Machine Learning-Based Prediction of COVID-19 Prognosis Using Clinical and Hematologic Data.

The coronavirus disease 2019 (COVID-19) pandemic is challenging healthcare systems worldwide. The prediction of disease prognosis has a critical role in confronting the burden of COVID-19. We aimed to investigate the feasibility of predicting COVID-19 patient outcomes and disease severity based on clinical and hematological parameters using machine learning techniques. This multicenter retrospective study analyzed records of 485 patients with COVID-19, including demographic information, symptoms, hematological variables, treatment information, and clinical outcomes. Different machine learning approaches, including random forest, multilayer perceptron, and support vector machine, were examined in this study. All models showed a comparable performance, yielding the best area under the curve of 0.96, in predicting the severity of disease and clinical outcome. We also identified the most relevant features in predicting COVID-19 patient outcomes, and we concluded that hematological parameters (neutrophils, lymphocytes, D-dimer, and monocytes) are the most predictive features of severity and patient outcome.

Diversified Regularization Enhanced Training for Effective Manipulator Calibration.

Recently, robot arms have become an irreplaceable production tool, which play an important role in the industrial production. It is necessary to ensure the absolute positioning accuracy of the robot to realize automatic production. Due to the influence of machining tolerance, assembly tolerance, the robot positioning accuracy is poor. Therefore, in order to enable the precise operation of the robot, it is necessary to calibrate the robotic kinematic parameters. The least square method and Levenberg-Marquardt (LM) algorithm are commonly used to identify the positioning error of robot. However, it generally has the overfitting caused by improper regularization schemes. To solve this problem, this article discusses six regularization schemes based on its error models, i.e., L1 , L2 , dropout, elastic, log, and swish. Moreover, this article proposes a scheme with six regularization to obtain a reliable ensemble, which can effectively avoid overfitting. The positioning accuracy of the robot is improved significantly after calibration by enough experiments, which verifies the feasibility of the proposed method.

Location-Allocation Model to Improve the Distribution of COVID-19 Vaccine Centers in Jeddah City, Saudi Arabia.

The correct distribution of service facilities can help keep fixed and overhead costs low while increasing accessibility. When an appropriate location is chosen, public-sector facilities, such as COVID-19 centers, can save lives faster and provide high-quality service to the community at a low cost. The purpose of the research is to highlight the issues related to the location of COVID-19 vaccine centers in the city of Jeddah, Saudi Arabia. In particular, this paper aims to analyze the accessibility of COVID-19 vaccine centers in Jeddah city using maximal coverage location problems with and without constraint on the number and capacity of facilities. A maximal coverage model is first used to analyze the COVID-19 vaccination coverage of Jeddah districts with no restriction on the facility capacity. Then, a maximize capacitated coverage method is utilized to assess the centers' distribution and demand coverage with capacity constraints. Finally, the minimize facilities model is used to identify the most optimal location required to satisfy all demand points with the least number of facilities. The optimization approaches consider the objective function of minimizing the overall transportation time and travel distance to reduce wastage on the service rate provided to the patients. The optimization model is applied to a real-world case study in the context of the COVID-19 vaccination center in Jeddah. The results of this study provide valuable information that can help decision-makers locate and relocate COVID-19 centers more effectively under different constraints conditions.

Deep learning-based approach for Arabic open domain question answering.

Open-domain question answering (OpenQA) is one of the most challenging yet widely investigated problems in natural language processing. It aims at building a system that can answer any given question from large-scale unstructured text or structured knowledge-base. To solve this problem, researchers traditionally use information retrieval methods to retrieve the most relevant documents and then use answer extractions techniques to extract the answer or passage from the candidate documents. In recent years, deep learning techniques have shown great success in OpenQA by using dense representation for document retrieval and reading comprehension for answer extraction. However, despite the advancement in the English language OpenQA, other languages such as Arabic have received less attention and are often addressed using traditional methods. In this paper, we use deep learning methods for Arabic OpenQA. The model consists of document retrieval to retrieve passages relevant to a question from large-scale free text resources such as Wikipedia and an answer reader to extract the precise answer to the given question. The model implements dense passage retriever for the passage retrieval task and the AraELECTRA for the reading comprehension task. The result was compared to traditional Arabic OpenQA approaches and deep learning methods in the English OpenQA. The results show that the dense passage retriever outperforms the traditional Term Frequency-Inverse Document Frequency (TF-IDF) information retriever in terms of the top-20 passage retrieval accuracy and improves our end-to-end question answering system in two Arabic question-answering benchmark datasets.

A Survey of Location-Allocation of Points of Dispensing During Public Health Emergencies.

Public health emergencies such as disease outbreaks and bioterrorism attacks require immediate response to ensure the safety and well-being of the affected community and prevent the further spread of infection. The standard method to increase the efficiency of mass dispensing during health emergencies is to create emergency points called points of dispensing (PODs). PODs are sites for distributing medical services such as vaccines or drugs to the affected population within a specific time constraint. These PODs need to be sited in optimal locations and have people (demand points) assigned to them simultaneously; this is known as the location-allocation problem. PODs may need to be selected to serve the entire population (full allocation) or different priority or needs groups (partial allocation). Several previous studies have focused on location problems in different application domains, including healthcare. However, some of these studies focused on healthcare facility location problems without specifying location-allocation problems or the exact domain. This study presents a survey of the PODs location-allocation problem during public health emergencies. This survey aims to review and analyse the existing models for PODs location-allocation during public health emergencies based on full and partial demand points allocation. Moreover, it compares existing models based on their key features, strengths, and limitations. The challenges and future research directions for PODs location-allocation models are also discussed. The results of this survey demonstrated a necessity to develop a variety of techniques to analyse, define and meet the demand of particular groups. It also proved essential that models be developed for different countries, including accounting for variations in population size and density. Moreover, the model constraints, such as those relating to time or prioritizing certain groups, need to be considered in the solution. Finally, additional comparative studies are required to clarify which methods or models are adequate based on predefined criteria.

Spatial Analysis of COVID-19 Vaccine Centers Distribution: A Case Study of the City of Jeddah, Saudi Arabia.

The COVID-19 pandemic is one of the most devastating public health emergencies in history. In late 2020 and after almost a year from the initial outbreak of the novel coronavirus (SARS-CoV-2), several vaccines were approved and administered in most countries. Saudi Arabia has established COVID-19 vaccination centers in all regions. Various facilities were selected to set up these vaccination centers, including conference and exhibition centers, old airport terminals, pre-existing medical facilities, and primary healthcare centers. Deciding the number and locations of these facilities is a fundamental objective for successful epidemic responses to ensure the delivery of vaccines and other health services to the entire population. This study analyzed the spatial distribution of COVID-19 vaccination centers in Jeddah, a major city in Saudi Arabia, by using GIS tools and methods to provide insight on the effectiveness of the selection and distribution of the COVID-19 vaccination centers in terms of accessibility and coverage. Based on a spatial analysis of vaccine centers' coverage in 2020 and 2021 in Jeddah presented in this study, coverage deficiency would have been addressed earlier if the applied GIS analysis methods had been used by authorities while gradually increasing the number of vaccination centers. This study recommends that the Ministry of Health in Saudi Arabia evaluated the assigned vaccination centers to include the less-populated regions and to ensure equity and fairness in vaccine distribution. Adding more vaccine centers or reallocating some existing centers in the denser districts to increase the coverage in the uncovered sparse regions in Jeddah is also recommended. The methods applied in this study could be part of a strategic vaccination administration program for future public health emergencies and other vaccination campaigns.

Hybrid machine learning approach for Arabic medical web page credibility assessment.

For many people, the Internet is their primary source of knowledge in today's modern world. Internet users frequently seek health-related information in order to better understand a health problem, seek guidance, or diagnose symptoms. Unfortunately, most of this information is inaccurate or unreliable, making it difficult for regular users to discern reliable sources of information. To determine online source reliability, specific knowledge and domain expertise are necessary. Researchers in health informatics studied a number of linguistic and non-linguistic indicators to assist ordinary individuals in judging medical web page credibility. This study proposes a method that automates the process of assessing the reliability of online medical sites based on textual and non-textual characteristics. To evaluate the proposed approach, we developed a real-world dataset of Arabic web pages that provide medical information. This dataset is the first Arabic medical web page dataset for content credibility evaluation. The hybrid approach was assessed using multiple machine learning and deep learning algorithms on the dataset, providing an accuracy and F1-score of 79% and 77%, respectively. We also identify the most observable patterns that help evaluate or detect unreliable web pages written in Arabic.

Optimization of Micro-Pollutants' Removal from Wastewater Using Agricultural Waste-Derived Sustainable Adsorbent.

Water pollution due to the discharge of untreated industrial effluents is a serious environmental and public health issue. The presence of organic pollutants such as polycyclic aromatic hydrocarbons (PAHs) causes worldwide concern because of their mutagenic and carcinogenic effects on aquatic life, human beings, and the environment. PAHs are pervasive atmospheric compounds that cause nervous system damage, mental retardation, cancer, and renal kidney diseases. This research presents the first usage of palm kernel shell biochar (PKSB) (obtained from agricultural waste) for PAH removal from industrial wastewater (oil and gas wastewater/produced water). A batch scale study was conducted for the remediation of PAHs and chemical oxygen demand (COD) from produced water. The influence of operating parameters such as biochar dosage, pH, and contact time was optimized and validated using a response surface methodology (RSM). Under optimized conditions, i.e., biochar dosage 2.99 g L-1, pH 4.0, and contact time 208.89 min, 93.16% of PAHs and 97.84% of COD were predicted. However, under optimized conditions of independent variables, 95.34% of PAH and 98.21% of COD removal was obtained in the laboratory. The experimental data were fitted to the empirical second-order model of a suitable degree for the maximum removal of PAHs and COD by the biochar. ANOVA analysis showed a high coefficient of determination value (R2 = 0.97) and a reasonable second-order regression prediction. Additionally, the study also showed a comparative analysis of PKSB with previously used agricultural waste biochar for PAH and COD removal. The PKSB showed significantly higher removal efficiency than other types of biochar. The study also provides analysis on the reusability of PKSB for up to four cycles using two different methods. The methods reflected a significantly good performance for PAH and COD removal for up to two cycles. Hence, the study demonstrated a successful application of PKSB as a potential sustainable adsorbent for the removal of micro-pollutants from produced water.

Clinical and hematologic presentations of adults with COVID-19 patients in Jeddah: A case control study.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), is associated with significant morbidity and mortality. The clinical features of COVID-19 were mentioned in previous studies. However, risk factors for COVID-19 are not fully recognized. The aim of this study is to characterize risk factors and clinical features of COVID-19 disease in Jeddah, Saudi Arabia. METHODS: A retrospective, chart-review, case-control study was conducted at King Abdulaziz University, Jeddah, Saudi Arabia. Demographic, clinical, radiological, and laboratory data on patients diagnosed between March 18 and May 18, 2020 were collected and analyzed. RESULTS: We reviewed medical records on 297 suspected cases of COVID-19. Of these, 175 (59%) tested positive for COVID-19 by polymerase chain reaction (PCR) and considered as cases, while 122 (41%) tested negative and considered as control. COVID-19 positive cases were more likely to be males, and non-health care providers. Hypertension (15%), diabetes (10%) and two or more concurrent comorbidities (54.4%) were more prevalent among COVID-19 patients. Patients presented with fever, cough, and loss of taste/smell were more likely to test positive for COVID-19 (P = 0.001, 0.008, 0.008; respectively). Radiological evidence of pneumonia was associated with confirmed COVID-19 disease (P = 0.001). Shortness of breath and gastrointestinal symptoms were not associated with the risk of COVID-19 at presentation. On admission, white blood cells, neutrophils, lymphocytes, eosinophils, basophils, and platelets were significantly lower among COVID-19 patients compared with controls. Surprisingly, D-Dimer levels were lower among COVID-19 positive patients when compared with controls. CONCLUSION: Male gender, hypertension, and diabetes are the most commonly observed risk factors associated with COVID-19 disease in Jeddah, Saudi Arabia. COVID-19 patient had significantly lower lymphocyte and neutrophil counts.