include confidence \\ levels to represent knowledge \\ \end{tabular} & & [19] & ✓ & & Acute Coronary \\ \cline{2-6}  & & [14] & ✓ & & Measles \\ \cline{2-6}  & & [20] & ✓ & & Lymph Node Cancer \\ \cline{2-6}  & & [15] & & ✓ & COVID-19 \\ \hline \multirow{8}{*}{Decision Tree} & & [17] & & ✓ & Psychogenic Pain \\ \cline{2-6}  & \begin{tabular}{c} Represent knowledge based on a tree \\ structure with a hierarchy of knowledge \\ relationships \\ \end{tabular} & [22] & ✓ & & & Glaucoma \\ \cline{2-6}  & \begin{tabular}{c} [23] \\ \end{tabular} & ✓ & & Thyroid \\ \cline{2-6}  & & [24] & ✓ & & Allergies \\ \hline \multirow{8}{*}{Decision Tree} & & [38] & ✓ & & Glaucoma \\ \cline{2-6}  & & [25] & ✓ & Liver Disease \\ \cline{2-6}  & & [26] & ✓ & Breast Cancer \\ \cline{2-6}  & & [27] & ✓ & Infectious Diseases \\ \cline{2-6}  & & [29] & ✓ & Angina Pectoris \\ \cline{2-6}  & \
\begin{tabular}{c} Utilizes probabilities based on Naive Bayes \\ to classify data \\ \end{tabular} & [30] & ✓ & Respiratory Diseases \\ \cline{2-6}  & & [31] & ✓ & Lymph Node Cancer \\ \cline{2-6}  & & [32] & ✓ & & Dental Hygiene \\ \cline{2-6}  & & [33] & ✓ & Hepatitis \\ \cline{2-6}  & & [28] & & ✓ & Diabetes \\ \hline Nearest neighbors &
\begin{tabular}{c} Determines the class of a new instance \\ using the attributes of the nearest neighbor \\ \end{tabular} & [35] & ✓ & & Melanoma Diagnosis \\ \cline{2-6}  & & [36] & ✓ & & Diagnostics \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Knowledge-based CDSSs. on its own like a human using massive amounts of data, deep learning and AI, which are based on artificial neural networks, can be used to support clinical decision-making. These methods analyze patterns in patient data to draw associations between symptoms and diagnoses. Moreover, deep learning and AI can be used to analyze various data, including text, images, videos, audio, and signals, enabling the development of non-knowledge-based CDSSs that can understand the overall clinical situation and context. The first step toward a non-knowledge-based CDSS begins with analyzing images and using them to make clinical decisions. A convolutional neural network (CNN) [41] that trains image patterns by mimicking the structure of the human optic nerve has been used to diagnose obstructive sleep apnea by learning high-order correlation features between polysomnography images and their labels [42; 43], and an automated system has been proposed to optimize patient satisfaction by analyzing patients' experiences with ambulance transport services with a combined model of CNN and word embeddings [44]. Similarly, a technique for diagnosing melanoma using a single CNN trained on a dataset of clinical images has been introduced [45]. There are also a number of cases of recurrent neural networks (RNNs) that can handle time series data. Electronic health records (EHRs) data, which is a digital version of a patient's paper chart, are good candidates for using RNNs [46] because these data provide clinical records with temporal information. A previous study [47] applied RNNs to the EHR data of heart failure patients to predict heart failure, and the method outperformed machine learning methods such as SVMs [48], MLPs [49], logistic regression [50], and KNNs [51].