The orthopaedic experience of COVID-19: A literature review.

This literature review aims to provide an account of the changes to orthopaedics in the era of COVID-19. Herein, the authors explored the use of telemedicine in orthopaedics as well as changes in surgical protocols, screening methods, work priorities and orthopaedic education. There was increased utilisation of telemedicine in orthopaedic training and outpatient cases as a means to provide continuity in education and care. The need to implement social distancing measures, coupled with the reduced availability of staff, has dictated that the practice of orthopaedics shifts to focus on acute care whilst redistributing resources to front-line specialities. This was facilitated by the cancellation of electives and the reduction of outpatient clinics. Thus, it is demonstrated that major changes have been implemented in many aspects of orthopaedic practice in order to address the challenges of the COVID-19 pandemic

Two-Stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant

Intelligent personal assistants (IPA) enable voice applications that facilitate people's daily tasks. However, due to the complexity and ambiguity of voice requests, some requests may not be handled properly by the standard natural language understanding (NLU) component. In such cases, a simple reply like “Sorry, I don't know” hurts the user's experience and limits the functionality of IPA. In this paper, we propose a two-stage shortlister-reranker recommender system to match third-party voice applications (skills) to unhandled utterances. In this approach, a skill shortlister is proposed to retrieve candidate skills from the skill catalog by calculating both lexical and semantic similarity between skills and user requests. We also illustrate how to build a new system by using observed data collected from a baseline rule-based system, and how the exposure biases can generate discrepancy between offline and human metrics. Lastly, we present two relabeling methods that can handle the incomplete ground truth, and mitigate exposure bias. We demonstrate the effectiveness of our proposed system through extensive offline experiments. Furthermore, we present online A/B testing results that show a significant boost on user experience satisfaction

Evaluating the Impact of a Collaborative Care Model in Diabetes Management in a Primary Healthcare Setting in Qatar Using Real-World Data

Objectives: To evaluate the impact of Collaborative Care Model (CCM) on diabetes-related outcomes among patients with diabetes attending a primary care setting. Methods: A multiple-time series, retrospective, observational study with a control group among patients with diabetes followed-up at Qatar Petroleum Diabetes Clinic. The impact of CCM on glycemic control, blood pressure, lipid profile, and anthropometrics was evaluated at baseline and up to 17 months of follow-up. Quantitative data were analyzed descriptively and inferentially using SPSS. Results: CCM significantly improved (p<0.05) the mean values (baseline vs. 17 months) of glycated hemoglobin A1c (6.9% vs. 6.5%), random blood glucose (194.38 mg/dL vs. 141.23 mg/dL), low-density lipoprotein cholesterol (3.7 mmol/L vs. 2.8 mmol/L), total cholesterol (5.43 mmol/L vs. 4.34 mmol/L), weight (78.52 Kg vs. 77.85 Kg), and body mass index (30.41 Kg/m2 vs. 30.17 Kg/m2) over 17-months within the intervention group; whereas, no significant changes occurred within the control group. Similarly, the between group comparisons demonstrated the superiority of CCM over usual care in improving several clinical outcomes. Conclusion: Inefficiencies in delivering diabetes care can be circumvented by the integration of CCM. The implementation of CCM in a primary healthcare setting improved several diabetes-related outcomes over 17-months

Assessing The Factual Accuracy of Generated Text

We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy). We introduce and release a new large-scale dataset based on Wikipedia and Wikidata to train relation classifiers and end-to-end fact extraction models. The end-to-end models are shown to be able to extract complete sets of facts from datasets with full pages of text. We then analyse multiple models that estimate factual accuracy on a Wikipedia text summarization task, and show their efficacy compared to ROUGE and other model-free variants by conducting a human evaluation study

An efficient heuristic method for active feature acquisition and its application to protein-protein interaction prediction

<p>Abstract</p> <p>Background</p> <p>Machine learning approaches for classification learn the pattern of the feature space of different classes, or learn a boundary that separates the feature space into different classes. The features of the data instances are usually available, and it is only the class-labels of the instances that are unavailable. For example, to classify text documents into different topic categories, the words in the documents are features and they are readily available, whereas the topic is what is predicted. However, in some domains obtaining features may be resource-intensive because of which not all features may be available. An example is that of protein-protein interaction prediction, where not only are the labels ('interacting' or 'non-interacting') unavailable, but so are some of the features. It may be possible to obtain at least some of the missing features by carrying out a few experiments as permitted by the available resources. If only a few experiments can be carried out to acquire missing features, which proteins should be studied and which features of those proteins should be determined? From the perspective of machine learning for PPI prediction, it would be desirable that those features be acquired which when used in training the classifier, the accuracy of the classifier is improved the most. That is, the <it>utility </it>of the feature-acquisition is measured in terms of how much acquired features contribute to improving the accuracy of the classifier. Active feature acquisition (AFA) is a strategy to preselect such instance-feature combinations (i.e. protein and experiment combinations) for maximum utility. The goal of AFA is th<it>e creation of optimal training set </it>that would result in the best classifier, and not in determining the best classification model itself.</p> <p>Results</p> <p>We present a heuristic method for active feature acquisition to calculate the utility of acquiring a missing feature. This heuristic takes into account the change in belief of the classification model induced by the acquisition of the feature under consideration. As compared to random selection of proteins on which the experiments are performed and the type of experiment that is performed, the heuristic method reduces the number of experiments to as few as 40%. Most notable characteristic of this method is that it does not require re-training of the classification model on every possible combination of instance, feature and feature-value tuples. For this reason, our method is far less computationally expensive as compared with previous AFA strategies.</p> <p>Conclusions</p> <p>The results show that our heuristic method for AFA creates an optimal training set with far less features acquired as compared to random acquisition. This shows the value of active feature acquisition to aid in protein-protein interaction prediction where feature acquisition is costly. Compared to previous methods, the proposed method reduces computational cost while also achieving a better F-score. The proposed method is valuable as it presents a direction to AFA with a far lesser computational expense by removing the need for the first time, of training a classifier for every combination of instance, feature and feature-value tuples which would be impractical for several domains.</p

Active learning for human protein-protein interaction prediction

Abstract Background Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome. Results Random forest (RF) has previously been shown to be effective for predicting protein-protein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data. Conclusion Active learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification.</p

Active Learning for Human Protein-Protein Interaction Prediction

Background: Biological processes in cells are carried out by means of protein protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome. Results: Random forest (RF) has previously been shown to be effective for predicting proteinprotein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data. Conclusion: Active learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification.</p

Impact of pharmacist-involved collaborative care on diabetes management in a primary healthcare setting using real-world data

Background Diabetes mellitus is a complex multi-system disorder, requiring multi-disciplinary care. The conventional care model, where physicians are the sole caregivers may not be optimal. Addition of other healthcare team members improves healthcare outcomes for patients with diabetes. Aim To evaluate the impact of pharmacist-involved collaborative care on diabetes-related outcomes among patients with diabetes attending a primary healthcare setting in Qatar using real-world data. Method A retrospective cohort study was conducted among patients with diabetes attending Qatar Petroleum Diabetes Clinic. Patients were categorized as either receiving pharmacist-involved collaborative care (intervention group) or usual care (control group). Data were analyzed using SPSS®. Glycemic control (glycated hemoglobin A1c, HbA1c), blood pressure, lipid profile, and body mass index were evaluated at baseline and up to 17 months of follow-up. Results After 17 months of follow-up, pharmacist-involved collaborative care compared to usual care resulted in a significant decrease in HbA1c (6.8 ± 1.2% vs. 7.1 ± 1.3%, p < 0.01). Moreover, compared to baseline, pharmacist-involved collaborative care significantly improved (p < 0.05) the levels of HbA1c (7.5% vs. 6.8%), low-density lipoprotein cholesterol (3.7 mmol/L vs. 2.8 mmol/L), total cholesterol (5.43 mmol/L vs. 4.34 mmol/L), and body mass index (30.42 kg/m2 vs. 30.17 kg/m2) after 17 months within the intervention group. However, no significant changes for these parameters occurred within the control group. Conclusion The implementation of pharmacist-involved collaborative care in a primary healthcare setting improved several diabetes-related outcomes over 17 months. Future studies should determine the long-term impact of this care model.The authors acknowledge Qatar University for funding the research through the Office of Research Support grant numbers QUST-2-CPH-2019-1 and QUST-2-CPH-2018-12.Scopu