Towards Quantifying the Distance between Opinions

Increasingly, critical decisions in public policy, governance, and business strategy rely on a deeper understanding of the needs and opinions of constituent members (e.g. citizens, shareholders). While it has become easier to collect a large number of opinions on a topic, there is a necessity for automated tools to help navigate the space of opinions. In such contexts understanding and quantifying the similarity between opinions is key. We find that measures based solely on text similarity or on overall sentiment often fail to effectively capture the distance between opinions. Thus, we propose a new distance measure for capturing the similarity between opinions that leverages the nuanced observation -- similar opinions express similar sentiment polarity on specific relevant entities-of-interest. Specifically, in an unsupervised setting, our distance measure achieves significantly better Adjusted Rand Index scores (up to 56x) and Silhouette coefficients (up to 21x) compared to existing approaches. Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarityComment: Accepted in ICWSM '2

Pattern of use of antibiotics in hospitalized patients in the medicine department of a tertiary care hospital

Background: (1) To assess pattern of antibiotic use among in-patients of medicine unit in a tertiary care hospital, (2) to determine the frequency of adverse drug reactions (ADR) among the inpatients receiving antibiotics in medicine unit.Methods: The study was prospective and based on the daily review of patient records for 2 months (June, July) of study period, including all the inpatients of medicine unit 1 receiving antimicrobials. The general information of the patients, infection, antimicrobial use, culture and sensitivity reports, concomitant disease, concomitantly administered drugs, as well as clinical response were collected. The prescribed antimicrobials were correlated with the patient’s culture and sensitivity report. The number of defined daily doses (DDDs) administered per patient was calculated for each antimicrobial prescribed as per WHO anatomical therapeutic chemical classification. The ADR observed during the study were assessed using WHO causality analysis. The economic burden of the antimicrobial used was analyzed using average cost of antimicrobial per patient. The study was approved by the Institute Ethics Committee.Results: The antimicrobials that are commonly used as per total drug use (DDDs) are ceftriaxone followed by doxycycline and metronidazole. The antimicrobials account for 58.6% of cost spent on drugs for inpatients. Four antimicrobial related ADR were reported during the study period.Conclusion: Ceftriaxone, doxycycline, and metronidazole are commonly used antibiotics and significant proportion of the cost of drugs is spent for antimicrobials in a medicine unit

Elizabethkingia meningoseptica causing meningitis after decompressive craniectomy for traumatic brain injury in a immunocompetent adult: Serious Neurocritical care Concern

Elizabethkingia is aerobic Gram negative bacteria is widely distributed in soil, food and food. It is very rarely pathogenic for human and especially those who are immunocompromised and various risk factors includes intravenous catheter, ventilatory support. We report a case, who had undergone decompressive craniectomy for traumatic brain injury, developed meningitis, CSF culture showed growth of Elizabethkingia and treated successfully with antibiotics therapy. This study reminds that rare pathogen should also be considered as causative organism for meningitis especially in postoperative cases and it responds well to antimicrobial therapy

Towards Quantifying the Distance between Opinions

The Fourteenth International AAAI Conference on Web and Social Media (ICWSM 2020), Atlanta, Georgia (held online due to coronavirus outbreak), 8-11 June 2020Increasingly, critical decisions in public policy, governance, and business strategy rely on a deeper understanding of the needs and opinions of constituent members (e.g. citizens, shareholders). While it has become easier to collect a large number of opinions on a topic, there is a necessity for automated tools to help navigate the space of opinions. In such contexts understanding and quantifying the similarity between opinions is key. We find that measures based solely on text similarity or on overall sentiment often fail to effectively capture the distance between opinions. Thus, we propose a new distance measure for capturing the similarity between opinions that leverages the nuanced observation -- similar opinions express similar sentiment polarity on specific relevant entities-of-interest. Specifically, in an unsupervised setting, our distance measure achieves significantly better Adjusted Rand Index scores (up to 56x) and Silhouette coefficients (up to 21x) compared to existing approaches. Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity

Learning fine-grained search space pruning and heuristics for combinatorial optimization

Combinatorial optimization problems arise naturally in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them requires considerable time, effort and experimentation. On the other hand, the number of optimization problems in the industry continues to grow. In recent years, machine learning techniques have been explored to address this gap. In this paper, we propose a novel framework for leveraging machine learning techniques to scale-up exact combinatorial optimization algorithms. In contrast to the existing approaches based on deep-learning, reinforcement learning and restricted Boltzmann machines that attempt to directly learn the output of the optimization problem from its input (with limited success), our framework learns the relatively simpler task of pruning the elements in order to reduce the size of the problem instances. In addition, our framework uses only interpretable learning models based on intuitive local features and thus the learning process provides deeper insights into the optimization problem and the instance class, that can be used for designing better heuristics. For the classical maximum clique enumeration problem, we show that our framework can prune a large fraction of the input graph (around 99% of nodes in case of sparse graphs) and still detect almost all of the maximum cliques. Overall, this results in several fold speedups of state-of-the-art algorithms. Furthermore, the classification model used in our framework highlights that the chi-squared value of neighborhood degree has a statistically significant correlation with the presence of a node in a maximum clique, particularly in dense graphs which constitute a significant challenge for modern solvers. We leverage this insight to design a novel heuristic we call ALTHEA for the maximum clique detection problem, outperforming the state-of-the-art for dense graphs.Access provided by IREL Consortium c/o Maynooth University The Library Maynooth Universit

ANN based peak power tracking for PV supplied DC motors.

The report presents an application of an Artificial Neural Network(ANN) for the identification of the optimal operating point of a PV supplied separately excited dc motor driving two different load torques. A gradient descent algorithm is used to train the ANN controller for the identification of the maximum power point of the Solar Cell Array (SCA) a gross mechanical energy operation of the combined system. The algorithm is developed based on matching of the SCA to the motor load through a buck-boost power converter so that the combined system can operate at the optimum point. The input parameter to the neural network is solar insulation and the output parameter is the converter chopping ratio corresponding to the maximum power output of the SCA or gross mechanical energy output of the combined PV system. The converter chopping ratios at different solar insulations are obtained from the ANN controller for two different load torques and are compared with computed values

Accuracy of 3D printed spine models for pre-surgical planning of complex adolescent idiopathic scoliosis (AIS) in spinal surgeries: a case series

Adolescent idiopathic scoliosis (AIS) is a noticeable spinal deformity in both adult and adolescent population. In majority of the cases, the gold standard of treatment is surgical intervention. Technological advancements in medical imaging and 3D printing have revolutionised the surgical planning and intraoperative decision making for surgeons in spinal surgery. However, its applicability for planning complex spinal surgeries is poorly documented with human subjects. The objective of this study is to evaluate the accuracy of 3D printed models for complex spinal deformities based on Cobb angles between 40° to 95°.This is a retrospective cohort study where, five CT scans of the patients with AIS were segmented and 3D printed for evaluating the accuracy. Consideration was given to the Inter-patient and acquisition apparatus variability of the CT-scan dataset to understand the effect on trueness and accuracy of the developed CAD models. The developed anatomical models were re-scanned for analysing quantitative surface deviation to assess the accuracy of 3D printed spinal models. Results show that the average of the root mean square error (RMSE) between the 3DP models and virtual models developed using CT scan of mean surface deviations for the five 3d printed models was found to be 0.5§0.07 mm. Based on the RMSE, it can be concluded that 3D printing based workflow is accurate enough to be used for presurgical planning for complex adolescent spinal deformities. Image acquisition and post processing parameters, type of 3D printing technology plays key role in acquiring required accuracy for surgical applications