Application of Artificial Intelligence Methods Depending on the Tasks Solved during COVID-19 Pandemic

: Health systems challenges that emerged during the COVID-19 pandemic, such as a lack of resources and medical staff, are forcing solutions which optimize healthcare performance. One of the solutions is the development of clinical decision support systems (CDSS) based on artificial intelligence (AI). We classified AI-based clinical decision-supporting systems used during the pandemic and evaluated the mathematical algorithms present in these systems. Materials and methods: we searched for articles relevant to the aim of the study in the Scopus publication database. Results: depending on the purpose of the development a clinical decision support system based on artificial intelligence during pandemic, we identified three groups of tasks: organizational, scientific and diagnostic. Tasks such as predicting of pandemic parameters, searching of analogies in pandemic progression, prioritization of patients, use of telemedicine are solved for the purposes of healthcare organization. Artificial intelligence in drugs and vaccine development, alongside personalized treatment programs, apply to new scientific knowledge acquisition. Diagnostic tasks include the development of mathematical models for assessing COVID-19 outcomes, prediction of disease severity, analysis of factors influencing COVID-19 complications. Conclusion: artificial intelligence methods can be effectively implemented for decision support systems in solving tasks that face healthcare during pandemic.


Introduction
In the context of the spread of COVID-19, national health systems around the world are under serious pressure. Most countries face the same challenges: lack of medical staff, overcrowding of hospitals, shortage of equipment and facilities for diagnosis and treatment, forced suspension of routine services, etc. According to the World Health Organization, about 40% of patients have a mild disease and do not require hospitalization, 40% of patients have moderate symptoms that may require hospitalization, 15% of patients have a severe disease requiring oxygen therapy and other medical treatment events in the hospital, and 5% of patients develop extremely severe pathologies requiring mechanical ventilation [1].
Another important aspect in the development of the pandemic is the high risk of infection for healthcare workers in primary healthcare centers and hospitals. Up to 10% of reported disease cases in China and up to 9% of all disease cases in Italy are among healthcare workers. In some EU countries, this rate reaches 26% [1,2].
With a sharp increase in the number of patients with coronavirus, a high incidence among medical workers has revealed the unpreparedness (unreadiness) of healthcare sys-tems in many countries to work in a pandemic. In this situation, the introduction of artificial intelligence (AI) methods in the organization of medical care will significantly reduce the burden on medical personnel and help optimize resources.
Currently, there is a technological potential for the introduction of modern information technologies in the work of medical facilities in order to improve the quality of medical care. The aim of this study was to identify the possibilities of using artificial intelligence for clinical decision supporting systems (CDSS) during a pandemic.

Materials and Methods
The relevance of CDSS is evidenced by the results of search queries to the Scopus publication indexing system. Thus, the clinical decision supporting system query procured a total of 56,738 articles. Further, within the framework of the obtained search results, an additional search was carried out for information related to the application of machine learning and artificial intelligence methods within the framework of the CDSS. Based on the additional phrase "machine learning", 7825 articles were filtered. Further, a pair of synonyms was added to the search query, describing belonging to research within the COVID-19 and SARS-CoV-2 pandemics with additional filtering by 2020 and 2022 publication years. After submitting a request to the indexing systems, we received 724 articles that were analyzed to determine the goal relevance for the review. Finally, only 92 original research articles were included finally to the review paper.
The artificial intelligence methods found during the search were classified depending on the direction of the practical problem being solved within the construction and application of the COVID-19-related CDSS.
The citation indexes of the journals were not taken into account when searching for the original research papers. This could be the possible limitation of the study.

Results and Discussion
Automation of processes aimed at solving the problems of medicine and healthcare during a pandemic has been investigated in hundreds of works. At the same time, a variety of publications can be summarized for solving organizational, scientific and diagnostic problems ( Figure 1).

Organizational task.
AI-based CDSS, solving the problems of medical care organizing. The first group included tasks related to the organization of medical care in a pandemic, namely patient routing, resource administration (drugs, bed resources, medical devices), predicting the number of cases, and the prediction of recovery. It is for this group of tasks that the use of AI methods is the most promising. An analysis of the publications made it possible to identify the following areas of AI application:

Predicting the Global Parameters of the Pandemic
The aim of such system is to make decisions in the field of planning the load on the medical infrastructure and to make decisions on its adjustment, taking into account changing predicted values. It is not a classical AI-based CDSS for the management of individual patients, however, it serves to plan and administer global health parameters in a pandemic. These predictions make it possible to reserve the need to create medical resources to counter a pandemic and prevent situations in which the existing healthcare system is unable to accept patients in the quantities that arise during the development of a pandemic.
Actually, almost all of these methods are aimed at analyzing the time series of morbidity/recovery/mortality parameters to construct predictive models. Another specific task is to discover the country regions with similar parameters of pandemic development via clusterization methods [3]; DNN and decision trees [4] describe such areas by some common features and build analogies for the pandemic development prognosis.
Moreover, we include in this group a method of monitoring the local environment to detect violations of the regime and the presence of symptoms, which also allow us to monitor the overall situation, deal with violations of the regime and predict their extent and consequences. The local environment (humidity, temperature, wind) influence on the number of COVID-19 cases also can be analyzed by AI methods [5].

Research Identifies Similar Variants of the Disease Development when Comparing the Dynamics of Patients' Treatment
Based on these, data comparison is made between countries in terms of the course of the disease in infected individuals.
For solving this task, clustering methods are applied, such as K-means, or Fuzzy PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) and VIKOR (from Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje, that means: Multicriteria Optimization and Compromise Solution) methods. The input parameters are tied to the problem being solved (depending on the type of searched analogy and the parameters of the region or patients) [3,8].
3.1.3. General Review Papers on the Use of Telemedicine and AI within it Contain a Minimum of Specific Information on Solutions within the AI-Based CDSS These papers are the most significant ones in terms of the number of works, therefore they are mentioned as a part of this review. This section included a number of works that, with some specifics (algorithms, results), could be attributed to other methods [23][24][25] as well as specific software solutions without describing their content and implementation [26]. These works confirm the applicability of AI methods for data analysis, but they are not allowed to be compared with similar tasks. There is the task of diet correction or disease monitoring using telemedicine when the in-person treatment is impossible under the restrictions associated with COVID-19 [24,26].
3.1.4. The Task to Prioritize Patients for Selected Treatments Based on Their Individual Characteristics (e.g., Blood Types) and the Severity of the Current Condition [27][28][29] The input data mainly includes diagnostic parameters (laboratory, x-ray data). Due to high workload of clinical laboratories an automated methods are needed even for such a trivial task as blood type analysis and machine learning methods have a great potential in this field. In paper [28], a deep convolutional neural network was trained to classify images of a blood test samples after applying specific antigens to determine the blood types [28]. An estimation of risk factors of COVID-19 severity allows us to prioritize patients providing them a better treatment. Machine learning methods such as regression, random forests (RF), support vector machines (SVM), and artificial neural networks (ANN), which can solve feature importance problem along with predictive model construction are used [27,29].
We include in this group methods that allow us to predict disease severity thus aiding us in better resource planning and allocation. Fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test [30]. Accurate prediction of COVID-19 severity is possible after analyzing clinical data with 5-layer deep neural network (DNN) [31]. Deep learning models with an autoencoder-based approach estimate the spread of the disease and predict the survival possibilities of the quarantined patients in isolation [32].

Research Tasks
The second group includes research tasks aimed at obtaining new knowledge through the analysis of complexly structured data, a large amount of heterogeneous data and intended to predictive assessment of health status, improving the quality of medical care. We included the following subtasks in this group: Application of Machine Learning Methods in DNA Analysis for Mutation Detection, Personalized Diagnosis and Treatment, Treatment Efficacy Prediction [33,34], Vaccine Development [35] and Drug Development [36] The input data are either a sequence of genes [37,38] or the structural chemical composition of the studied substances in the form of a sequence [36]. For example, in [39] 3Clike protease inhibitors were created by a novel generative adversarial network. For gene analysis, statistical methods such as Markov models or Monte Carlo methods is used to build sequences from available samples [34,39].
The output is possible for the next elements of the gene sequence, genomic biomarkers that affect the course of diseases, or options for potentially effective drugs.

Diagnostics Tasks
This group includes approaches aimed at assisting the physician in making a diagnosis, prescribing drugs, and providing hardware assistance. Despite the objective importance of diagnostic tasks, we left them only the third place in our classification, since the decision about the patient's condition, the conclusion about treatment administration, in any case, should be made by the physician. There are following subsections within the framework of AI participation in diseases diagnostics. The best performance was observed in models based on clinical, laboratory and radiological variables [40]. Within the pandemic, these approaches have been used to predict the outcome of COVID-19 and have shown similar results. However, most of these studies do not include continuous physiological signals (e.g., ventilator parameters, vital signals) for prediction and do not work in real-time. Such information would significantly improve the characteristics of predicting in terms of expanding the classes of problems being solved (not only a single prediction, but also monitoring in dynamics) [41]. A subspecies of diagnostic application is an approach for predicting the development of a disease and developing personalized patient management and treatment plans based on the success of previous patients with a similar prognosis [42].
An analysis of the solutions found showed that the created systems are mainly aimed at predicting survival/death (the metric of belonging to a class can be used to assess the risk of death). In the framework of solving these problems, statistical regression methods and their modifications are most often encountered [27,[41][42][43][44][45][46][47][48]. There are also decision trees [10,27,49], K nearest neighbors (KNN) [50], and SVM [46,48]. Neural network algorithms are found, but relatively infrequently due to small amount of annotated data [27]. Methods for constructing decision-making systems based on several classifiers and combining their decisions (ensemble methods [46]) have a mean F1 score of more than 0.8. At the same time, within the framework of the study, both for this one and for other classes, the data sets differ greatly. Standard datasets used simultaneously in many significant works have not been identified. This leads to the fact that quality metrics can be used for comparison within the same work but are poorly applicable for comparison within different works on different data. This makes it possible to assess the general applicability of solutions, but it does not allow us to clearly identify the best ones.
In addition, the analysis showed the predominance of systems for constructing a predictive model for a patient without real-time monitoring of his condition. This can be seen from the prevailing use of input data such as age, gender, chronic diseases (clearly static parameters within the disease), blood test results (not monitored in real time, although it is possible to conduct repeated studies and track dynamics).
The latest works show that AI-based analysis of data from wearable devices (with LTSM-based autoencoder) [51], biosensors (with Fuzzy-based decision tree) [52] and audio data (with Deep learning-enabled tracking tool using gated recurrent units) [53] can accurately differentiate between healthy and COVID-19 infected people and predict recovery mode. Furthermore, custom gradient boosting ensemble learning method makes it possible to predict infection in real time after analyzing the data from wearable devices [54].
The input parameters are the results of radiographs or computed tomography. The output results within the solutions found are the classification results for the absence of pneumonia/non-COVID-19 pneumonia/COVID-19 associated pneumonia. A solution has been found, which allows for a full cycle of diagnostics and monitoring of patient treatment [64]. However, the analysis of CT results is described without a detailed description of other aspects. For this reason, this work is assigned to this class, and not allocated to an independent class.
Adding more information to CT scans such as acoustic data or results of RT-PCR tests gives high diagnostic accuracy after analyzing with Deep Learning-assisted Multi-modal Data Analysis [77] and EfficientNet-B0 initialized on ImageNet pretrained weights [78], respectively. [79] CT results are used as input data. Data are manually processed to extract parameters for subsequent analysis and predictive model construction. The outputs of the system are not a mortality prediction, but a metric describing the severity of the disease and the possibility of complications.
In addition, a system for predicting complications during treatment (the occurrence of acute respiratory distress syndrome (ARDS)) [92] is singled out separately. Initially, the work was included in the review as an example of a system for monitoring the patient's condition and controlling the ventilation system, however, after a detailed study, it was found that it was aimed rather at identifying a dangerous complication and signaling about its occurrence. For this reason, the work is moved to the existing class. Works directly related to the management of systems for the treatment and monitoring of patients with COVID-19 are not found in the leading indexed publications.
We make a comparative table on the basis of the identified classes of application of AI methods in the construction of CDSS (Table 1) Recall-shows what proportion of objects that actually belong to the positive class is predicted correctly; -F1 is a metric that combines precision and recall with equal priority.  [14] Interest in search terms "Corona", "COVID-19", "Coronavirus", "Antiseptic sale", "Antiseptic purchase", "Hand washing", "Hand sanitizer", "Ethanol", "Antiseptic" in Persian for the previous day The incidence of COVID-19 the day before (Iran). Similar parameters for the current day, of particular importance, the incidence of COVID-19 (in Iran).

Conclusions
The current state of science makes it possible to adapt existing AI methods for solving new problems quickly, in particular those that have arisen during the COVID-19 pandemic. There are three areas of AI application for making medical decisions of the most diverse focus on work, from an individual patient to obtaining predictions on a global scale.
The most applicable mathematical algorithms for solving the organizational problems during a pandemic include: simple recurrent neural network, long-term short-term memory, gated repeating units, variational autoencoder, support vector regression, linear regression, K-means, logistic regression, neural networks, etc.
Variations of hidden Markov models, Monte Carlo method, GAN network are the best analytical methods for scientific problems.
Clinical decision-supporting systems based on logistic regression, support vector machine, nearest K neighbors, random forest, gradient boosting, ensembles, Gaussian process, linear models, naive Bayes, nearest neighbors, decision tree, discriminant analysis, etc. are used for diagnostic purposes.
Despite the overall positive trend, a number of shortcomings can be identified that can be considered the promising areas in the framework of the creation of AI-compiled CDSS in the context of counteracting COVID-19 and in a more general sense: The vast majority of the found solutions are not aimed at processing the patient's current indicators in real time. The papers consider the analysis of the patient's input indicators, which, as a rule, are static in the context of a pandemic-age, gender, the presence of chronic diseases. On the basis of the static information, a single prognosis/diagnosis is given, on which basis a decision is made on hospitalization/severity of the condition; however, the results of continuous monitoring were not found in the works.
The analyzed works do not present CDSS aimed at managing patient support systems. The work only makes it possible to monitor the operation of the ventilator system, however, data on the management of this system depending on the patient's condition, adaptation to them are not presented. This shows the prospects of research in the development of such systems that would reduce the burden on physicians, allowing them to monitor the patient's condition and take part in the validation of decisions made by the system and to launchA appropriate treatment.