Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Topic Detection Approaches in Identifying Topics and Events from Arabic Corpora

© 2018 The Authors. Published by Elsevier B.V. How can we know what is going on in the world with a click of a button? With the increase of digital data everywhere, it is becoming difficult to categorize and retrieve information from such huge data. Topic detection is considered a powerful way to mine data and relate similar documents together. Although the Arabic content on the web is increasing every day, the application of topic detection on Arabic text is not up to this increase. In this paper we are investigating famous topic detection techniques, and latest significant scholarly articles related to topic detection in general and in the Arabic domain in specific. This survey paper will help researchers interested in the domain of topic detection to be familiar with commonly used techniques and updated with the latest technologies in this area

Topic extraction in social media

[abstract not available

Information gain as a feature selection method for the efficient classification of influenza based on viral hosts

The paper demonstrates the improvement in Influenza A classification based on viral host when applying feature selection on classical machine learning techniques. The impact of using the most informative DNA positions on classifier efficiency and performance was measured. Both decision trees (DTs) and neural networks (NNs) were used. The experiments were conducted on DNA sequences belonging to the PB1 and HA segments of subtypes H1 and H5 respectively. Sequences from each segment were further divided into human and nonhuman hosts prior to classification analysis. Accuracy, sensitivity, specificity, precision and time were used as performance measures. Extracting the best hundred informative positions with information gain increased classification efficiency by 90% for both classifiers, without compromising performance significantly. NNs performed better on both DNA segments than DTs, when decreasing the number of informative positions below a hundred. The classification speed of NNs was improved vastly compared to DTs, when classifying the H1, PB1 segment

BERT BiLSTM-Attention Similarity Model

Semantic similarity models are a core part of many of the applications of natural language processing (NLP) that we may be encountering daily, which makes them an important research topic. In particular, Question Answering Systems are one of the important applications that utilize semantic similarity models. This paper aims to propose a new architecture that improves the accuracy of calculating the similarity between questions. We are proposing the BERT BiLSTM-Attention Similarity Model. The model uses BERT as an embedding layer to convert the questions to their respective embeddings, and uses BiLSTM-Attention for feature extraction, giving more weight to important parts in the embeddings. The function of one over the exponential function of the Manhattan distance is used to calculate the semantic similarity score. The model achieves an accuracy of 84.45% in determining whether two questions from the Quora duplicate dataset are similar or not

Deep learning framework with confused sub-set resolution architecture for automatic arabic diacritization

[abstract not available

Automatic text summarization: A comprehensive survey

Automatic Text Summarization (ATS) is becoming much more important because of the huge amount of textual content that grows exponentially on the Internet and the various archives of news articles, scientific papers, legal documents, etc. Manual text summarization consumes a lot of time, effort, cost, and even becomes impractical with the gigantic amount of textual content. Researchers have been trying to improve ATS techniques since the 1950s. ATS approaches are either extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate representation then generates the summary with sentences that are different than the original sentences. The hybrid approach combines both the extractive and abstractive approaches. Despite all the proposed methods, the generated summaries are still far away from the human-generated summaries. Most researches focus on the extractive approach. It is required to focus more on the abstractive and hybrid approaches. This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions