Actual Data Research Articles

Predictive Modeling of COVID-19 Intensive Care Unit Patient Flows and Nursing Complexity: A Monte Carlo Simulation Study.

This study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status. The simulation was performed 1000 times to generate a scenario, and the mean outcome was compared with the observed outcome. The model also allowed for a 7-day forecast of ICU occupancy. The simulation output had a good fit with the actual data, with an R2 of 0.998 and a root mean square error of 0.22. The study demonstrated the usefulness of the Monte Carlo simulation model for predicting the demand for ICU beds and could help optimize resource allocation during a pandemic. The model's extrinsic validity was confirmed using open data from the French Public Health Authority. This study provides a valuable tool for healthcare systems to anticipate and manage surges in ICU demand during pandemics.

Model-Assisted Muti-source Fusion Hypergraph Convolutional Neural Networks for intelligent few-shot fault diagnosis to Electro-Hydrostatic Actuator

Electro-Hydrostatic Actuator (EHA) is a critical electro-hydraulic actuator system widely used in aerospace equipment. To ensure its normal operation, the intelligent fault diagnosis of the EHA system has gained increasing attention. However, the EHA exhibits strong nonlinearity, high structural complexity, and difficulty obtaining fault samples. A Model-Assisted Multi-source Fusion Hypergraph Convolutional Neural Network (MAMF-HGCN) is proposed to address the few-shot intelligent fault diagnosis of EHA. Specifically, the fault data obtained from the hydraulic simulation model is used to establish the relationship among each channel signal. This assists in constructing a hypergraph structure for actual multi-source fault data under limited samples. Each node in the hypergraph employs message transmission to fuse signals from different channels. Subsequently, the hypergraph data are input into the constructed hypergraph convolutional neural network to perform fault classification. Finally, validating the EHA hydraulic system test rig from Nanjing University of Science and Technology illustrates the method’s effectiveness in diagnosing hydraulic system problems under limited fault samples.

Event-Triggered Federated Learning for Fault Diagnosis of Offshore Wind Turbines With Decentralized Data

Rapid developments of offshore wind industry offer a strong demand opportunity for offshore wind turbine remote diagnosis. As offshore wind turbines are often located in harsh and communication-constrained environments, the collection and transmission of data is severely restricted, which poses a serious challenge to the conventional centralized diagnostic paradigm that relies on data aggregation. To address this challenge, we propose a novel event-triggered federated learning framework for decentralized fault diagnosis of offshore wind turbines. Specifically, federated learning is first employed to learn decentralized local knowledge from geographically distributed offshore wind turbines, so that the communication objects are transformed from massive raw data into learned parameters, thereby relieving the communication burden. Then, we design an event-triggered communication mechanism and incorporate it into federated learning, the core of which is to modify the communication requirement from uploading all trained parameters periodically to communicating only when necessary. The proposed framework is verified by a real-world offshore wind turbine dataset from six large wind farms in China. An ablation study shows that the proposed framework can maintain high diagnostic performance while reducing communication costs. A comprehensive comparison based on three benchmark models demonstrates that the proposed framework can reduce the communication burden by up to 63% while obtaining better diagnostic performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study was motivated by the problem of collaborative diagnosis of distributed offshore wind turbines under the constraints of data privacy and communication overhead. The method employs a federated learning-based fault diagnosis framework, which permits to obtain global fault diagnosis knowledge without aggregating raw data scattered in each end device, thus avoids the risk of data leakage. Moreover, a strategy integrating parameter variation and accuracy gain is designed to avoid communication redundancy for collaborative training. The practicability and superiority of our proposed framework is demonstrated using extensive experiments against actual industrial data collected from six offshore wind farms.

Elimination of Random Mixed Noise in ECG using Convolutional Denoising Autoencoder with Transformer Encoder.

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.

Forecasting Türkiye's International Tourism Demand

The structure of tourist demand is sensitive since it is very easily affected by the consequences of economic, political, and social crises. Since the limited ability to increase tourism supply, it is crucial to analyze the demand structure and develop suitable strategies. This outcome can only be achieved by an accurate and effective demand prediction. There is no singular approach that ensures success in demand forecasting. Hence, in order to estimate demand accurately, it is advisable to create many models and choose the one with the lowest error rate. This study aimed to develop the best-performing prediction model by using monthly data of international visitors who visited Türkiye from January 2002 to August 2023 and stayed in Tourism Ministry-certified accommodations. Within this framework, the data was first analyzed to identify the trend and seasonal component. Afterwards, various models were employed including Naive III, simple moving average, double moving average, seasonal exponential smoothing, and artificial neural networks. The data generated by these models has been analyzed by comparing it with the actual data from the last 24 months, using MAPE and RMSE results. According to the research findings, it has been determined that artificial neural networks produce the most accurate results.

Forecasting individual bids in real electricity markets through machine learning framework

With the increasing uncertainty caused by the complexity of the world’s energy environment and the increasing penetration rate of renewable energy, it is significant to estimate the future operation of power markets in advance. Forecasting individual bids in spot electricity markets is a promising new method for achieving so, but it has not been fully studied due to the difficulty of forecasting a bid function. The idealization of existing optimization-based models decreases their practical effects in real markets. Thus, we propose a scalable forecasting framework that incorporates several customized state-of-art machine learning methods according to the characteristics of the bidding data. First, several low-rank approximation algorithms are customized to encode the high-dimensional bidding curves into low-dimensional feature spaces and reconstruct them from the predicted feature space. Second, a transformer-based multidimensional time series prediction algorithm is proposed to predict the bidding feature based on both related factors and historical bidding records. To appropriately evaluate the performances of the forecasting methods, we introduce a dynamic criterion based on the economic implications of bids. The comprehensive framework is tested based on actual market data from the Australian national electricity market, and in the empirical example, the feasibility and effectiveness of the proposed framework are demonstrated.

Inversion of Boreal Forest Height Using the CRITIC Weighted Least Squares Three-Stage Temporal Decorrelation Iterative Algorithm

The inversion of forest height using the RVoG (Random Volume over Ground) model is susceptible to overestimation or underestimation due to three error sources, propagating inaccuracies to the estimated forest height. Furthermore, variations in the height and density of the scenario could impact how well the RVoG three-stage inversion performs. This work utilizes the L-band single-baseline full polarization interferometric dataset as its basis. It optimally applies the CRITIC (Criteria Importance Through Intercriteria Correlation) method to the first stage of a three-stage process. This approach aims to overcome the issues mentioned above and enhance the accuracy of forest parameter estimation. A CRITIC weighted least squares temporal decoherence iterative algorithm is also proposed for the characteristics of the spaceborne data, in combination with the temporal decoherence algorithm of previous research. The proposed approach is tested and applied to both simulated and actual data. The optimization approach is first assessed using four simulated datasets that simulate coniferous forests with different densities and heights. The preliminary findings suggest that optimizing the complex coherence fitting process through the weighted least squares method enhances the accuracy of ground phase estimation and, consequently, improves the accuracy of the three-stage approach for inverting forest height. The ground phase estimation results for low forest height consistently remained within 0.02 rad, with a root mean square error (RMSE) below 0.05 rad, and no saturation occurred with increasing forest density. The enhanced algorithm outperforms the traditional technique in terms of accuracy in ground phase estimation. Subsequently, the optimized approach is applied to ALOS-2 spaceborne data, proving more successful than the conventional algorithm in reducing the RMSE of forest height. The findings illustrate the method’s superior inversion performance, obtaining an accuracy exceeding 80% in both the test and validation sets. The validation set’s RMSE is approximately 2.5 m, and the mean absolute error (MAE) is within 2 m. Moreover, it is observed that to counteract the uncertainty in temporal decoherence induced by climate change, a larger temporal baseline necessitates a larger random motion compensation term and phase offset term.

Uncertainty assessment of aquifer hydraulic parameters from pumping test data

Data from pumping tests is a noisy process, and therefore, performing the pumping test numerous times will not get the same drawdown values. As a consequence, various pumping experiments lead to different values for aquifer parameter estimates. The data of pumping tests are usually analyzed using traditional methods (aquifer tests and AQtesolv software), which depend on trial and error technique. During these methods, non-unique values of hydraulic parameters are selected, which usually have a high level of uncertainty. Uncertainty must be taken into account in determining aquifer parameters, especially when using groundwater models for decision makers. The main goal of this study is to build a comprehensive tool for quantifying uncertainty associated with hydraulic parameter estimation from different pumping test conditions for fully penetrating wells in confined and semi-confined aquifers. To achieve the previous objective, a FORTRAN code was developed to apply the Markov Chain Monte Carlo (MCMC) algorithm using different likelihood functions (exponential, inverse, and log). This developed tool can be used to detect the most probable range of aquifer parameters that are consistent with pumping test data with a high degree of confidence. The tool was successfully used to several hypothetical cases to demonstrate the uncertainty in the quantification of aquifer parameters and compare the findings to the standard method's results. Also, the concept was verified numerically (using Modflow program) with satisfactory results using a hypothetical case with well-known aquifer parameters. Finally, the tool was applied for actual pumping test data with good results.

Digital twinning of temperature fields for modular multilayer multiphase pipeline structures

The temperature field of oil and gas wells in the field of petroleum engineering presents a core problem and challenge in the digital twin framework due to its ultra-long-distance and highly variable structural characteristics. The varying wellbore cross-sectional structures with depth make it difficult to establish an effective and generalized analytical model for heat transfer. In this study, we propose, for the first time, a method to automate the construction of multi-layered and multi-component heat transfer models by using a general computational model based on non-steady-state single-phase structural modules. This method enables the automated generation of complex multi-layered and multi-component heat transfer models, thereby achieving the construction of a generalized model for temperature field characterization with varying wellbore cross-sectional structures over ultra-long distances. Utilizing this modeling approach, we validate the proposed method through case studies using actual wellbore temperature field data. The results demonstrate the lightweight and efficient computational analysis of temperature field information under non-steady-state conditions.

Electrochemical polishing, characterisation and in vitro evaluation of additively manufactured CoCr stents with personalised designs

Additive manufacturing (AM) technology has paved the way for manufacturing personalised stents. However, there is a notable gap in comprehensive microstructure analyses and in vitro evaluations of the AM CoCr stents using advanced methodologies. To address this gap, this study focuses on investigating the microstructure and in vitro performance of personalised CoCr stents manufactured through micro-laser powder bed fusion (μ-LPBF). The evaluation process begins with the measurements of dimensions and surface roughness, followed by in-depth microstructural analyses. To improve surface roughness and reduce excessive strut size, the μ-LPBF stents undergo electrochemical polishing. Importantly, in vitro stent deployments are carried out in artificial arteries manufactured based on actual patients' data. Compared to the commercial MULTI-LINK VISION CoCr stent, the μ-LPBF personalised stents have rough surface finish (average roughness: 1.55 μm for μ-LPBF vs. 1.09 μm for commercial) and compromised grain microstructures (elongated for μ-LPBF vs. equiaxed for commercial). However, the personalised stents demonstrate better performances in in vitro tests. Notably, compared to the commercial stent in the two studied cases, they deliver larger lumen gains (up to 11.24 %) and reduced recoils (up to 4 times). This study validates the merit of the lesion-specific designs and the feasibility of using AM technology for stent fabrication.

An Analysis of the Correct Frequency of the Service Inspections of German Passenger Cars—A Case Study on Kazakhstan and Poland

This article presents a case study on estimating the real service inspection intervals for German-brand passenger cars in Kazakhstan and Poland. This study aimed to identify disparities between the official recommendations of manufacturers for car maintenance and the real data collected in these two countries. The following passenger cars were examined: Audi A6, Q5, and Q8; Porsche Cayenne and Cayenne coupe; and Volkswagen Passat, Polo, Teramont, Tiguan, Touareg, Arteon, Golf, T-Cross, Tiguan all space, Touran, T-Roc, and Up. To assess the difference between real and recommended values, the manufacturer criteria of a recommended mileage of 15,000 and 30,000 km or a time frame of 365 and 730 days to the first service inspection were applied. The data analysis showed that in Kazakhstan, 31.4% of cars did not meet the warranty conditions, while in Poland, it was 21.0%. The dominant criterion that was not met was the time criterion. The assessment of these factors emphasizes the importance of customizing vehicle maintenance schedules to the specific conditions and driving behaviors prevalent in each country. The practical contribution of the article lies in uncovering the discrepancies between official manufacturer recommendations for car maintenance and the actual data collected in Kazakhstan and Poland. By identifying specific models, Volkswagen Touareg and Tiguan in Kazakhstan and Volkswagen Up in Poland, for which the maintenance intervals deviated significantly from those recommended, this study offers valuable insights for optimizing service schedules and improving the efficiency of maintenance practices in these countries. From a scientific perspective, this article contributes by providing empirical evidence of real-world maintenance behaviors for German-brand passenger cars.

Co-Sharding: A Sharding Scheme for Large-Scale Internet of Things Application

Blockchain technology finds widespread application in the management of Internet of Things (IoT) devices. In response to the challenges posed by performance scalability and the convergence of multiple ledgers stemming from an expanding network, this study introduces the concept of Co-Sharding . Within this framework, the ledger maintained by sub-chains overseeing IoT operations in distinct geographic regions is conceptualized as a shard within the Large-scale Internet of Things (LIoT) ledger. Meanwhile, elected nodes within each region assume responsibility for maintaining a coordinating shard, facilitating cross-regional communication and data interaction. Furthermore, our work presents a multi-objective optimization algorithm grounded in the multi-shard paradigm to enact a scheduling strategy that spans various regions. We undertake a series of pertinent experiments and conduct a comparative analysis of scheduling algorithms within the context of a real-world cross-regional agricultural IoT system, utilizing actual operational data. The comparative results demonstrate that, in comparison to intra-sub-region scheduling, the Co-Sharding approach enhances machine utilization rates by approximately 30% and reduces scheduling time by around 18% when confronted with a task count of 12. In terms of performance, Co-Sharding also exhibits the capability to reduce the storage requirements of lightweight nodes within each region by approximately 39% while concurrently improving throughput by approximately 1.5 times when contrasted with a single-chain architecture.

Meaningful within-patient change for clinical outcome assessments: model-based approach versus cumulative distribution functions

ABSTRACT Objectives The FDA recommends the use of anchor-based methods and empirical cumulative distribution function (eCDF) curves to establish a meaningful within-patient change (MWPC) for a clinical outcome assessment (COA). In practice, the estimates obtained from model-based methods and eCDF curves may not closely align, although an anchor is used with both. To help interpret their results, we investigated and compared these approaches. Methods Both repeated measures model (RMM) and eCDF approaches were used to estimate an MWPC on a target COA. We used both real-life (ClinicalTrials.gov: NCT02697773) and simulated data sets that included 688 patients with up to six visits per patient, target COA (range 0 to 10), and an anchor measure on patient global assessment of osteoarthritis from 1 (very good) to 5 (very poor). Ninety-five percent confidence intervals for the MWPC were calculated by the bootstrap method. Results The distribution of the COA score changes affected the degree of concordance between RMM and eCDF estimates. The COA score changes from simulated normally distributed data led to greater concordance between the two approaches than did COA score changes from the actual clinical data. The confidence intervals of MWPC estimate based on eCDF methods were much wider than that by RMM methods, and the point estimate of eCDF methods varied noticeably across visits. Conclusions Our data explored the differences of model-based methods over eCDF approaches, finding that the former integrates more information across a diverse range of COA and anchor scores and provides more precise estimates for the MWPC.

Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm

To enhance the economic viability of wind energy in cold regions and ensure the safe operational management of wind farms, this paper proposes a short-term wind turbine blade icing wind power prediction method that combines principal component analysis (PCA) and fractional Lévy stable motion (fLsm). By applying supervisory control and data acquisition (SCADA) data from wind turbines experiencing icing in a mountainous area of Yunnan Province, China, the model comprehensively considers long-range dependence (LRD) and self-similar features. Adopting a combined pattern of previous-day predictions and actual measurement data, the model predicts the power under near-icing conditions, thereby enhancing the credibility and accuracy of icing forecasts. After validation and comparison with other prediction models (fBm, CNN-Attention-GRU, XGBoost), the model demonstrates a remarkable advantage in accuracy, achieving an accuracy rate and F1 score of 96.86% and 97.13%, respectively. This study proves the feasibility and wide applicability of the proposed model, providing robust data support for reducing wind turbine efficiency losses and minimizing operational risks.

The role of supply chain practices on sustainability performance in the pharmaceutical sector: a case from developing economy

The aim of this research is to broadly identify the sustainable influence of green practices to cope with the problems caused by pharmaceutical manufacturing, purchasing and distribution, which have insignificant effects on the environment, well-being, animals, ocean, and water; in this regard, the significance of the supply chain practices on sustainable performance examined. In this quantitative study, the researcher will use a cross-sectional research design and a structured questionnaire for data collection to find out the research problem from professionals of supply chain managers. In this study, a random sampling technique will be considered to get the actual data from the experience of supply chain managers. The research scope is limited to the Karachi region to discover Supply Chain Practices on Sustainability performance in the Pharmaceutical Sector in Karachi. Thereby potentially restricting generalizability. The findings are based on a sample of 105 pharmaceutical companies in Karachi.

Olah Kata Sosio-Kultural

This research aims to describe the phenomenon of word processing which has socio-cultural meaning. This type of research is qualitative research. Qualitative research is research used to obtain more in-depth data, namely data that contains meaning. The meaning in question is the actual data in the form of words, sentences and discourse. The data in this research are words, phrases, sentences written by social media users which imply socio-cultural meaning. Data collection techniques in this research are library techniques and note-taking techniques. Library technique is a data collection technique by examining books and journal articles that are related to the problem being solved. The note technique is a technique for noting several forms that are relevant for research on written language use. The data analysis technique in research is descriptive analysis through the steps of identifying data, classifying data, presenting data, and drawing conclusions. As a result of this research, the researcher found five examples of socio-cultural words, including: 1) Black skin, curly hair, I'm Papuan Bae sonde bae land of Timor lebe bae 3) Jogja is special, the country is special, the people are special, Bali Island Attractive Gods, Ama Mawolo, Ina Marawi.

Modeling and Optimization of Interior Ballistics within Pneumatic Underwater Launchers

A new area of underwater equipment research focus is the use of underwater unmanned vehicles (UUVs) with launch mechanisms to deploy lightweight and small-sized robots for functions including communication, exploration, and detection. The internal ballistic mathematical model of the underwater launch system for small robots is established in this paper. The internal ballistic parameters and the robot displacement and velocity change rule over time are obtained. The optimization calculation of the crucial parameters to be determined by the particle swarm algorithm is completed. Following optimization, the gas cylinder’s initial pressure is 2 MPa, its capacity is 30 L, its opening area is 9.683 × 10−5 m2, and its opening time is 0.02 s. A numerical simulation is performed for the small robot’s underwater launch process, based on the mathematical and physical model supplied by Fluent 2020 software. The results yield the robot’s motion law and the properties of the flow field during the launch process. The purpose of the underwater launcher experiment is to determine the robot’s motion characteristics. The accuracy of the theoretical model is confirmed by comparing and analyzing the numerical simulation results with the actual data.

Theory and Empirical Analysis of Bridge Load Limitation Under the Action of Typical Heavy-Duty Vehicles

Abstract With the continuous growth of transportation demands, in-service highway bridges face greater challenges in their long-term operational lifespans, and bridge collapse accidents caused by vehicle overloading occur from time to time. Additionally, under the influence of loads and environmental factors, various wear patterns inevitably lead to degraded reinforced concrete bridges. In view of this problem, it is reasonable and feasible to limit the vehicle loads passing over highway bridges, and the key basis for limitation is determining the load limit value of a bridge. To determine a reasonable load limit value of a bridge, this paper has carried out the following research: Based on a classification of vehicle types, this paper explores the load parameters of several heavy-duty vehicles with large traffic volumes through traffic flow information and summarizes the load spectra of typical heavy-duty vehicles. On the basis of the theory of structural reliability, a method for determining the limit load of bridges is proposed. Given a structural target reliability index, the theoretical load limit value of a bridge can be calculated. To ensure the rationality of the theory of bridge load limit value, by relying on the engineering example, the theoretical load limit value is calculated. By comparing actual bridge load test data with the finite element model results, the rationality of the bridge load limiting theory is verified. Finally, the paper notes that it is safer and more reliable to define the load limit value according to the bending stress state for bridges.

Risk-Informing Nuclear Reactor Safety: The Prediction of the Probability of Core Damage Due to Loss of Power and Cooling

Abstract The modern idea of risk-informed decision-making (RIDM) is here critically examined for all existing, advanced and, generation-IV nuclear reactor systems. Motivated by the predictive difficulties of probabilistic risk assessment (PRA) in regard to occurred accidents, it is evident that the real (not hypothetical) consequences of nuclear core damage accidents that must be considered and quantified are the financial losses, infrastructure damages, societal disruptions, and adverse political policies, and not solely the traditional exceedance of regulated radiation release or public exposure limits. With this perspective, a new dynamic analysis is presented for estimating the probability of core damage due to extended loss of power and cooling in a modern nuclear reactor, giving results different from current standardized PRA/PSA analyses. Using existing data, we approach the multitude of different events in a new way: whatever the initial event in the finite event set {external flood, fire, hurricane, ice storm, typhoon, earthquake, cyber attack…}, the fundamental concerns are the consequent loss and nonrestoration of power, and the inadequate cooling of the core. The present proposed quantitative evaluation uses applicable and fully “exchangeable” severe event data for nuclear and nonnuclear systems, including active and passive emergency back-up systems for a wide range of power losses that lead to widespread damage and societal disruption. While not design-specific, this new independent “order-of-magnitude” estimate for the probability of core damage is some two to two hundred times larger than that shown or reported in recent modern and formal PSA/PRA for licensing submissions using generic failure rate data rather than actual severe event data. This new finding suggests greater uncertainties exist than presently assumed for risk-informed decision-making (RIDM), and points to the need for a major reconsideration and updating of risk assessment and regulatory risk-informed approaches for nuclear plant safety evaluation.

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation.