Early detection of student degree-level academic performance using educational data mining.

Higher educational institutes generate massive amounts of student data. This data needs to be explored in depth to better understand various facets of student learning behavior. The educational data mining approach has given provisions to extract useful and non-trivial knowledge from large collections of student data. Using the educational data mining method of classification, this research analyzes data of 291 university students in an attempt to predict student performance at the end of a 4-year degree program. A student segmentation framework has also been proposed to identify students at various levels of academic performance. Coupled with the prediction model, the proposed segmentation framework provides a useful mechanism for devising pedagogical policies to increase the quality of education by mitigating academic failure and encouraging higher performance. The experimental results indicate the effectiveness of the proposed framework and the applicability of classifying students into multiple performance levels using a small subset of courses being taught in the initial two years of the 4-year degree program.

A Convolutional Neural Network Architecture for Segmentation of Lung Diseases Using Chest X-ray Images.

The segmentation of lungs from medical images is a critical step in the diagnosis and treatment of lung diseases. Deep learning techniques have shown great promise in automating this task, eliminating the need for manual annotation by radiologists. In this research, a convolution neural network architecture is proposed for lung segmentation using chest X-ray images. In the proposed model, concatenate block is embedded to learn a series of filters or features used to extract meaningful information from the image. Moreover, a transpose layer is employed in the concatenate block to improve the spatial resolution of feature maps generated by a prior convolutional layer. The proposed model is trained using k-fold validation as it is a powerful and flexible tool for evaluating the performance of deep learning models. The proposed model is evaluated on five different subsets of the data by taking the value of k as 5 to obtain the optimized model to obtain more accurate results. The performance of the proposed model is analyzed for different hyper-parameters such as the batch size as 32, optimizer as Adam and 40 epochs. The dataset used for the segmentation of disease is taken from the Kaggle repository. The various performance parameters such as accuracy, IoU, and dice coefficient are calculated, and the values obtained are 0.97, 0.93, and 0.96, respectively.

Computing Drug-Drug Similarity from Patient-Centric Data.

In modern biology and medicine, drug-drug similarity is a major task with various applications in pharmaceutical drug development. Various direct and indirect sources of evidence obtained from drug-centric data such as side effects, drug interactions, biological targets, and chemical structures are used in the current methods to measure the level of drug-drug similarity. This paper proposes a computational method to measure drug-drug similarity using a novel source of evidence that is obtained from patient-centric data. More specifically, patients' narration of their thoughts, opinions, and experience with drugs in social media are explored as a potential source to compute drug-drug similarity. Online healthcare communities were used to extract a dataset of patients' reviews on anti-epileptic drugs. The collected dataset is preprocessed through Natural Language Processing (NLP) techniques and four text similarity methods are applied to measure the similarities among them. The obtained similarities are then used to generate drug-drug similarity-based ranking matrices which are analyzed through Pearson correlation, to answer questions related to the overall drug-drug similarity and the accuracy of the four similarity measures. To evaluate the obtained drug-drug similarities, they are compared with the corresponding ground-truth similarities obtained from DrugSimDB, a well-known drug-drug similarity tool that is based on drug-centric data. The results provide evidence on the feasibility of patient-centric data from social media as a novel source for computing drug-drug similarity.

U-Net Model with Transfer Learning Model as a Backbone for Segmentation of Gastrointestinal Tract.

The human gastrointestinal (GI) tract is an important part of the body. According to World Health Organization (WHO) research, GI tract infections kill 1.8 million people each year. In the year 2019, almost 5 million individuals were detected with gastrointestinal disease. Radiation therapy has the potential to improve cure rates in GI cancer patients. Radiation oncologists direct X-ray beams at the tumour while avoiding the stomach and intestines. The current objective is to direct the X-ray beam toward the malignancy while avoiding the stomach and intestines in order to improve dose delivery to the tumour. This study offered a technique for segmenting GI tract organs (small bowel, big intestine, and stomach) to assist radio oncologists to treat cancer patients more quickly and accurately. The suggested model is a U-Net model designed from scratch and used for the segmentation of a small size of images to extract the local features more efficiently. Furthermore, in the proposed model, six transfer learning models were employed as the backbone of the U-Net topology. The six transfer learning models used are Inception V3, SeResNet50, VGG19, DenseNet121, InceptionResNetV2, and EfficientNet B0. The suggested model was analysed with model loss, dice coefficient, and IoU. The results specify that the suggested model outperforms all transfer learning models, with performance parameter values as 0.122 model loss, 0.8854 dice coefficient, and 0.8819 IoU.

Leveraging Tweets for Artificial Intelligence Driven Sentiment Analysis on the COVID-19 Pandemic.

The COVID-19 pandemic has been a disastrous event that has elevated several psychological issues such as depression given abrupt social changes and lack of employment. At the same time, social scientists and psychologists have gained significant interest in understanding the way people express emotions and sentiments at the time of pandemics. During the rise in COVID-19 cases with stricter lockdowns, people expressed their sentiments on social media. This offers a deep understanding of human psychology during catastrophic events. By exploiting user-generated content on social media such as Twitter, people's thoughts and sentiments can be examined, which aids in introducing health intervention policies and awareness campaigns. The recent developments of natural language processing (NLP) and deep learning (DL) models have exposed noteworthy performance in sentiment analysis. With this in mind, this paper presents a new sunflower optimization with deep-learning-driven sentiment analysis and classification (SFODLD-SAC) on COVID-19 tweets. The presented SFODLD-SAC model focuses on the identification of people's sentiments during the COVID-19 pandemic. To accomplish this, the SFODLD-SAC model initially preprocesses the tweets in distinct ways such as stemming, removal of stopwords, usernames, link punctuations, and numerals. In addition, the TF-IDF model is applied for the useful extraction of features from the preprocessed data. Moreover, the cascaded recurrent neural network (CRNN) model is employed to analyze and classify sentiments. Finally, the SFO algorithm is utilized to optimally adjust the hyperparameters involved in the CRNN model. The design of the SFODLD-SAC technique with the inclusion of an SFO algorithm-based hyperparameter optimizer for analyzing people's sentiments on COVID-19 shows the novelty of this study. The simulation analysis of the SFODLD-SAC model is performed using a benchmark dataset from the Kaggle repository. Extensive, comparative results report the promising performance of the SFODLD-SAC model over recent state-of-the-art models with maximum accuracy of 99.65%.

Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network.

BACKGROUND: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. METHOD: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image's labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. RESULTS: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network's performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. CONCLUSION: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.

Secure Telemedicine System Design for COVID-19 Patients Treatment Using Service Oriented Architecture.

The coronavirus pandemic, also known as the COVID-19 pandemic, is an ongoing virus. It was first identified on December 2019 in Wuhan, China, and later spread to 192 countries. As of now, 251,266,207 people have been affected, and 5,070,244 deaths are reported. Due to the growing number of COVID-19 patients, the demand for COVID wards is increasing. Telemedicine applications are increasing drastically because of convenient treatment options. The healthcare sector is rapidly adopting telemedicine applications for the treatment of COVID-19 patients. Most telemedicine applications are developed for heterogeneous environments and due to their diverse nature, data transmission between similar and dissimilar telemedicine applications is a difficult task. In this paper, we propose a Tele-COVID system architecture design along with its security aspects to provide the treatment for COVID-19 patients from distance. Tele-COVID secure system architecture is designed to resolve the problem of data interchange between two different telemedicine applications, interoperability, and vendor lock-in. Tele-COVID is a web-based and Android telemedicine application that provides suitable treatment to COVID-19 patients. With the help of Tele-COVID, the treatment of patients at a distance is possible without the need for them to visit hospitals; in case of emergency, necessary services can also be provided. The application is tested on COVID-19 patients in the county hospital and shows the initial results.

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs.

Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.

The burden of bites and stings management: Experience of an academic hospital in the Kingdom of Saudi Arabia.

PURPOSE: The main aim of this study is to estimate the economic burden and prevalence of bites and stings injuries in Saudi Arabia. METHODS: A retrospective, cross-sectional study was conducted at King Saud University Medical City (KSUMC) for all bites and stings cases presented to the Emergency Department (ED) between the period June 2015 and May 2019. RESULTS: A total of 1328 bites and stings cases were treated in the ED at KSUMC. There were 886 insect bites and stings cases, 376 animal bites, 22 human bites, 34 scorpion stings, and ten snakebites. Most cases were reported in April - June. Females account for 62% of the reported cases, and the mean age was 24 years old. The total management cost of bite and sting cases during the study period was 3.4 million Saudi Riyal (SR). The spending cost of the management of animal bites was the highest as it cost 1,681,920.76 SR, followed by insect's management costing 1,228,623.68 SR. CONCLUSION: Bites and stings have a considerable health care burden on our society. Although the vast majority of the cases were not associated with a severe life-threatening condition, many were visit ED and associated with high medical costs. Increased awareness of the hazards of animal-related injuries, especially during spring and summer, where most cases take place may lower its incidence and decrease EDs visits.

Pharmacist-led Medication Counseling for Patients Undergoing Hemodialysis: A Path to Better Adherence.

OBJECTIVE: The primary objective was to assess the satisfaction of patients undergoing hemodialysis regarding counseling services provided by pharmacists. The secondary objectives were to compare the effect of years on dialysis and the presence of comorbidities on patient satisfaction. METHODS: A total of 138 patients were included in the study, and all demographic and clinical variables were retrieved from the dialysis unit records of King Abdulaziz Medical City over a period of 4 months from July to October 2015. Chi-square test and Fisher's exact test were used for group comparisons at a significance level of 0.05. Results: Most patients aged between 51 and 75 years and had been on dialysis for 1 to 5 years; 94.9% of them had comorbidities. The overall satisfaction of patients toward pharmacy services was excellent (77.5%), and approximately 38.4% of patients thought that pharmacists were providing clear information about their prescribed medications. In addition, 55.8 % of the patients did not know that hemodialysis could affect the efficacy of their medications. Conclusions: Patients undergoing hemodialysis were somewhat satisfied with the counseling provided by the pharmacist. Moreover, there is a need for educational programs for patients undergoing hemodialysis that would increase awareness among hospital pharmacists to improve patients' medication knowledge.

UPLC/MS-MS assay development for estimation of mozavaptan in plasma and its pharmacokinetic study in rats.

AIM: Mozavaptan is a nonpeptide vasopressin receptor antagonist approved for the treatment of ectopic antidiuretic hormone secretion syndrome. METHODS & RESULTS: A simple, rapid and fully validated UPLC/MS-MS method was developed for the quantitation of mozavaptan in rat plasma. The chromatographic separation was conducted on an Acquity UPLC BEH™ C18 column with an optimum mobile phase of 10 mM ammonium acetate buffer and 0.1% formic acid in acetonitrile (30:70 v/v) at a flow rate of 0.3 ml/min. The multiple reaction monitoring transitions were performed at m/z 428.16→119.03 for mozavaptan and m/z 237.06→179.10 for carbamazepine (internal standard). CONCLUSION: The method was effectively applied for the determination of mozavaptan pharmacokinetic parameters after the oral administration of 3 mg/kg mozavaptan in rats.

Pharmacy education in Saudi Arabia: A vision of the future.

Background: Pharmacy education in developing countries faces many challenges. An assessment of the challenges and opportunities for the future of pharmacy education in Saudi Arabia has not been conducted. Objectives: The purpose of the study was to ascertain the views and opinions of pharmacy education stakeholders regarding the current issues challenging pharmacy education, and to discuss the future of pharmacy education in Saudi Arabia. Methods: A total of 48 participants attended a one-day meeting in October 2011, designed especially for the purpose of this study. The participants were divided into six round-table discussion sessions with eight persons in each group. Six major themes were explored in these sessions, including the need to improve pharmacy education, program educational outcomes, adoption of an integrated curriculum, the use of advanced teaching methodologies, the need to review assessment methods, and challenges and opportunities to improve pharmacy experiential training. The round-table discussion sessions were videotaped and transcribed verbatim and analyzed by two independent researchers. Results: Participants agreed that pharmacy education in the country needs improvement. Participants agreed on the need for clear, measureable, and national educational outcomes for pharmacy programs in the Kingdom. Participants raised the importance of collaboration between faculty members and departments to design and implement an integrated curriculum. They also emphasized the use of new teaching methodologies focusing on student self-learning and active learning. Assessments were discussed with a focus on the use of new tools, confidentiality of examinations, and providing feedback to students. Several points were raised regarding the opportunities to improve pharmacy experiential training, including the need for more experiential sites and qualified preceptors, addressing variations in training quality between experiential sites, the need for accreditation of experiential sites, and the use of technology to track experiential activities and assessments. Conclusion: Several challenges for improving pharmacy education in Saudi Arabia were discussed by stakeholders. To tackle these challenges facing most pharmacy schools in the Kingdom, national efforts need to be considered by involving all stakeholders.