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6 results on '"Ravikumar Patel"'

1. STUDY OF SERUM URIC ACID LEVELS AND KILLIP CLASSIFICATION IN PREDICTING PROGNOSIS OF ACUTE MYOCARDIAL INFARCTION.

Author

Arvind C. L., Shashidhar, Ravikumar Patel, G. N., and Manjunath F. V.

Subjects
MYOCARDIAL infarction, URIC acid, CORONARY disease, MYOCARDIAL ischemia, ACUTE coronary syndrome, TYPE 2 diabetes
Abstract

The present study has been undertaken with the following High uric acid level is a negative prognostic factor in patients with myocardial infarction. In myocardial infarction higher the uric acid level increases the risk of mortality rate. There is uncertainty about the role of uric acid in acute coronary syndrome and whether it could be used as a prognostic marker in MI patients. A detailed clinical examination was performed in all patients. 100 patients of acute myocardial infarction who fulfilled inclusion/exclusion criteria were enrolled for the study. Thirty three percent patients were known diabetic in our study. Non-diabetic and diabetic patients had comparable serum uric acid levels on Day-0 This finding is consistent with study which there was no significant association between serum uric acid level and diabetic status. However, this finding contrasts with other study which showed that hyper uricemia is significantly associated with type 2 diabetes mellitus. Twenty one percent patients had history of ischemic heart disease. There was significant difference between serum uric acid concentration at the time of admission and h/o ischemic heart disease [ABSTRACT FROM AUTHOR]

Published
2023

2. Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

Author

Ravikumar Patel and Kalpdrum Passi

Subjects
POS tagging, maximum entropy, Computer science, SVM, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, KNN, WordNet, 02 engineering and technology, Lexicon, Machine learning, computer.software_genre, Naive Bayes classifier, data preprocessing, naïve Bayes, 0202 electrical engineering, electronic engineering, information engineering, word tokenization, Parsing, business.industry, Lemmatisation, Sentiment analysis, 020206 networking & telecommunications, name entity recognition, word stemming and lemmatizing, Random forest, Support vector machine, machine learning, Tokenization (data security), natural language processing (NLP), 020201 artificial intelligence & image processing, Artificial intelligence, Data pre-processing, business, computer, random forest, Sentence, Natural language
Abstract

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using na&iuml, ve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Na&iuml, ve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

Published
2020
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3. Xanthomonas vasicola pv. vasculorum (bacterial leaf streak of corn)

Author

Lindsay Triplett and Ravikumar Patel

Abstract

Xanthomonas vasicola pv. vasculorum (Xvv) is a bacterial pathogen that causes both bacterial leaf streak of maize and sugarcane gumming disease. After decades limited to South Africa, bacterial leaf streak of maize spread rapidly through maize-growing areas of Argentina, Brazil and the USA since 2014. The origin, method and biological underpinnings of this sudden spread are not well understood but are the subject of active research. Effective control methods remain elusive, but sanitation and crop debris management may limit the disease. Yield impact data are not yet available, but lesions may become severe enough to limit plant productivity in some varieties. The pathogen is not currently considered a quarantine threat by the USDA, EPPO or IPPC. Taxonomic Tree Top of page Domain: Bacteria Phylum: Proteobacteria Class: Gammaproteobacteria Order: Xanthomonadales Family: Xanthomonadaceae Genus: Xanthomonas Species: Xanthomonas vasicola pv. vasculorum Notes on Taxonomy and Nomenclature Top of page The taxonomic nomenclature of the pathogen has undergone several changes and is still being resolved at the time of this report. The Xanthomonas clade causing.

Published
2021
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5. Serum calcium level as a prognostic marker in acute pancreatitis – one year hospital based cross-sectional study.

Author

Sangapur, Ravi N., Patil, Vivek M., Ravikumar Patel, G. N., and Patil, Rekha

Subjects
PROGNOSIS, PANCREATITIS, BLADDER stones, CALCIUM, TERTIARY care, NECROTIZING pancreatitis, CHRONIC pancreatitis
Abstract

Introduction: There is no simple biomarker which can serve as the single parameter to determine the severity and prognosis in patients with Acute Pancreatitis. Hence this study was aimed to determine the role of serum calcium level as a prognostic marker in Acute Pancreatitis. Methodology: This study was conducted in the medical wards of a tertiary care hospital. 100 patients with diagnosis of acute pancreatitis were enrolled. This was a one year cross-sectional study from January 2018 to December 2018. Results: In this study male preponderance was more (86%) than females and the ratio was 6.14:1. The commonest age group was between 20 to 40 years (64%) and the mean age was 39.21 ± 14.08 years. Ethanol (68%) was the commonest etiology followed by gall bladder calculi (24%). Abdominal pain was the common clinical presentation (80%) and epigastric tenderness the common clinical sign (70%). Maximum patients had moderate CT Severity Index(CTSI) score (64%). Serum calcium <8.5 mg/dl was found in 64 patients of which 45 had moderate pancreatitis, 14 had severe pancreatitis and 5 had mild pancreatitis. On comparing the serum calcium level with the CTSI, there was decrease in value of serum calcium in patients who had higher CTSI. A significant inverse co-relation (p=0.0001) was observed between CTSI and serum calcium levels indicating that hypocalcemia was associated with poor prognosis of Acute Pancreatitis. Conclusion: Serum calcium level can be used as simple marker to assess the severity and prognosis of Acute Pancreatitis and helps determine need for intensive care management as it is easily available in primary and secondary care centers. [ABSTRACT FROM AUTHOR]

Published
2022

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