Cardea: An Open Automated Machine Learning Framework for Electronic Health Records

An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted and verified software framework exists, forcing researchers to arduously repeat previous work. In this paper, we propose Cardea, an extensible open-source automated machine learning framework encapsulating common prediction problems in the health domain and allows users to build predictive models with their own data. This system relies on two components: Fast Healthcare Interoperability Resources (FHIR) -- a standardized data structure for electronic health systems -- and several AUTOML frameworks for automated feature engineering, model selection, and tuning. We augment these components with an adaptive data assembler and comprehensive data- and model- auditing capabilities. We demonstrate our framework via 5 prediction tasks on MIMIC-III and Kaggle datasets, which highlight Cardea's human competitiveness, flexibility in problem definition, extensive feature generation capability, adaptable automatic data assembler, and its usability

Impact of diabetes continuing education on health care professionals’ attitudes towards diabetes care in a Yemeni city

Purpose: To evaluate the impact of a continuing education (CE) program on the attitudes of health care professionals (HCPs) towards diabetes care in Yemen.Methods: A pre- and post-intervention study was carried out in Mukalla City, Hadramout, Yemen and was offered to all physicians, pharmacists, and nurses registered in the Health Office in the Mukalla City. The HCPs were invited to attend a CE program. All participants filled out a questionnaire before the intervention (pre-test) that measured the attitudes of the participants towards diabetes. An interventional program was given in the form of a seminar, and participants were requested to complete the same questionnaire after the seminar.Results: A total of 73 HCPs attended the CE, including 19 pharmacists (26 %), 37 physicians (50.7 %), and 17 (23.3 %) nurses. The pre- and post-intervention changes in the questionnaire responses were significant only for attitude toward the values of blood glucose levels (p = 0.009) and attitude toward autonomy of diabetes patients (p = 0.023).Conclusion: HCPs in Mukalla City have positive attitudes toward diabetes. Physicians were more aware of the sequelae of diabetes than other healthcare professional groups with nurses showing the least understanding. Therefore, more emphasis should be placed upon designing education programs for diabetes specifically tailored for nurses and pharmacists.Keywords: Diabetes, Continuing education, Attitude, Health care professional

Nephroprotective potential of Polyalthia longifolia roots against vancomycin-induced renal toxicity in experimental animals

This study was done to investigate the possible nephroprotective effect of an ethanolic root extract of Polyalthia Longifolia (PL) on vancomycin-induced nephrotoxicity using curative and protective models. Vancomycin (150 mg/kg, intravenous) was given to healthy Wistar albino rats in the curative model before the start of treatment, whereas the protective group received vancomycin at the conclusion of the 10-day treatment procedure. Animals were divided into six groups for both models; group I served as the normal control, while groups II, III, IV, V, and VI were kept as toxic control, standard (selenium, 6 mg/kg), LDPL (low dose of PL 200 mg/kg), HDPL (high dose of PL 400 mg/kg), and HDPL + selenium (interactive) groups, respectively. Renal biomarkers [(uric acid, creatinine, blood urea nitrogen (BUN), serum proteins], and blood electrolyte levels were measured for all tested groups. When compared to the vancomycin group, the HDPL significantly (p < 0.01) showed greater effectiveness in lowering the BUN, potassium, and calcium levels. Additionally, in the curative model, there was a significant (p < 0.05) decrease in the blood levels of uric acid, creatinine, BUN, potassium, and calcium in the animals who received the combination of selenium and HDPL. Both LDPL and HDPL did not provide any distinguishable effect in the protective model, but groups that received HDPL with selenium did provide detectable protection by significantly lowering their levels of uric acid, BUN, serum potassium, and total serum protein in comparison to the vancomycin control group. These findings indicate that, whether administered before or after renal damage is induced, the Polyalthia longifolia root extract provided only modest protection to nephrons, which require selenium support to prevent vancomycin-induced kidney damage

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

Analysis of Web Spam for Non-English Content: Toward More Effective Language-Based Classifiers.

Web spammers aim to obtain higher ranks for their web pages by including spam contents that deceive search engines in order to include their pages in search results even when they are not related to the search terms. Search engines continue to develop new web spam detection mechanisms, but spammers also aim to improve their tools to evade detection. In this study, we first explore the effect of the page language on spam detection features and we demonstrate how the best set of detection features varies according to the page language. We also study the performance of Google Penguin, a newly developed anti-web spamming technique for their search engine. Using spam pages in Arabic as a case study, we show that unlike similar English pages, Google anti-spamming techniques are ineffective against a high proportion of Arabic spam pages. We then explore multiple detection features for spam pages to identify an appropriate set of features that yields a high detection accuracy compared with the integrated Google Penguin technique. In order to build and evaluate our classifier, as well as to help researchers to conduct consistent measurement studies, we collected and manually labeled a corpus of Arabic web pages, including both benign and spam pages. Furthermore, we developed a browser plug-in that utilizes our classifier to warn users about spam pages after clicking on a URL and by filtering out search engine results. Using Google Penguin as a benchmark, we provide an illustrative example to show that language-based web spam classifiers are more effective for capturing spam contents

Network Scan Detection with LQS: A Lightweight, Quick and Stateful Algorithm

Network scanning reveals valuable information of accessible hosts over the Internet and their offered network services, which allows significant narrowing of potential targets to attack. Addressing and balancing a set of sometimes competing desirable properties is required to make network scanning detection more appealing in practice: 1) fast detection of scanning activity to enable prompt response by intrusion detection and prevention systems; 2) acceptable rate of false alarms, keeping in mind that false alarms may lead to legitimate traffic being penalized; 3) high detection rate with the ability to detect stealthy scanners; 4) efficient use of monitoring system resources; and 5) immunity to evasion. In this paper, we present a scanning detection algorithm designed to accommodate all of these goals. LQS is a fast, accurate, and light-weight scan detection algorithm that leverages the key properties of the monitored network environment as variables that affect how the scanning detection algorithm operates. We also present what is, to our knowledge, the first automated way to estimate a reference baseline in the absence of ground truth, for use as an evaluation methodology for scan detection. Using network traces from two sites, we evaluate LQS and compare its scan detection results with those obtained by the state-of-the-art TRW algorithm. Our empirical analysis shows significant improvements over TRW in all of these properties