Stress, safety and surgical performance

Imperial Users onl

Systems Approach to daily clinical care

AbstractSafety and quality of healthcare provision are affected by a number of factors. These factors include the clinical skills of the treating surgeon or other physician, but also the way practitioners think and behave as members of a healthcare team, and the clinical environment in which care is provided. We first discuss Bahal et al.’s paper as a demonstration of the Systems Approach to clinical performance and patient safety. We then highlight recent advances driven by the Systems Approach in understanding and measuring clinical decision-making, teamworking, and the clinical environment. We conclude that human factors research can provide an understanding of how to balance conflicting opinions and priorities in clinical care with the best interests of the patient, in a manner which allows each doctor to fulfil their duty of care

Review Paper on Sentiment Analysis of The Demonetization of Economy

Sentiment analysis is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has increased the interest and attention of many researchers in recent years. Today online reviews of sentiment analysis on Demonetization has become a hot research field. Demonetization was a unique event for Indians which was organized by PM of India. On 8th nov,2016 PM of India declared a decision of Demonetization under which notes of 500 & 1000 were banned to tackle the problem of corruption, terrorist funding and black money. Sentiment analysis on Demonetization mainly focus on framework of lexicon construction, feature extraction, polarity determination, maximum entropy and SVM. This paper explains the survey on the latest development in sentiment analysis. The methods used in current research are especially emphasized to analysis the sentiments of Indians on Demonetization. Finally, in this paper we pointed out some possible future directions of research

Review paper On Modifed RSA for Cloud Computing MRSA -ENCRYPTION

Cloud computing is advance practical application for healthy communication over the internet. The internet supplies some layer of network facilities via which they pass on the above remote network. The cloud is virtual network hub where we store our confidential data and make it secure. The cloud gives some facilities one authenticate users can access our data. The cloud authentication service is an approach and authentication integrity with a hybrid cloud framework. The cloud authentication service enables your association to control how user�s access resources with centralized access along with authentication policies also can raise user productivity with single sign-on sso

Análisis comparativo de K-NN, Naïve-Bayes y regresión logística para la detección de fraude con tarjetas de crédito

Introduction: This paper highlights the outcome of the comparative study of “Various Machine learning algorithms namely K-NN, Naive Bayes, and Logistic Regression for Credit Card Fraud Detection” carried out based on a dataset taken from UCI.com in 2022-23 at Manav Rachna International Institute of Research and Studies. Problem: Credit card fraud is still rife today and the modes are increasingly varied. Quite often we hear of fraud cases that cause irreplaceable injury to banks and financial institutions which cannot be compensated in terms of costs. To avoid scams with various modes of credit cards, we must be able to identify and find out the modes often used by fraudsters. This scheme liberates such financial institutions and banks with complete and appropriate information using Machine Learning Techniques, not only about the modes that scammers or fraudsters often use but also ways to protect against such frauds. Objective: The present paper discusses the various machine learning models based on classification and regression, namely K-Nearest Neighbors, Naïve Bayes, and Logistic Regression, which are successfully able to achieve the classification accuracy of 80% using Logistic Regression with a Precision of 78%, Recall of 100%, and F1-Score of 88% for fraudulent credit card transactions. Methodology: The comparative analysis demonstrates that for Precision, Recall, and Accuracy parameters, the K-Nearest Neighbor is a better approach for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes. Results: The accuracy is marginal high in Logistic Regression but the False Positive parameters are not able to identify the imbalanced data; therefore, they disguise the results and accuracy of Logistic Regression and K-Nearest Neighbor deems fit for such cases. Conclusion: This scheme depicts the automated fraud classification systems using machine learning techniques, namely K-Nearest Neighbor, Logistic Regression, and Naive Bayes, to produce a model that can distinguish valid and invalid credit card transactions. Originality: Through this research, the most relevant features are used to go through the visualization of accuracy with the confusion matrix, and accuracy calculations are obtained from the dataset used.Limitations: Deep learning techniques could have been used to fetch even better results.Introducción: este artículo muestra el resultado de un estudio comparativo de “varios algoritmos de machine learning, a saber, K-NN, Naïve-Bayes y regresión logística para la detección de fraudes con tarjetas de crédito”, realizado con base en un conjunto de datos tomado de UCI.com en 2022-23 en el Instituto Internacional de Investigaciones y Estudios Manav Rachna. Problema: el fraude con tarjetas de crédito está muy extendido hoy en día y las modalidades son cada vez más variadas. A menudo, se oye hablar de casos de fraude que causan daños irreparables a bancos e instituciones financieras, que no pueden ser compensados en términos de costos. Para evitar estafas con diversos modos de tarjetas de crédito, se debe poder identificar y descubrir los modos que suelen utilizar los estafadores. Este esquema proporciona a dichas instituciones financieras y bancos información completa y adecuada utilizando técnicas de machine learning, no solo sobre los modos que suelen utilizar los estafadores o defraudadores, sino también sobre las formas de protegerse contra dichos fraudes. Objetivo: el presente artículo analiza los diversos modelos de machine learning basados en clasificación y regresión, a saber, K-Nearest Neighbors (K-NN), Naïve Bayes y regresión logística, que pueden lograr con éxito una precisión de clasificación del 80% utilizando regresión logística con una precisión de 78%, Retiro del 100% y F1 Score del 88% para transacciones fraudulentas con tarjeta de crédito. Método: el análisis comparativo muestra que, para los parámetros de precisión, recuperación y exactitud, el K-NN es un mejor enfoque para detectar transacciones fraudulentas que la regresión logística y el Naïve Bayes.Resultados: la precisión es marginalmente alta en la regresión logística, pero los parámetros de falso positivo no pueden identificar los datos desequilibrados; por lo tanto, disfrazan los resultados y la precisión de la regresión logística y el K-NN se considera adecuado para tales casos. Conclusión: este esquema describe los sistemas automatizados de clasificación de fraude que utilizan técnicas de machine learning, a saber, K-NN, Regresión logística y Naïve Bayes, para producir un modelo que pueda distinguir transacciones con tarjetas de crédito válidas e inválidas. Originalidad: a través de esta investigación, se utilizan las características más relevantes para visualizar la precisión con la matriz de confusión y se obtienen cálculos de precisión a partir del conjunto de datos utilizado.Limitaciones: se podrían haber utilizado técnicas de Deep learning para obtener mejores resultados

Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog

The task of identifying out-of-domain (OOD) input examples directly at test-time has seen renewed interest recently due to increased real world deployment of models. In this work, we focus on OOD detection for natural language sentence inputs to task-based dialog systems. Our findings are three-fold: First, we curate and release ROSTD (Real Out-of-Domain Sentences From Task-oriented Dialog) - a dataset of 4K OOD examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, our examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset. Second, we explore likelihood ratio based approaches as an alternative to currently prevalent paradigms. Specifically, we reformulate and apply these approaches to natural language inputs. We find that they match or outperform the latter on all datasets, with larger improvements on non-artificial OOD benchmarks such as our dataset. Our ablations validate that specifically using likelihood ratios rather than plain likelihood is necessary to discriminate well between OOD and in-domain data. Third, we propose learning a generative classifier and computing a marginal likelihood (ratio) for OOD detection. This allows us to use a principled likelihood while at the same time exploiting training-time labels. We find that this approach outperforms both simple likelihood (ratio) based and other prior approaches. We are hitherto the first to investigate the use of generative classifiers for OOD detection at test-time.Comment: Accepted for AAAI-2020 Main Trac

Effect of antibiotics on inflammatory marker (IL-6) and perinatal outcomes in women with preterm premature rupture of membranes

Background: The aim is to study the effect of antibiotics on inflammatory marker (IL-6) and perinatal outcomes in women with preterm premature rupture of membranes (PPROM).Methods: 60 women with PPROM at 28–34 weeks of gestation were enrolled in the study. All the women were given antibiotics as per hospital protocol and were subjected to blood sampling for Interleukin -6(IL-6) at admission, delivery and umbilical cord blood. IL-6 levels were assessed for associations with adverse perinatal outcomes and the effect of antibiotic treatment on IL-6 and perinatal outcomes were studied. The data were analyzed using t test and χ2 test.Results: Increased level of IL-6 was associated with chorioamnionitis and neonatal sepsis (p<0.001). High level of IL-6 led to early delivery and complete course of antibiotics suppressed IL-6 (p<0.001) and clinical chorioamnionitis in women with PROM. Full course of antibiotics also decreased the admission rate of babies to neonatal intensive care unit and suppressed respiratory distress syndrome, neonatal sepsis.Conclusions: Increased level of IL-6 is seen in women with chorioamnionitis and neonatal sepsis. Antibiotics suppress the IL-6 levels, chorioamnionitis and neonatal sepsis

Spectrum of Childhood and Adolescent Ovarian Tumors in India: 25 Years Experience at a Single Institution

BACKGROUND: Ovarian tumour in children and adolescent girls form an uncommon but important part of gynaecological malignancies. They account for 1% of all the childhood malignancies and 8% of all abdominal tumours in children. Since the ovarian cysts are thought to arise from mature follicles, these tumours were considered to be infrequent in the paediatric population.AIM: The rarity of this condition prompted us to conduct this study and share our experience on the incidence and clinicopathological features of different ovarian tumours in girls up to 20 years of age observed in last 25 years at a single tertiary care hospital.MATERIAL AND METHODS: This was a retrospective study conducted in the Department of Pathology at a tertiary hospital, Delhi. All ovarian tumours up to the age of 20 years in the past 25 years (1990-2014) were included for the purpose of studying the clinicopathological aspects of ovarian tumours in this age group. Descriptive statistics for prevalence and age-wise prevalence was done. Chi-square test, to find an association between the age, laterality and size with malignancy was performed.RESULTS: We received a total of 1102 cases of ovarian tumours over the period of 25 years  (1990 to 2014), of which 112 (10%) cases were seen in girls up to 20 years of age. The mean age of the patients was 15.3 ± 4 years. The most common presenting complaint was pain abdomen (46.4 %) There was a statistically significant correlation found between size and malignancy status of tumours in our study (p = 0.00). Of 112 cases of ovarian tumours, 39/112 (34.8%) were malignant and 73/112 (65.2%) were benign. Mature  cystic teratoma (27.6%) was the most common type of benign tumour in this age group and immature teratomas were the most common type of malignant ovarian neoplasms.CONCLUSION: Premenarchal girls with ovarian masses may have varied presentations. Abdominal pain is the most common presenting complaint of young adolescent girls with adnexal masses. So the index of suspicion should be kept high and prompt investigations like ultrasound must be performed early to rule out such adnexal masses. Immature Teratoma was the most common malignant and mature cystic teratoma was the most common benign tumour in our study