An Ensemble Machine Learning Approach To Causal Inference in High-Dimensional Settings

The machine-learning algorithms have gained popularity and have gotten the attention of many researchers in the fields of statistics and computer sciences in recent decades. Due to their computational capabilities in big data, many researchers have been attempting to incorporate machine-learning in prediction and inference problems. One of the recent methods that got a lot of attentions was referred to as the double machine learning method (DML). This method attempts to estimate the effect of the treatment variable in the presence of high-dimensional nuisance function by incorporating machine-learning algorithms. Previous studies have shown that the DML method is able to reduce the bias in estimating the targeted parameter when many covariates are present in the dataset. In this dissertation, a method was proposed that is referred to as the double super learner method (DSL). Since there are many machine-learning algorithms in existence today that are different in their searching strategy, there is no way to know which algorithm performs best for a given dataset. The proposed DSL method was developed in parallel with the DML method and works by incorporating several machine-learning algorithms via the super learner function. Numerical simulation was performed across various data settings in terms of the sample, the number of associated covariates, and the type of treatment variable. In comparison with the original DML method, numerical simulation showed that the proposed method achieved reduction in bias and provided valid confidence intervals in situations where the original method did not. A package called DoubleSL was then developed and made public for those who desire to use this method in their research. In addition, real-data examples were included in the package to demonstrate the use of this method

A Named Entity Recognition System Applied to Arabic Text in the Medical Domain

Currently, 30-35% of the global population uses the Internet. Furthermore, there is a rapidly increasing number of non-English language internet users, accompanied by an also increasing amount of unstructured text online. One area replete with underexploited online text is the Arabic medical domain, and one method that can be used to extract valuable data from Arabic medical texts is Named Entity Recognition (NER). NER is the process by which a system can automatically detect and categorise Named Entities (NE). NER has numerous applications in many domains, and medical texts are no exception. NER applied to the medical domain could assist in detection of patterns in medical records, allowing doctors to make better diagnoses and treatment decisions, enabling medical staff to quickly assess a patient's records and ensuring that patients are informed about their data, as just a few examples. However, all these applications would require a very high level of accuracy. To improve the accuracy of NER in this domain, new approaches need to be developed that are tailored to the types of named entities to be extracted and categorised. In an effort to solve this problem, this research applied Bayesian Belief Networks (BBN) to the process. BBN, a probabilistic model for prediction of random variables and their dependencies, can be used to detect and predict entities. The aim of this research is to apply BBN to the NER task to extract relevant medical entities such as disease names, symptoms, treatment methods, and diagnosis methods from modern Arabic texts in the medical domain. To achieve this aim, a new corpus related to the medical domain has been built and annotated. Our BBN approach achieved a 96.60% precision, 90.79% recall, and 93.60% F-measure for the disease entity, while for the treatment method entity, it achieved 69.33%, 70.99%, and 70.15% for precision, recall, and F-measure, respectively. For the diagnosis method and symptom categories, our system achieved 84.91% and 71.34%, respectively, for precision, 53.36% and 49.34%, respectively, for recall, and 65.53% and 58.33%, for F-measure, respectively. Our BBN strategy achieved good accuracy for NEs in the categories of disease and treatment method. However, the average word length of the other two NE categories observed, diagnosis method and symptom, may have had a negative effect on their accuracy. Overall, the application of BBN to Arabic medical NER is successful, but more development is needed to improve accuracy to a standard at which the results can be applied to real medical systems

Iron Deficiency Anemia in Adults and its Diagnosis and Treatment: A Systemic Review

The aim of this study is to explore the clinical management in diagnosis and treatment of the iron deficiency anemia in adults with a systematic review methodology, as the iron deficiency is the most frequent cause of anemia worldwide. And it impairs quality of life, increases asthenia and can lead to clinical worsening of patients. In addition, iron deficiency has a complex mechanism whose pathologic pathway is recently becoming better understood. This review summarizes the current knowledge regarding diagnostic algorithms for iron deficiency anemia. The majority of aetiologies occur in the digestive tract, and justify morphological examination of the gut. First line investigations are upper gastrointestinal endoscopy and colonoscopy, and when negative, the small bowel should be explored; newer tools such as video capsule endoscopy have also been developed. The treatment of iron deficiency is aetiological if possible and iron supplementation whether in oral or in parenteral form

Radiologic Management of Vascular Malformations’ Interventional, Classification and Diagnosis

This study aimed at analyzing the diverse group of congenital vascular malformations, with respect to their place within the broader classification of vascular anomalies and their pathologic, clinical, and radiologic diagnosis and management. And the study discuss some of the techniques, agents, and approaches used in the interventional treatment of this difficult group of lesions. The researchers are aware and acknowledge that there are several different techniques and agents that can be used to treat these lesions. The techniques and agents described in this article have been used for years by the experts with good results. The aim of this study is to share experience in the management of vascular malformations with these techniques at Jordanian hospitals, and to assess the patient satisfaction levels by the evaluation of the follow-up of patients with vascular malformations treated in the Interventional Radiology Unit from January 2016 to December 2016. Patients were classified according to the hemodynamics of the lesions (high- vs. low-flow)

MULDASA:Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media

The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialect

Graduate Medical Students’ Mental Health Concerns During COVID-19 Pandemic

Medical students are more likely than the general population to experience perceived stress during the pandemic due to many variables. This study aimed to evaluate the stress levels and prevalence of different mental health conditions among graduate medical students in Al Kharj City. An anonymous online survey was conducted among graduate medical students of Prince Sattam bin Abdulaziz University (PSAU). For this investigation, the following scales were used to measure the prevalence of common mental health issues: DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure-Adult (CCSM-A); Perceived Stress Scale (PSS-10-C), to measure COVID-19-related student stress; and the COVID-19 Student Stress Questionnaire to get the global stress score (GSS). Two hundred twenty-one students were contacted, and 214(96.8%) consented to participate in the study. According to the CCSM-A scale, anxiety (73%) and depressive symptoms (71%) were the most frequently reported symptoms by the students. After correcting for age and self-perceived COVID-19 risk, there was a significant relationship between anger, suicidal ideation, and substance use, on one hand, and the study year on the other graduate medical students who have mental health issues bear a heavy load. In the post-pandemic recovery period, regular mental health assessments and providing early and adequate mental health assistance to needy people are imperative

Causes of elective cesarean delivery on maternal request in Aljouf, Saudi Arabia

Background: Recently observed there is a steadily higher rate of cesarean delivery worldwide mostly due to the increasing number of women requesting an elective cesarean section on maternal request without valid indication. The aim of the study was to determine the causes of elective cesarean delivery on maternal requests in Aljouf Saudi Arabia.Methods: This was a descriptive cross-sectional study and data was evaluated by completing seven questionnaires and interviews with laboratory reports who were admitted for cesarean delivery at the Obstetrics department of Maternity and Children Hospital (MCH) Aljouf, Saudi Arabia from January 2020 to December 2020. A total of 141 Saudi women of age between 18 and over 35 years were enrolled, including those who have singleton pregnancy, no complications during pregnancy, and no medical indication for cesarean delivery.Results: 141 women reported willingness to request cesarean delivery. The mean systolic 120±6.23, diastolic 75±2.45 blood pressure mm of Hg, and fasting blood sugar level 4.1±1.1 mmol/l have been found within the normal limit. The ultrasound (US) confirmed singleton pregnancy without any abnormalities.  Data reveals that common causes of elective cesarean section on maternal request to avoid the episiotomy 77.3%, fear of labor pain 69.5%, trauma to the vagina 79.4%, uncertainty about timing 61.7%, losing a baby during vaginal delivery 54.6%, experience other members 41.8%, the risk for baby 39%, prolapse or incontinence24.1%, unsatisfactory sexual intercourse 17.7% and the undesirable experience of the previous vaginal delivery 12%.Conclusions: Maternal request for cesarean delivery is considered one of the reasons for increasing the rate of cesarean delivery in Saudi Arabia. To avoid the episiotomy and fear of labor pain may strong causes for choosing cesarean delivery

Pattern of Strabismus in Children and Adolescents in Hail, KSA

Background: Strabismus (Squint), abnormal ocular alignment could occur constantly or intermittently. Double vision, poor vision or abnormal head positioning may accompany it. A child with squint may stop using the affected eye. This can lead to visual loss, which can become permanent unless treated early in childhood. Objective: to estimate the prevalence of squint, types and treatment characteristics in the studied children and adolescents in Hail city, Saudi Arabia. Methods: A cross-sectional study conducted in Hail city, Saudi Arabia. The study included 299 participants; 148 male and 151 female children and adolescents from 6 months to 19 years. The study period was from 1 January to 30 May 2018. Data collected by personal interview using a pre-designed questionnaire, which distributed among mothers of children and adolescents to be self-reported. Results: Squint found in 17.1% of the studied sample. Squint was right sided in 37.3% of the cases, left sided in also 37.3% and in both eyes in 25.5% of the studied cases. About half (47.1%) of cases had inward squint (esotropia) and 15.7% outward squint (exsotropia), 21.6% of the cases had Intermittent squint and 52.2% had permanent squint. In most (70.7%) of cases, squint affected the visual acuity. As regards treatment, 45.1% received medical treatment and 13.7% received surgical treatment. Only 19.6% of cases completely cured and 39.2% had recurrence. There was insignificant relation with sex, squint in parents, other hereditary diseases and consanguinity between parents (P>0.05). Conclusion: in this study, the prevalence of squint in the studied children and adolescents in Hail city, Saudi Arabia was 17.1%. No significant difference between males and females. After treatment, only 19.6% of cases completely cured and 39.2% had recurrence. Health education of the public about importance of early treatment is mandatory. Keywords: Squint; strabismus; prevalence; types; Hail; Saudi Arabia