The rapid spread of COVID-19 worldwide has claimed thousands of lives and has put unprecedented pressure on the healthcare systems around the world. The World Health Organization has emphasised the need for comprehensive testing in order to fight the virus [1]. With the lack of testing kits available worldwide, there is a call for novel testing methods that can help arrest the spread faster [18]. Every health care worker exposed to the virus puts additional pressure on an already outstretched infrastructure. Here, in this study we propose a machine learning approach towards predicting Covid-19 cases among a sample population who have undergone other clinical tests and blood spectrum tests. The patient data used in this effort has been donated by Hospital Israelita Albert Einstein, at São Paulo, Brazil [6] for the purpose of research. The problem at hand is divided into two parts: Predict confirmed COVID-19 cases amongst suspected cases based on the laboratory tests of their clinical samples. Predict admission to general, semi-ICU, and ICU wards among those who predicted positive for COVID-19 in the first task. Our approach uses Classification from Supervised Learning techniques to solve this problem. The efficacy of this approach could be used to scale and develop automated systems that could predict the likeliness of Covid-19 based on laboratory tests that are readily accessible. From the features presented to us in the dataset, we are able to predict with 87.0 - 97.4 percent accuracy at a 95 percent confidence level that a patient is suffering from Covid-19 when biomarkers are taken into consideration. Among those that tested positive, we were able to demonstrate that our model could predict with 87.0 - 100 percent accuracy at a 95 percent confidence that whether the patient would be admitted to a particular ward.
Introduction to ICSR Narrative Writing in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
A Study on Pattern of Using Prophylactic Antibiotics in Caesarean Sectioniosrphr_editor
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
Introduction to ICSR Narrative Writing in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
A Study on Pattern of Using Prophylactic Antibiotics in Caesarean Sectioniosrphr_editor
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
Presentation: Global pharmacovigilance networks - A regulator'sTGA Australia
Global pharmaceutical companies manufacture and distribute a broad portfolio of drug products in multiples regions and countries. The pharmacovigilance system must ensure safety data collection in compliance with local regulations, and consolidate all sources to ensure an ongoing monitoring of potential changes in benefit-risk profiles. It must also guarantee a timely communication to patients, prescribers and regulatory authorities. The complexity resides in the need for a dense network of local safety departments, a strong global organisation processing and analysing cases, and a reporting system ensuring compliance to heterogeneous regulatory requirements. Pfizer has one of the largest pharmacovigilance department among all global companies, and has established patient safety as a core priority. We will describe how pharmacovigilance is organised at Pfizer, global compliance and individual patient safety.
Presentation at Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021 organized by the scientific research group in Egypt with Collaboration with Faculty of Computers and AI, Cairo University and the Chinese University in Egypt
Four Unique Laboratory Characteristics Applied to Assess the Severity of COVI...semualkaira
The sudden outbreaking of COVID-19 worldwide has
brought into sharp increased burden of economic and treatment.
How to simply, quickly and accurately assess the severity of patients with COVID-19 in the early stage after hospital admission is
essential for healthcare systems
Four Unique Laboratory Characteristics Applied to Assess the Severity of COVI...komalicarol
The sudden outbreaking of COVID-19 worldwide has
brought into sharp increased burden of economic and treatment.
How to simply, quickly and accurately assess the severity of patients with COVID-19 in the early stage after hospital admission is
essential for healthcare systems.
Presentation: Global pharmacovigilance networks - A regulator'sTGA Australia
Global pharmaceutical companies manufacture and distribute a broad portfolio of drug products in multiples regions and countries. The pharmacovigilance system must ensure safety data collection in compliance with local regulations, and consolidate all sources to ensure an ongoing monitoring of potential changes in benefit-risk profiles. It must also guarantee a timely communication to patients, prescribers and regulatory authorities. The complexity resides in the need for a dense network of local safety departments, a strong global organisation processing and analysing cases, and a reporting system ensuring compliance to heterogeneous regulatory requirements. Pfizer has one of the largest pharmacovigilance department among all global companies, and has established patient safety as a core priority. We will describe how pharmacovigilance is organised at Pfizer, global compliance and individual patient safety.
Presentation at Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021 organized by the scientific research group in Egypt with Collaboration with Faculty of Computers and AI, Cairo University and the Chinese University in Egypt
Four Unique Laboratory Characteristics Applied to Assess the Severity of COVI...semualkaira
The sudden outbreaking of COVID-19 worldwide has
brought into sharp increased burden of economic and treatment.
How to simply, quickly and accurately assess the severity of patients with COVID-19 in the early stage after hospital admission is
essential for healthcare systems
Four Unique Laboratory Characteristics Applied to Assess the Severity of COVI...komalicarol
The sudden outbreaking of COVID-19 worldwide has
brought into sharp increased burden of economic and treatment.
How to simply, quickly and accurately assess the severity of patients with COVID-19 in the early stage after hospital admission is
essential for healthcare systems.
Investigation of Long term Hazards and Multi organ Impact of SARS COV-2 in Po...Jagruti Marathe
Introduction
Background
Burden of COVID 19
Need of the study
Rationale of the study
Review of literature
Epidemiology
Hypothesis
Aim and objective
Material and Method
Criteria
Study design
Outcome
Result
Analysis
Discussion
Coronavirus are a large family of viruses that causes illness ranging from the common cold to more serve disease such as middle east respiratory syndrome(MERS-COV) and sever acute respiratory syndrome (SARS-COV).
A novel corona virus (nCOV) is a new strain that has not been previously identified in humans.
SARS-CoV-2 belongs to the Single Standing RNA Viruses class of coronaviruses, but the infection had been rapidly spreading around the world and World Health Organization (WHO) declared a pandemic .
Considerations for Diagnostic COVID-19 Tests in the 4 Medical Testing Centre ...semualkaira
In this study during the coronavirus disease in 2021 (COVID-19)
pandemic, design, development, validation, verification incidence
and implementation of diagnostic tests are reported by many diagnostic tests from May until December 2021 we managed to
establish clinical validation and formal approval. In this article
we summarize the crucial role of diagnostic tests during the first
global wave of COVID-19. The technical and implementation and
diagnostics during a possible resurgence of COVID-19 in future
global waves or regional outbreaks. We continued global improvement in diagnostic test that is essential for more rapid detection of
patients, possibly at the point of care, and for optimized prevention
and treatment
Coronavirus disease 2019 detection using deep features learningIJECEIAES
A Coronavirus disease 2019 (COVID-19) pandemic detection considers a critical and challenging task for the medical practitioner. The coronavirus disease spread so rapidly between people and infected more than one hundred and seventy million people worldwide. For this reason, it is necessary to detect infected people with coronavirus and take action to prevent virus spread. In this study, a COVID-19 classification methodology was adopted to detect infected people using computed tomography (CT) images. Deep learning was applied to recognize COVID-19 infected cases for different patients by employing deep features. This methodology can be beneficial for medical practitioners to diagnose infected patients. The results were based on a new data collection named BasrahDataset that includes different CT scan videos for Iraqi patients. The proposed system gave promised results with a 99% F1-score for detecting COVID-19.
Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimizati...ijtsrd
The opinion of disease is important for Covid 19 as the antigen kit and RTPCR are unperfect and should be better for diagnosing such disease. Real Time Return Transcription real time converse transcription - polymerase chain . Healthcare practices include the collection of various sorts of patient data to help the physician diagnose the patients health. These data could be simple symptoms, first diagnosis by a doctor, or an in depth laboratory test. These data are therefore used for analyses only by a doctor, who subsequently uses his particular medical skills to found the ailment. In order to classify Covid 19 disease datasets such mild, middle and severe diseases, the proposed model utilizes the notion of controlled machine education and GWO optimization to regulate if the patient is affecting or not. An efficiency analysis is calculated and compared of disease data for both algorithms. The results of the simulations illustrate the effective nature and complexity of the data set for the grading techniques. Compared to SVM, the suggested model provides 7.8 percent improved prediction accuracy. The prediction accuracy is 8 better than the SVM. This results in an F1 score of 2 percent better than an SVM forecast. Swati Shilpi | Dr. Damodar Prasad Tiwari "Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid-19 Pandemic Crisis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46400.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/46400/health-risk-prediction-using-support-vector-machine-with-gray-wolf-optimization-in-covid19-pandemic-crisis/swati-shilpi
The Envisia Genomic Classifier is the first commercially available genomic test to improve the diagnosis of idiopathic pulmonary fibrosis (IPF). The test harnesses the power of RNA sequencing and machine learning to improve physicians’ ability to differentiate IPF from other interstitial lung diseases (ILD) without the need for invasive, risky and costly surgery.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
A Machine Learning Approach to Predicting Covid-19 Cases Amongst Suspected Cases and Their Category of Admission
1. A MACHINE LEARNING APPROACH
TO PREDICTING COVID-19 CASES
AMONGST SUSPECTED CASES
AND THEIR CATEGORY OF
ADMISSION
Dr. D Narayana, Amanpreet Singh,
Aparna Ranjith, Divya Dixit, Abhay,
Suparna Khan, Anwesh Reddy
Presented by
Amanpreet Singh
2. COVID-19 timeline
DEC’19
First documented
Covid-19 hospital
admission in
Wuhan, Hubei
JAN ‘20
Multiple cases
registered from
countries around
the globe.
FEB ‘20
WHO named the
disease Coronavirus
Disease - 2019
(COVID-19)
MAR ‘20
UN global response
launched by WHO.
Complete
lockdown imposed
in several countries
APR ‘20
Evidence of
transmission from
asymptomatic to
symptomatic and
pre-asymptomatic
people infected
with Covid-19 were
reported
4. Objective
● Predict confirmed COVID-19 cases amongst suspected cases
based on the laboratory tests of their clinical samples.
● Predict admission to general, semi-ICU, and ICU wards among
those who predicted positive for COVID-19 in the first task.
The World Health Organization has emphasized the need for
comprehensive testing in order to fight the virus . With the lack of
testing kits available worldwide, there is a call for novel testing methods
that can help arrest the spread effectively.
5. About the data
Source Hospital Israelita Albert Einstein, at São Paulo, Brazil
Date Range The data has been collected between March 28 - April 3, 2020
Total records 5644
Attributes 111
Data points used
for prediction
Subset I Subset II
Attributes: 20 Attributes:48
Records: 598 Records: 254
Standard
Deviation
1
Mean 0
Challenges Missing values and imbalanced data
6. Clinical Parameters
● Complete Blood Count Tests
● Liver Function Tests
● Renal Analysis (Kidney)
● Influenza Tests
● Blood gas analysis
● Salt tests
7. Logical Grouping of Attributes
Blood Liver Function Influenza Renal(Urine)
Haemoglobin Alanine
Transaminase
RSV Urine-ph
Red Blood Cells Aspartate
Transaminase
Influenza A Urine-Aspect
Lymphocytes Total Bilirubin Influenza B Urine-Color
C-Reactive
Protein
Indirect Bilirubin Rhinovirus/Enteroviru
s
Urine-RBC
Creatinine Alkaline
Phosphatase
Adenovirus Urine-Density
Potassium GGT Parainfluenza 3 Urine-Haemoglobin
9. Ward Distribution of Patients
Of all the positive patients
● 1.4% were admitted to ICU
● 1.4% were admitted to Semi-ICU
● 6.5% were admitted to Regular
Ward
10. Methodology
● Data Cleaning
● Imbalanced Data – Sampling(UpSampling and Downsampling)
● Imputation
● Classification
● Results
11. Feature Selection Criteria
The first subset has been made taking the features related to CBC along with age
quantile and three categorical variables representing the hospital admission and
the target variable (‘sars_cov2’). The information in these features is related to
RBC, WBC and platelets. A total of 20 features have been chosen with 598
records that have no missing values.
The second subset includes features related to blood, flu and liver parameters
along with age quantile and three categorical variables representing the hospital
admission and the target variable (‘sars_cov2’). It contains 48 attributes with 254
records. The larger dataset after careful filtering had missing values which were
eventually treated using a placeholder value.
For the ward prediction task, the ICU, semi-icu, regular ward were
merged into one categorical column with ordinal values 0,1,2,3
01
02
03
14. Model Performances
Model Performances
Model Training Accuracy(%) Testing Accuracy(%)
Logistic Regression 91.52 89.61
Decision Tree 90.39 83.11
Random Forest 97.17 94.80
Bagging 100 96.10
Gradient Boost 100 94.80
ADA Boosting 98.30 93.50
15. Conclusion
● Our results demonstrate that adding more bio-markers can help in
increasing prediction of covid-19 using machine learning approach.
● Further it can help in predicting severity of the disease (ward).
● Adding these methods to testing protocols can minimize contact for
healthcare workers.
● The model’s output can be used as a tool for prioritization and to support
further medical decision-making processes.
16. Future Enhancements
● Larger Dataset
● More ethnically representative sample data
● Capturing critical features like D-dimer, CRP
● Including X-ray
● Considering co-morbidities
Hello everyone, my name is Amanpreet Singh
In this study we propose a machine learning approach towards
predicting Covid-19 cases among a sample population who
have undergone other clinical tests and blood spectrum tests.
Before moving forward lets talk a little about the covid-19
–December ‘19 - First documented COVID-19 patient was admitted in the hospital in Wuhan, Hubei Province, China
–January ‘20 - Multiple cases registered from countries around the globe and WHO declared the COVID-19 outbreak as the sixth public health emergency of international concern
–February ‘20 - WHO named the disease caused by 2019-nCoV: Coronavirus Disease-2019 (COVID-19)
–March ‘20 - The UN Global Humanitarian Response Plan was launched by the WHO. Complete Lockdown was introduced in many countries.
–April ‘20 – Evidence of transmission from symptomatic, pre-symptomatic and asymptomatic people infected with COVID-19 were reported. WHO published a draft landscape of COVID-19 candidate vaccines
Developing countries with fewer testing resources have adopted conservative testing methods in order to conserve
testing kits on symptomatic and mild to severe cases. As more evidence surfaces around symptoms and associated
risks of Covid-19, it has been observed that blood examination can play a key role in the route to extensive testing.
The patient data used in this effort has been donated by Hospital Israelita Albert Einstein, at São Paulo, Brazil for
the purpose of research. The blood and clinical samples were collected of patients who visited the hospital
for a suspected infection of Covid.
The data was made available in a standardized and normalized form.
It has a unit standard deviation and a mean of zero
There was an imbalance in class distribution across both target columns ‘sars_cov2’ and ‘Ward’
The features which comprise various clinical parameters can
be broadly categorized under CBC, liver function, renal
analysis, salt tests, blood gas analysis (arterial and venous),
And influenza tests.
The CBC is indicative of an individual’s overall health and detects
a range of potential disorders but is not limited to anaemia, leukaemia, or inflammation
The attributes which have more than 98% of missing values were eliminated.
After Data Cleaning, it was observed that there was an imbalance in class distribution across both target columns
‘sars_cov2’ and ‘Ward’ so we have performed sampling there, both up sampling and down sampling.
The missing data was not imputed as imputations may render the results compromised.
Binary classification is used in the first problem statement. And in the second problem we have used multi-class classification.
In the results we have used auc/roc metrics with prediction accuracy of our model performances.
Scenario-1: Area under the ROC-curve for Covid-19 Predictions was observed to be 87.58%.
Scenario-2: Area under the ROC-curve for Covid-19 Predictions was observed to be 97.82%.
Prediction accuracy of the model is also an important factor in determining the efficiency of the model.
we are able to predict with 87.0 - 97.4 percent accuracy at a 95 percent confidence level that a patient is suffering from Covid-19
When biomarkers are taken into consideration.
Among those that tested positive, we were able to demonstrate that our model
could predict with 87.0 - 100 percent accuracy at a 95 percent
confidence that whether the patient would be admitted to a
particular ward.
We performed classification using different machine learning algorithms and compared the testing and
training accuracy of the different classifiers used.
The best accuracy was obtained using Random Forest Classifier with a training accuracy of 97.17% and testing
accuracy of 94.80 % .