# A Fair and Safe Usage Drug Recommendation System in Medical Emergencies by a Stacked ANN
## 2 Related Work  
From the existing literature, different health recommender systems (HRS) are available. Collaborative filtering utilizes past user behavior to examine similar profiles and determine preferences to make clear recommendations. A hospital recommendation system was proposed by [PERSON] et al. [14] based on the treatments, consulted physicians, hospitals, and patient health indicators of a patient. An alternative hybrid recommender system based on available information on family doctors and available patients was suggested [15].  
Various HRS help to support medical treatment and prognosis [16]. Recommendations made on content-based filtering are dependent on specific features only. Different features selected using rough set feature reduction can predict diabetes [17]. Content-driven models are used to evaluate radial doses and weights for elements in cancer treatments [18]. It is reported that content-based models achieve a better performance than traditional models in predicting the risk of heart attack [19].  
A model called iCARE uses collaborative filtering and hybrid learning to predict disease risk based on a patient's previous illnesses [20]. The risks of delivery for pregnant women can be predicted using a collaborative filtering algorithm that includes Mahalanobis distance and fuzzy membership [21]. Ontologies and methods of problem-solving are fundamental components of knowledge-based systems [22]. Based on the knowledge of users and products, knowledge-based filtering selects products that are suitable for users [23]. Meanwhile, hybrid systems combine different filtering approaches [24].  
Demographic filtering offers recommendations based on demographic data such as age, gender, nationality, and residency [25]. In medical emergencies such as the COVID-19 pandemic, older people have a higher risk of complications and contracting serious illnesses if they are untreated. Through information filtering, the HRS can handle such emergencies by collecting patient messages and recommending treatment [26]. With the help of the patient's demographic information, these messages for smoking cessation users used hybrid filtering to assess similarity.  
A semantic web is a fast-evolving technology that utilizes a content-based recommendation system with machine-readable annotations [27]. The social-based filtering algorithm considers information about an individual's neighborhood, along with similar tastes [28]. To prescribe the most appropriate treatment to patients, semantic clustering assesses the similarities between records, taking into account the patient's demographics, location, and medical complications [29].  
The DRS offers medicine based on patient reviews using sentiment analysis and feature engineering. The risk level classification identifies a patient's immune system and recommends medicines if the patient has a low immune system [30]. [PERSON] et al. [31] proposed GalenOWL, a semantic-driven online framework with the help of a specialist to manage drug recommendations based on the past profile of the patient. By considering worldwide standards such as ICD-10 and UNII, this framework converts clinical data and drug interactions to ontological terms. Cloud-assisted drug recommendation (CADRE) also considers the patient's side effects and shifts to the cloud to advance the quality of the patient's experience [32]. No particular DRS system was developed for the COVID-19 emergency. Therefore, we aimed to develop a DRS modeling framework by incorporating a stacked artificial neural network (ANN) for the fair and safe usage of drugs in pandemics.