SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)

Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi

Prescribing patterns of antihypertensive medications: A systematic review of literature between 2010 and 2020

Background: Hypertension has affected over 1.13 billion people worldwide in 2015 and it's one of the most preventable risk-factors for morbidity and mortality. Antihypertensives significantly reduce cardiovascular risks. Several studies on antihypertensives' prescribing patterns were conducted worldwide, and guidelines were developed on hypertension management. However, no systematic reviews were conducted globally to synthesize the evidence from these studies. This review aims to evaluate antihypertensives' prescription patterns, and adherence to international guidelines for hypertension management worldwide. Methods: Full-text antihypertensives' prescribing patterns evaluation studies were included. Reviews, commentaries, guidelines, and editorials were excluded. Various databases were searched including PubMed, Embase, and others. Studies were limited to English only and to articles published from (01/01/2010) to (20/03/2020). Crowe Critical Appraisal Tool (CCAT) was used for quality assessment. Results: The most commonly prescribed antihypertensives as monotherapy in adult patients with no comorbidities were ACEIs/ARBs (Angiotensin converting enzyme inhibitors/Angiotensin receptor blockers), followed by CCBs (Calcium channel blockers), and BBs (Beta Blockers). Most commonly prescribed dual combinations were thiazide diuretics+ACEIs/ARBs, BBs + CCBs and CCBs+ACEIs/ARBs. Among diabetic patients, the most common agents were ACEIs/ARBs. Among patients with heart diseases, CCBs were prescribed frequently. While patients with kidney diseases, CCBs and ARBs were most prescribed. Of the 40 studies included in the review, only four studies directly assessed the prescribing patterns of antihypertensives in adherence to clinical practice guidelines. And only two studies confirmed adherence to guidelines. Furthermore, the quality of the majority of studies was moderate (50%), while 25% of articles were reported as either high or low quality. Conclusion: This review revealed that there are areas for improvement for prescribing practices of antihypertensives in concordance with the latest evidence and with clinical practice guidelines.This work was supported by a student grant (grant number QUST-2-CPH-2020-20 ) from Qatar University Office of Research and Graduate Studies . Its contents are solely the responsibility of the authors and do not necessarily represent the official views of Qatar University.Scopu

Comparative Evaluation of Sentiment Analysis Methods Across Arabic Dialects

Sentiment analysis in Arabic is challenging due to the complex morphology of the language. The task becomes more challenging when considering Twitter data that contain significant amounts of noise such as the use of Arabizi, code-switching and different dialects that varies significantly across the Arab world, the use of non-Textual objects to express sentiments, and the frequent occurrence of misspellings and grammatical mistakes. Modeling sentiment in Twitter should become easier when we understand the characteristics of Twitter data and how its usage varies from one Arab region to another. We describe our effort to create the first Multi-Dialect Arabic Sentiment Twitter Dataset (MD-ArSenTD) that is composed of tweets collected from 12 Arab countries, annotated for sentiment and dialect. We use this dataset to analyze tweets collected from Egypt and the United Arab Emirates (UAE), with the aim of discovering distinctive features that may facilitate sentiment analysis. We also perform a comparative evaluation of different sentiment models on Egyptian and UAE tweets. These models are based on feature engineering and deep learning, and have already achieved state-of-The-Art accuracies in English sentiment analysis. Results indicate the superior performance of deep learning models, the importance of morphological features in Arabic NLP, and that handling dialectal Arabic leads to different outcomes depending on the country from which the tweets are collected.This work was made possible by NPRP 6-716-1-138 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Competence in Endoscopic Ultrasound and Endoscopic Retrograde Cholangiopancreatography, From Training Through Independent Practice.

BACKGROUND & AIMS: It is unclear whether participation in competency-based fellowship programs for endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP) results in high-quality care in independent practice. We measured quality indicator (QI) adherence during the first year of independent practice among physicians who completed endoscopic training with a systematic assessment of competence. METHODS: We performed a prospective multicenter cohort study of invited participants from 62 training programs. In phase 1, 24 advanced endoscopy trainees (AETs), from 20 programs, were assessed using a validated competence assessment tool. We used a comprehensive data collection and reporting system to create learning curves using cumulative sum analysis that were shared with AETs and trainers quarterly. In phase 2, participating AETs entered data into a database pertaining to every EUS and ERCP examination during their first year of independent practice, anchored by key QIs. RESULTS: By the end of training, most AETs had achieved overall technical competence (EUS 91.7%, ERCP 73.9%) and cognitive competence (EUS 91.7%, ERCP 94.1%). In phase 2 of the study, 22 AETs (91.6%) participated and completed a median of 136 EUS examinations per AET and 116 ERCP examinations per AET. Most AETs met the performance thresholds for QIs in EUS (including 94.4% diagnostic rate of adequate samples and 83.8% diagnostic yield of malignancy in pancreatic masses) and ERCP (94.9% overall cannulation rate). CONCLUSIONS: In this prospective multicenter study, we found that although competence cannot be confirmed for all AETs at the end of training, most meet QI thresholds for EUS and ERCP at the end of their first year of independent practice. This finding affirms the effectiveness of training programs. Clinicaltrials.gov ID NCT02509416

Competence in Endoscopic Ultrasound and Endoscopic Retrograde Cholangiopancreatography, From Training Through Independent Practice.

BACKGROUND & AIMS: It is unclear whether participation in competency-based fellowship programs for endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP) results in high-quality care in independent practice. We measured quality indicator (QI) adherence during the first year of independent practice among physicians who completed endoscopic training with a systematic assessment of competence. METHODS: We performed a prospective multicenter cohort study of invited participants from 62 training programs. In phase 1, 24 advanced endoscopy trainees (AETs), from 20 programs, were assessed using a validated competence assessment tool. We used a comprehensive data collection and reporting system to create learning curves using cumulative sum analysis that were shared with AETs and trainers quarterly. In phase 2, participating AETs entered data into a database pertaining to every EUS and ERCP examination during their first year of independent practice, anchored by key QIs. RESULTS: By the end of training, most AETs had achieved overall technical competence (EUS 91.7%, ERCP 73.9%) and cognitive competence (EUS 91.7%, ERCP 94.1%). In phase 2 of the study, 22 AETs (91.6%) participated and completed a median of 136 EUS examinations per AET and 116 ERCP examinations per AET. Most AETs met the performance thresholds for QIs in EUS (including 94.4% diagnostic rate of adequate samples and 83.8% diagnostic yield of malignancy in pancreatic masses) and ERCP (94.9% overall cannulation rate). CONCLUSIONS: In this prospective multicenter study, we found that although competence cannot be confirmed for all AETs at the end of training, most meet QI thresholds for EUS and ERCP at the end of their first year of independent practice. This finding affirms the effectiveness of training programs. Clinicaltrials.gov ID NCT02509416