scholarly journals Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial

2020 ◽  
Vol 9 (12) ◽  
pp. 3834
Author(s):  
Hoyt Burdick ◽  
Carson Lam ◽  
Samson Mataraso ◽  
Anna Siefkas ◽  
Gregory Braden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Learning Algorithm ◽  
Pragmatic Trial ◽  
Therapeutic Agents ◽  
Study Endpoint ◽  
Machine Learning Algorithm ◽  
The Novel ◽  
Hazard Ratios ◽  
Novel Coronavirus

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (p = 0.011) increase in survival (adjusted hazard ratio 0.29, 95% confidence interval (CI) 0.11–0.75). Adjusted survival among the algorithm indicated patients was 82.6% in the treated arm and 51.2% in the arm not treated. No association between treatment and mortality was observed in the general population. A 31% increase in survival at the end of the study was observed in a population of COVID-19 patients that were identified by a machine learning algorithm as having a better outcome with hydroxychloroquine treatment. Precision medicine approaches may be useful in identifying a subpopulation of COVID-19 patients more likely to be proven to benefit from hydroxychloroquine treatment in a clinical trial.

scholarly journals A novel machine learning algorithm has the potential to reduce by 1/3 the quantity of ILR episodes needing review

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
A Rosier ◽  
E Crespin ◽  
A Lazarus ◽  
G Laurent ◽  
A Menet ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Machine Learning Algorithm ◽  
The Novel ◽  
Funding Sources ◽  
High Workload ◽  
Positive Rate ◽  
The Impact

Abstract Background Implantable Loop Recorders (ILRs) are increasingly used and generate a high workload for timely adjudication of ECG recordings. In particular, the excessive false positive rate leads to a significant review burden. Purpose A novel machine learning algorithm was developed to reclassify ILR episodes in order to decrease by 80% the False Positive rate while maintaining 99% sensitivity. This study aims to evaluate the impact of this algorithm to reduce the number of abnormal episodes reported in Medtronic ILRs. Methods Among 20 European centers, all Medtronic ILR patients were enrolled during the 2nd semester of 2020. Using a remote monitoring platform, every ILR transmitted episode was collected and anonymised. For every ILR detected episode with a transmitted ECG, the new algorithm reclassified it applying the same labels as the ILR (asystole, brady, AT/AF, VT, artifact, normal). We measured the number of episodes identified as false positive and reclassified as normal by the algorithm, and their proportion among all episodes. Results In 370 patients, ILRs recorded 3755 episodes including 305 patient-triggered and 629 with no ECG transmitted. 2821 episodes were analyzed by the novel algorithm, which reclassified 1227 episodes as normal rhythm. These reclassified episodes accounted for 43% of analyzed episodes and 32.6% of all episodes recorded. Conclusion A novel machine learning algorithm significantly reduces the quantity of episodes flagged as abnormal and typically reviewed by healthcare professionals. FUNDunding Acknowledgement Type of funding sources: None. Figure 1. ILR episodes analysis

scholarly journals Identification of Fake Accounts for Making Trust on Network using Machine Learning

2019 ◽  
Vol 8 (2S3) ◽  
pp. 796-800
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
The Novel ◽  
Related Information ◽  
Legitimate User ◽  
The People ◽  
Novel Approach ◽  
The Social

Social Networks are gradually influencing the people to communicate with each other and share the personal, public related information. Different social networks have different target people. In particular Facebook used to establish the friendship, LinkedIn to find new Job, these Rabid growth of social networks, people tend to misuse the social networks to spoil others reputation or to steal the others information’s. Fake profiles are dangerous in social networks platform. It is essential to identify the fake users from social networks. This work presents the novel approach to predict and differentiate the fake user and legitimate user from social networks by using Machine Learning algorithm and we achieved significant results.

scholarly journals Statistical analysis of national & municipal corporation level database of COVID-19 cases In India

2020 ◽  
Author(s):  
Naman S. Bajaj ◽  
Sujit S Pardeshi ◽  
Abhishek D Patange ◽  
Disha Kotecha ◽  
Kavidas K Mate
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Learning Algorithm ◽  
Regional Level ◽  
Supervised Machine Learning ◽  
Limited Area ◽  
Machine Learning Algorithm ◽  
Fatality Rate ◽  
Current Contribution ◽  
Novel Coronavirus

Since its origin in December 2019, Novel Coronavirus or COVID-19 has caused massive panic in the word by infecting millions of people with a varying fatality rate. The main objective of Governments worldwide is to control the extent of the outbreak until a vaccine or cure has been devised. Machine learning has been an efficient mechanism to train, map, analyze, and predict datasets. This paper aims to utilize regression, a supervised machine learning algorithm to assess time-series datasets of COVID-19 pandemic by performing comparative analysis on datasets of India and two Municipal Corporations of Maharashtra, namely, Mira-Bhayander and Akola. This study's current contribution is an attempt towards drawing attention to the dynamics of the pandemic in a controlled locality such as Municipal Corporation. The results of the current study depicts that growth of COVID-19 cases is exponential when considered nationally, however, for limited area the nature of curve is observed to be cubic for total cases and multi-peak Gaussian for active cases. In conclusion, Government should empower district/ corporations to adopt their own methodology and decisionmaking policy to contain the pandemic at regional-level like in the case of Dharavi.

scholarly journals Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241543
Author(s):  
Sovesh Mohapatra ◽  
Prathul Nath ◽  
Manisha Chatterjee ◽  
Neeladrisingha Das ◽  
Deepjyoti Kalita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Therapeutic Agents ◽  
The Novel ◽  
Epidemic Disease ◽  
Treatment Regimens ◽  
Antiretroviral Drug ◽  
Rapid Prediction ◽  
Bayes Algorithm ◽  
Novel Coronavirus ◽  
Smart Drug

Background The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2. Methods and findings We perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID–DB00701) would probably be the most effective drug based on the selected criterions. Conclusions Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Sign in / Sign up

Export Citation Format

Share Document