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COVID-19 Analysis and Forecasting Using Deep Learning

#artificialintelligence

As of October 2020, the COVID-19 pandemic has claimed over 1 million lives across the world and over 41 million people have been infected. Understanding the factors and policies that influence the spread of the virus can help governments make informed decisions in order to control infections and deaths until a vaccine becomes widely available. The data used for this project can be divided into four different parts, each represented as separate data frames/tables in the code: policy data, mobility data, demographic data, and COVID-19 time-series statistics. The policy data, extracted from the OxCGRT dataset, contains information about the policies implemented by the government in each country to control the spread of COVID-19. The policy data is available for each day after the start of the pandemic.


When 'code rot' becomes a matter of life or death, especially in the Internet of Things

ZDNet

The possibilities opened up to us by the rise of the Internet of Things (IoT) is a beautiful thing. However, not enough attention is being paid to the software that goes into the things of IoT. This can be a daunting challenge, since, unlike centralized IT infrastructure, there are, by one estimate, at least 30 billion IoT devices now in the world, and every second, 127 new IoT devices are connected to the internet. They are increasingly growing sophisticated and intelligent in their own right, housing significant amounts of local code. The catch is that means a lot of software that needs tending.


Leveraging Digital Health and Machine Learning Toward Reducing Suicide--From Panacea to Practical Tool

#artificialintelligence

Because the rates of suicide attempts and deaths have recently increased to 50-year highs,1 new solutions are needed. The urgency to reverse this trend has brought attention to technology-based tools, such as text messaging, smartphone apps, smartphone sensors, electronic health records, and machine-learning algorithms, that can offer crucial data to improve the prognostication of suicide or immediate support for those at risk. This promise of real-time data from connected devices, large quantities of social-behavioral interactions from social media and internet, and longitudinal clinical trends from electronic health records, when paired with artificial intelligence to automatically identify risk, is often touted as a panacea. Yet, to date, this approach has found less clinical success than expected. The current, limited technological advances in suicide prevention do not reflect a failure of technology or big data but rather a need to realign research aims and clinical use with prevention research that addresses the upstream suicide risk that precedes suicide crisis. In a recent report,2 the National Action Alliance for Suicide Prevention outlined 3 gaps in health care that contribute to suicide death: failing to (1) proactively identify suicide risk, (2) act efficiently for safety, and (3) provide supportive contacts for people at risk of suicide.


Episode #41: Brand Management, How You Should Use AI with the Beard, Curphy Smith by #BIZ with the Beard • A podcast on Anchor

#artificialintelligence

Nov 1st, 2019 will mark the 20th anniversary of the death of the greatest football player of all time, #34, Walter Payton of the Chicago Bears. The #BIZ with Beard & Bald Podcast was blessed to spend time with Walter's son, Jarrett Payton, this week to discuss his dad as a player, father, friend, and the impact he had on the world. Jarrett shares with us the motivational wisdom of his father, living with the Payton name, being there for his dad at the end of his life, and what it meant to induct his dad into the NFL Hall of Fame. Jarret reveals Walter's true feelings about not getting the ball to score in the Super Bowl, his impact on racial boundaries, who is the GOAT, and some touching and private moments that have never been shared before. Jarrett discusses his own success and journey and how his father's encouragement and guidance, even in death, have helped mold him into the successful entrepreneur, father, husband, and man he is today.


New artificial intelligence models show potential for predicting outcomes – IAM Network

#artificialintelligence

New applications of artificial intelligence (AI) in health care settings have shown early success in improving survival and outcomes in traffic accident victims transported by ambulance and in predicting survival after liver transplantation, according to two research studies presented at the virtual American College of Surgeons Clinical Congress 2020. Both studies evaluated how AI can crunch massive amounts of data to support decision-making by surgeons and other care providers at the point of care. In one study, researchers at the University of Minnesota applied a previously published AI approach known as natural language processing (NLP) to categorize treatment needs and medical interventions for 22,529 motor vehicle crash patients that emergency medical service (EMS) personnel transported to ACS-verified Level I trauma centers in Minnesota. According to a 2016 study by the National Academies of Sciences, Engineering, and Medicine, 20 percent of medical injury deaths are potentially preventable representing a quality gap the researchers sought to address. Reviewing the performance of EMS teams to profile potentially preventable deaths can enable quality improvement efforts to reduce these deaths.


The Ethics of AI and Autonomous Vehicles

#artificialintelligence

In the most diverse sectors of our society, artificial intelligence ( AI) is assuming a significant role. We have no return point, and artificial intelligence will be incorporated into our daily life, professionally or socially, into our future. But together with the crescent adoption of the technology, some ethical concerns are posed by the notion of "thinking computers" being able to making decisions like humans. A practical approach to AI adoption must be researched and examined, and this article starts to explore ethical guidelines for the use of intelligent and autonomous systems. Artificial Intelligence ( AI) has been applied widely among us, with potentially great benefits to humanity but at the same time, several concerns regarding AI's unethical use are growing.


Can Police Brutality be Reformed Using Artificial Intelligence?

#artificialintelligence

Data suggests that 94% of the officers are at minimal risk, 4% at advisable risk and 2% are at actionable risk. The brutal custodial death of George Floyd has sparked worldwide protests. Not only it has revealed the bitter reality of police misconduct, but has also shredded light on the skewed judicial system. Though the protests started after George Floyd was killed due to gruesome racial bias, but police brutality has existed in the society for a long time. Moreover, the USA is not the only country where the responsibility of the police is being questioned. In India, the custodial death of Father-Son duo Jayaraj and Phoenix has put police accountability under heavy scrutiny.


New artificial intelligence models show potential for predicting outcomes

#artificialintelligence

New applications of artificial intelligence (AI) in health care settings have shown early success in improving survival and outcomes in traffic accident victims transported by ambulance and in predicting survival after liver transplantation, according to two research studies presented at the virtual American College of Surgeons Clinical Congress 2020. Both studies evaluated how AI can crunch massive amounts of data to support decision-making by surgeons and other care providers at the point of care. In one study, researchers at the University of Minnesota applied a previously published AI approach known as natural language processing (NLP) to categorize treatment needs and medical interventions for 22,529 motor vehicle crash patients that emergency medical service (EMS) personnel transported to ACS-verified Level I trauma centers in Minnesota. According to a 2016 study by the National Academies of Sciences, Engineering, and Medicine, 20 percent of medical injury deaths are potentially preventable representing a quality gap the researchers sought to address. Reviewing the performance of EMS teams to profile potentially preventable deaths can enable quality improvement efforts to reduce these deaths.


New artificial intelligence models show potential for predicting outcomes

#artificialintelligence

CHICAGO: New applications of artificial intelligence (AI) in health care settings have shown early success in improving survival and outcomes in traffic accident victims transported by ambulance and in predicting survival after liver transplantation, according to two research studies presented at the virtual American College of Surgeons Clinical Congress 2020. Both studies evaluated how AI can crunch massive amounts of data to support decision-making by surgeons and other care providers at the point of care. In one study, researchers at the University of Minnesota applied a previously published AI approach known as natural language processing (NLP)1 to categorize treatment needs and medical interventions for 22,529 motor vehicle crash patients that emergency medical service (EMS) personnel transported to ACS-verified Level I trauma centers in Minnesota. According to a 2016 study by the National Academies of Sciences, Engineering, and Medicine, 20 percent of medical injury deaths are potentially preventable2 representing a quality gap the researchers sought to address. Reviewing the performance of EMS teams to profile potentially preventable deaths can enable quality improvement efforts to reduce these deaths.


Coronavirus testing: What is a false positive?

BBC News

There has been a lot of talk on social media about "false positive" test results after several commentators suggested they might be seriously skewing the coronavirus figures - but that is based on a misunderstanding of the impact of false positives. Talk Radio host Julia Hartley-Brewer has claimed that "nine out of 10 of the positive cases of Covid we are finding in the community when we do random testing, when anyone just puts themselves forward, will be wrong. They will not be people who have got coronavirus." Could it be true that 90% of positive results from tests in the community - that means tests not carried out in hospitals - are false? The answer is "no" - there is no way that so-called false positives have had such an impact on the figures.