To reduce and track the spread of COVID-19, researchers and provider organizations have increasingly turned to artificial intelligence and machine learning tools to improve their surveillance efforts.

For more coronavirus updates, visit our resource page, updated twice daily by Xtelligent Healthcare Media.

From predicting patient outcomes to anticipating future hotspots across the country, big data analytics systems have helped health leaders stay ahead of the pandemic, resulting in more efficient care delivery.

However, healthcare organizations’ level of pandemic preparation is only as good as the data available to them. While the industry is no stranger to data issues, the COVID-19 pandemic has brought a host of unique challenges to the forefront of care delivery.

The novel, global nature of the virus has led to significant gaps in COVID-19 data, with inconsistencies in information leaving officials unsure of the effectiveness of public health interventions.

“It’s now well-known that asymptomatic infections are a common phenomenon in the spread of coronavirus. And it’s very important to understand that phenomenon because, depending on how many asymptomatic infections there are, public health interventions might be different,” Lucy Li, PhD, data scientist at the Chan Zuckerberg Biohub, told HealthITAnalytics.

Researchers at the Chan Zuckerberg Biohub are working to overcome this challenge. Using machine learning and cloud computing technology, Li estimated the number of undetected infections at 12 locations in Asia, Europe, and the US over the course of the pandemic.

The results showed that a wide range of infections were undetected in these locations, with the rate of undetected infections as high as over 90 percent in Shanghai.

Additionally, when the virus was first transmitted to these 12 locations, over 98 percent of infections were undetected during the first few weeks of the outbreak. This suggests that the pandemic was already well underway by the time intense testing began to occur.

These findings have important implications for public health policy and provider organizations, Li noted.

“For disease outbreaks where you can detect every single infection, rapid testing and just a small amount of contact tracing is enough to get the epidemic under control. But for coronavirus, because there are so many asymptomatic infections out there, testing alone won’t help control the pandemic,” she said.

“Because usually when you do testing, you’re testing symptomatic patients. But that’s only a subset of the total number of infections out there. You’re really missing a lot of people who are able to spread the infection, but are not quarantining. Being able to get a sense of what that number might be is helpful for allocating resources.”

Li’s research was supported by the AWS Diagnostic Development Initiative, a global effort to accelerate diagnostic research and innovation during the COVID-19 pandemic and to help mitigate future disease outbreaks.

The initiative allows individuals to take advantage of the cloud and other innovative tools, something that Li said was essential for her research.

“The data I’m using are the viral genomes – the viral DNA. As the viral genomes spread through the population, they accumulate mutations. Generally, these mutations are not good or bad, they’re just changes in the genome. Every time the virus is spread to a new person, it could accumulate new mutations. So, if we know how quickly the virus mutates, we can infer how many missing transmission links there were in between the observed genomes,” she said.

“That’s the data I’m fitting the models to. And because there are many different scenarios that could explain what we see in the viral genomes, I have to leverage machine learning and cloud computing to test all of those hypotheses and to see which one can explain the observed changes in the viral genomes.”

These data analytics tools are well-suited to meeting the challenges brought on by COVID-19, Li pointed out.

“In order to try to quantify the unreported infections, we formulate models of how disease spreads in the population. And then we generate many simulations from these models, and we find out which of those simulations fits the data that we see,” she said.

“That allows us to test different levels of under-reporting and understand which of those can best explain the data that we see. That’s not really possible without a lot of computational resources, and it’s a very time-intensive process. The machine learning tool allows us to explore different explanations of the data that we’re seeing, and we can test many hypotheses. It’s a crucial tool for this type of analysis.”

With machine learning and cloud computing technologies, Li was able to streamline a previously time-consuming task.

“Before cloud computing became more common and these big computational resources became available, some of these analyses could take months to run. I’ve seen papers that were based on months of running a very complex model,” Li said.

“But by having access to more computational resources in the cloud, we can shorten that time from months to days, because we’re able to leverage much more memory and better parallelize our analysis.”

The research could help public health officials monitor the rate of under-reporting in real-time, which could indicate how well current surveillance systems are operating.

“The better the current public health surveillance system is at detecting infections, the fewer underreported cases we would have. But if we see the underreported cases increasing, that would suggest that there needs to be more testing in the population. The results of this research can help the public health department determine how much more testing they would need,” Li said.

“This type of research can also help indicate how close we are to the end of the pandemic. By tracking how many people in the population have been infected by the virus or the number of undetected cases, we could get a sense of how far are we from eliminating this disease.”

With the amount of information generated by the COVID-19 pandemic, analytics tools are critical for uncovering new insights and potential solutions.

“Since the start of the pandemic, we’ve racked our brains to figure out what we can do to help the public health departments in reducing the spread of COVID-19. The number one request that we get from public health departments is information. And sometimes, just presenting the raw data to these departments is sufficient by itself,” Li concluded.

“But quite often, we need to use machine learning and mathematical models to infer these parameters or numbers that we can’t directly see in the data. There has been so much effort from different research groups around the world in developing new models to help us tease out the underlying information that’s not obvious from the data alone.”

The post Using Machine Learning to Calculate Unreported COVID-19 Cases appeared first on Artificial Intelligence.

Patient hesitancy to seek care during the pandemic created the perfect storm for delayed care. But big data and analytics are driving population health at Southwestern Health Resources to close care gaps.

“There has been a dramatic decrease in willingness of people to seek healthcare, whether it’s for urgent, critical medical needs or routine screening,” Andrew Ziskind, MD, senior executive officer of Southwestern Health Resources told HealthITAnalytics. “Right now, there’s a public health crisis in the short term, but in the long term, there will be tens of thousands of new cancer cases because of a lack of screening.”

Four in ten adults reported avoiding care because of COVID-19, according to recent data from the Centers for Disease Control and Prevention. So closing care gaps required innovative thinking to manage populations. At the center of this strategy is actionable data.

“The first thing we can do for our existing members is to identify who has gaps,” Ziskind explained. “Our data is robust enough that we can see where the targets are geographically, age-wise, and so forth.”

As a clinically integrated network, Southwestern Health Resources has access to claims data and clinical data to inform these decisions.

“Claims data is a lagging indicator,” Ziskind argued. “But clinical data is probably the most important advantage of being a provider-based clinically integrated network. All of our primary care physicians are connected to us through a common electronic medical record.”

This connectedness allows for easy data sharing.

At traditional health systems, patients with diabetes who might have seen an ophthalmologist and closed a care gap during that visit may have forgotten to inform their primary care provider. While the gap in care is technically closed, the provider is unaware.

But an integrated data network eliminates this problem as the primary care providers can have access to all of the patient’s records.

“Documentation around gap closure is often very challenging. The more we can mine the data and identify alternatives, the better,” continued Ziskind. “We’re using the breadth of data that we have access to for identifying where the gaps are. Once we know what they are, we can then use a gap-targeted approach for each specific one.”

Southwestern Health Resources took a multi-pronged approach to targeting patients and closing their gaps in care. The network began by calling each patient with unfilled gaps, but the call center team members saw very low response rates.

“Patients are suspicious about phone calls. They sometimes are confused as to if the call is from the hospital or health system or insurance company,” highlighted Ziskind. “We found that the highest success rate is if the patient is contacted on behalf of their physician.”

Patients then had the option to seek care in person or have a provider come to their home. Patients who opted for in-office visits were given instructions on how to make an appointment and Southwestern Health facilitated services at home if the patient preferred.

“You have to customize at the level of the individual patient,” Ziskind emphasized. “We tried to get rid of any patient burden.”

Ensuring the information was culturally component was also critical. Not only does this include translating information into multiple languages, but it also means delivering messaging in a way that best suits patient need.

“There’s a component through local churches and community access. There’s traditional mail. There’s email. There’s social media,” Ziskind highlighted. “We’re really trying to take a multi-prong approach to enhancing awareness.”

These efforts began with data that allowed Southwestern Health Resources to identify gaps in care and thrived when data on individual patient preference was actionable. Customizing outreach improved gap closure and gave providers actionable information.

As gap closure efforts continue, Ziskind and team plan to focus on clinically relevant patient outcomes, including promoting preventive health screenings.

“More and more we’re trying to move upstream in the disease process. We’re focusing on risking risk as opposed to just the management of patients who have advanced, complex disease,” he concluded. “The earlier we can detect disease, the better the long-term outcome will be for the patient.”

The post How Big Data, Analytics Drives Population Health, Closes Care Gaps appeared first on Artificial Intelligence.

Phil Duffy, Vice President, Product, Program and UX Design for Brain Corp, joined host Daniel Litwin to tackle a broad, yet critical topic in today’s automated world – how robotics are building new bridges between employees and technology.

Brain Corp builds the software for autonomous mobile robots, or AMRS, used by retailers and grocery stores across the world and counts several Fortune 500 customers among its client base, including Walmart, Kroger, Giant Eagle, Schnucks and Simon Property Group.

Duffy provided some key insights from the front lines regarding how those customers and others are leveraging AMRs during the pandemic, how the rapidly growing of adoption of robotic solutions could continue into the new normal and beyond, and the impact of robotics on day-to-day operations during COVID and into global reopening.

“We’ve known about robots in warehouses and industrial settings for 20-odd years, but the robots that scale in open-to-public spaces … are a fairly new thing,” Duffy said. “Up until recently, customers have been nervous about the prospect of robots in open spaces. What’s really happened during COVID is that a lot of the customers we deal with in the robotics industry have recognized that there’s an opportunity here to gain value.”

Essentially, that translates to robots taking over dull and monotonous jobs, allowing human employees to take on cleaning and other critical tasks during this uncertain period.

The post How Robotics Are Creating Synergy Between Employees and Technology appeared first on Artificial Intelligence.

September 15, 2020 – While the rapid spread of COVID-19 has exposed many unflattering healthcare truths, the glaring health disparities highlighted by the pandemic are perhaps the most detrimental to patient health.

The virus has had a disproportionate impact on minority and underserved communities, shining a spotlight on existing clinical and non-clinical inequities.

“Minorities are more likely to suffer from chronic conditions like high blood pressure, diabetes, obesity, and heart disease. Additionally, these patient populations typically lack access to adequate healthcare, or have a limited understanding of the healthcare system,” said Sampson Davis, MD, an emergency medicine physician.

“These individuals also tend to work in the service industry – transportation, the food industry, or airports. In these jobs, there’s no work-from-home possibilities that can allow people to distance themselves socially. In that sense, there’s a heighted risk of exposure to COVID-19.”

In order to target and reduce the impact of the virus on minority populations, organizations have increasingly turned to data analytics techniques to better track COVID-19 spread.

“As healthcare experts, collecting data is invaluable in what we do. It allows us to track what is working and what is not. Without data, we will pretty much continue to do the same thing over and over again expecting a different outcome,” said Davis.

“In order to decrease the impact of this virus, we need to track which solutions work and which don’t work, as well as where the virus is currently having the biggest impact. The data also breaks down the tedious minutia needed for our pandemic response, because what works for one community won’t necessarily work for the next community.”

With data analytics tools, providers have been able to deliver the right care to the right patients at the right time. These technologies helped clinicians navigate the early months of the pandemic, allowing them to uncover essential information to appropriately treat their patients.

“We started off thinking medications like hydroxychloroquine would have an impact, which it hasn’t. What we have seen with the data collected is that a medication called dexamethasone has helped us to decrease the impact of the virus. We have also discovered that turning patients from a supine position on their back to lying on their chest or abdomen allows them to actively participate in their breathing, which improves their chances of recovery,” said Davis.

“The data has also enabled us to know where the hotspots were – that at one point in time the virus was surging in New York and New Jersey, and then it traveled south to Georgia and Florida, and then over to Texas and Colorado. When we know where these hotspots are, we can try to understand why these areas are impacted the most, and we can target interventions to mitigate the impact of the virus.”

In the coming months, as the country works to reduce the effect of COVID-19, Davis noted that the healthcare industry will need to use data to get ahead of the virus.

“We have experienced similar scenarios throughout history. We’ve seen polio, we’ve seen the flu, we’ve seen mumps and measles. These are all diseases that we now have vaccines for, and that is the result lot of discovering the technology to defeat the virus or the bacteria, but also collecting the data to know what works and what doesn’t,” said Davis.

“So now, we have to think about how we gather and share data across the country to pinpoint where the need is the greatest. And by knowing where the need is the greatest, we can then say that if we have a vaccine or treatment, we can start there to see what type of impact this treatment may have.”

Data sharing will also play a key role in mitigating the impact of coronavirus, Davis stated.

“The virus is ahead of us and we’ve had to do catch-up for the last six months. And now I feel like we’re starting to get to a place where things are balancing. Unfortunately, we have lost hundreds of thousands of lives. But at the same time, sharing the information liberally and equally across the table allows us to see where the greatest need is and how to make the greatest impact with the treatment of patients,” he said.

“When I started in medicine, we used paper charting, and now we have everything in a digital format with the latest information readily accessible. It goes without saying that data analytics are instrumental when it comes to healthcare delivery day in and day out, and it’s going to serve the same role in defeating this pandemic.”

The pandemic has highlighted the substantial care disparities that exist in healthcare, and many expect that the strategies developed during this period will endure even after the situation has waned.

“We will get past this moment with coronavirus. But it has shown us is that in order to achieve health equity, we have to defeat healthcare disparities. That means reducing the disproportionate numbers of minorities that suffer from high blood pressure, diabetes, heart disease, obesity, and other conditions,” Davis concluded.

“Coronavirus did not target one community or race or gender, but you can see those who are at greatest risk suffer the most. The beauty of collecting the data is not just that we have this information, but also that we can use this information to move forward. My hope is that we all step up and continuously push ourselves to be on the forefront of making a difference in our country.”

The post How Big Data Analytics Can Mitigate COVID-19 Health Disparities appeared first on Artificial Intelligence.

Global Marketers presents an updated and Latest Study on AI and Big Data Analytics in Telecoms Market 2020-2026. This report comprises a detailed study of the market covering its future predictions by the past year as a reference for the period between 2020 and 2026 as the forecast period. The report breakdowns major segments and highlights wider level geographies. The report bridges a perfect balance of both qualitative and quantitative information of the AI and Big Data Analytics in Telecoms Market. This report also offers an all-inclusive study of the future trends and developments of the market.

Companies Profiled in this report includes:

Microsoft
Alibaba
Amazon
Amdocs
Apple
AT&T
Baidu
Dell
Ericsson
Facebook
Fico
Google
Huawei
Iberia
IBM
Iflytek
Infosys
IntelCisco

AI and Big Data Analytics in Telecoms Market forecast and review in five major regions: North America, Europe, Asia-Pacific (APAC), Middle East, and Africa (MEA), and South & Central America.

The key insights and evaluations presented in this AI and Big Data Analytics in Telecoms report are worth knowing for any market participant, helping them in ascertaining the superior dynamics and the future trajectories of the global AI and Big Data Analytics in Telecoms Market. The report explains the locale, economic situations with the item value, benefit, demand & supply with market development rate and figure.

By Product Type, AI and Big Data Analytics in Telecoms Market has been segmented into:

cloud
on premise

By Application, AI and Big Data Analytics in Telecoms Market has been segmented into:

Customer Analytics
Network Security
Network Optimization
Self-Diagnostics
Virtual Assistance
Others

We, at Global Marketers, understand the economic impact on various sectors and markets. Using our holistic market research methodology, we are focused on aiding your business sustain and grow during COVID-19 pandemics. With deep expertise across various industries-no matter how large or small and with a team of highly experienced and dedicated analysts, we offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

Key Questions Answered In The Report:

What will be the AI and Big Data Analytics in Telecoms Market size in terms of value and volume in the next five years?
Which segment is currently leading the global AI and Big Data Analytics in Telecoms Market?
In which region will the AI and Big Data Analytics in Telecoms Market be growing rapidly?
Which players will take the lead in the AI and Big Data Analytics in Telecoms Market?

Major factors covered in the report:

Global AI and Big Data Analytics in Telecoms Market summary
Economic Impact on the AI and Big Data Analytics in Telecoms Industry
AI and Big Data Analytics in Telecoms Market Competition in terms of Manufacturers
Production, Revenue (Value) by geographical segmentation
AI and Big Data Analytics in Telecoms Market Analysis by Application
Cost Investigation
Industrial Chain, Raw material sourcing strategy and Downstream Buyers
Marketing Strategy comprehension, Distributors and Traders
Study on AI and Big Data Analytics in Telecoms Market Research Factors
Global AI and Big Data Analytics in Telecoms Market Forecast

Fundamentals of Table of Content:

Chapter 1 about the AI and Big Data Analytics in Telecoms Industry
Chapter 2 Global AI and Big Data Analytics in Telecoms Market Competition Landscape
Chapter 3 Global AI and Big Data Analytics in Telecoms Market share
Chapter 4 Supply Chain Analysis
Chapter 5 Company Profiles
Chapter 6 AI and Big Data Analytics in Telecoms Market Globalisation & Trade
Chapter 7 Distributors and Customers
Chapter 8 Import, Export, Consumption and Consumption Value by Major Countries
Chapter 9 Global AI and Big Data Analytics in Telecoms Market Forecast through 2026
Chapter 9 Global AI and Big Data Analytics in Telecoms Market Forecast through 2026
Chapter 10 Key success factors and Market Overview

The post AI and Big Data Analytics in Telecoms Market Focuses on SWOT analysis, Industry Synopsis, Development Plans 2020 to 2026 appeared first on Artificial Intelligence.

Ample Market Research(AMR) has published a new market study, titled, Artificial Intelligence (AI) Market. The market study not only presents a comprehensive analysis of market overview and dynamics for the historical period, 2014-2019, but also contributes global and regional predictions on the market value, volume production, and consumption throughout the future period, 2019-2026.

There are a number of insights are included or analyzed in this market study which is helpful in devising strategies for the future and take necessary steps. New project investment feasibility analysis and SWOT analysis are offered along with insights on industry barriers.

The market study also explains the key market players, especially the wholesalers, distributors, businesspersons along with the industrial chain structure. The development of market trends is considered along with the competitive landscape in various regions, countries, provinces which would boost top and arising market players to discover the lucrative investment pockets.

The Coronavirus Pandemic (COVID-19) has affected every aspect of life worldwide. This has led to several changes in market conditions. The report covers the rapidly changing market scenario and the initial and future impact assessments.

The market study starts with a brief introduction and market overview, in which the Artificial Intelligence (AI) industry is first defined before estimating its market scope and size. Next, the market study elaborates on the status of the market scope and market size estimation.

This is followed by an overview of the market segmentation such as type, application, and region. The drivers, limitations, and opportunities are listed for the Artificial Intelligence (AI) industry, followed by industry news and policies.

The market study presents an industry chain examination, concentrating on upstream raw material suppliers and major or principal downstream buyers. The information is presented by tables and figures, which also cover production cost structure and market channel analysis.

Major companies or players involved in the Artificial Intelligence (AI) industry are also outlined, along with their market share and product types.

With the help of tables and figures, valuable insights on production, value, price, and gross margin of each player are offered.

The major market players operating in the industry are Alphabet, Hanson Robotics, IBM, Amazon, Xilinx, Blue Frog Robotics, Promobot, Intel, Kuka, Fanuc, Softbank, ABB, Microsoft, Harman International Industries, Nvidia

Market share based on the region for each player is outlined for 2019. Insights on future growth for each player would help in understanding the evolution of the competitive scenario and assist emerging players to gain a competitive edge.

The market study segments the global Artificial Intelligence (AI) market based on factors such as type, application, and region. For the historic period, extensive insights on value, market share, production, growth rate, and price analysis for each sub-segment is offered by the report.

For the future period, sound forecasts on market value and volume are offered for each type as Hardware, Software, Services and application such as Healthcare, BFSI, Law, Retail, Advertising & Media, Automotive & Transportation, Agriculture, Manufacturing, Others.

In the same period, the report also provides a detailed analysis of market value and consumption for each region.

Additionally, the report also examines regional production, consumption, export, and import for the historic period. The regions analyzed in the research include North America (Covered in Chapter 7 and 14), United States, Canada, Mexico, Europe (Covered in Chapter 8 and 14), Germany, UK, France, Italy, Spain, Russia.

Finally, the current market status and SWOT analysis for each region are elaborated, which would help market players to achieve a competitive edge by determining the predominant segments.

Market Research findings and conclusions and more are provided at the end of the market study of the Artificial Intelligence (AI).

With the presented market data, AMR offers customizations according to particular needs on Local, Regional and Global Markets.

About Ample Market Research

Ample Market Research provides comprehensive market research services and solutions across various industry verticals and helps businesses perform exceptionally well. Attention to detail, consistency, and quality are elements we focus on. However, our mainstay remains to be knowledge, expertise, and resources to make us industry players.

Our end goal is to provide quality market research and consulting services to customers and add maximum value to businesses worldwide. We desire to delivery reports that have the perfect concoction of useful data.

Our mission is to capture every aspect of the market and offer businesses a document that makes solid grounds for crucial decision making.

The post Artificial Intelligence Market To Witness Huge Growth By 2025 | Alphabet, Hanson Robotics, IBM, Amazon, Xilinx, Blue Frog Robotics appeared first on Artificial Intelligence.

IndustryGrowthInsights, 23-08-2020: The research report on the Python Package Software Market is a deep analysis of the market. This is a latest report, covering the current COVID-19 impact on the market. The pandemic of Coronavirus (COVID-19) has affected every aspect of life globally. This has brought along several changes in market conditions. The rapidly changing market scenario and initial and future assessment of the impact is covered in the report. Experts have studied the historical data and compared it with the changing market situations. The report covers all the necessary information required by new entrants as well as the existing players to gain deeper insight.

Furthermore, the statistical survey in the report focuses on product specifications, costs, production capacities, marketing channels, and market players. Upstream raw materials, downstream demand analysis, and a list of end-user industries have been studied systematically, along with the suppliers in this market. The product flow and distribution channel have also been presented in this research report.

The Major Manufacturers Covered in this Report:
Spyder
MySQL
Celery
Editra
py2exe
PyAudio
WunderWeather
Red Bot
cx_Freeze
Gunicorn

The Research Study Focuses on:

Market Position of Vendors
Vendor Landscape
Competitive scenario
Manufacturing Cost Structure Analysis
Recent Development and Expansion Plans
Industry Chain Structure
By Types:
Cloud Based
Web Based

By Applications:
Large Enterprises
SMEs

By Regions:

North America (The US, Canada, and Mexico)
Europe (the UK, Germany, France, and Rest of Europe)
Asia Pacific (China, India, and Rest of Asia Pacific)
Latin America (Brazil and Rest of Latin America)
Middle East & Africa (Saudi Arabia, the UAE, South Africa, and Rest of Middle East & Africa)
The Python Package Software Market Report Consists of the Following Points:

The report consists of an overall prospect of the market that helps gain significant insights about the global market.
The market has been categorized based on types, applications, and regions. For an in-depth analysis and better understanding of the market, the key segments have been further categorized into sub-segments.
The factors responsible for the growth of the market have been mentioned. This data has been gathered from primary and secondary sources by industry professionals. This provides an in-depth understanding of key segments and their future prospects.
The report analyses the latest developments and the profiles of the leading competitors in the market.
The Python Package Software Market research report offers an eight-year forecast.

In conclusion, the Python Package Software Market report is a reliable source for accessing the research data that is projected to exponentially accelerate your business. The report provides information such as economic scenarios, benefits, limits, trends, market growth rate, and figures. SWOT analysis is also incorporated in the report along with speculation attainability investigation and venture return investigation.

About IndustryGrowthInsights:
IndustryGrowthInsights has set its benchmark in the market research industry by providing syndicated and customized research report to the clients. The database of the company is updated on a daily basis to prompt the clients with the latest trends and in-depth analysis of the industry. Our pool of database contains various industry verticals that include: IT & Telecom, Food Beverage, Automotive, Healthcare, Chemicals and Energy, Consumer foods, Food and beverages, and many more. Each and every report goes through the proper research methodology, validated from the professionals and analysts to ensure the eminent quality reports.

The post Python Package Software Market: Global Analysis Of Key Manufacturers, Dynamics & Forecast 2020-2026 appeared first on Artificial Intelligence.

The Center for Noise and Vibration Control at KAIST announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis.

Professor Yong-Hwa Park from the Department of Mechanical Engineering developed a deep learning-based cough recognition model to classify a coughing sound in real time. The coughing event classification model is combined with a sound camera that visualizes their locations in public places. The research team said they achieved a best test accuracy of 87.4 %.

Professor Park said that it will be useful medical equipment during epidemics in public places such as schools, offices, and restaurants, and to constantly monitor patients’ conditions in a hospital room.

Fever and coughing are the most relevant respiratory disease symptoms, among which fever can be recognized remotely using thermal cameras. This new technology is expected to be very helpful for detecting epidemic transmissions in a non-contact way. The cough event classification model is combined with a sound camera that visualizes the cough event and indicates the location in the video image.

To develop a cough recognition model, a supervised learning was conducted with a convolutional neural network (CNN). The model performs binary classification with an input of a one-second sound profile feature, generating output to be either a cough event or something else.

In the training and evaluation, various datasets were collected from Audioset, DEMAND, ETSI, and TIMIT. Coughing and others sounds were extracted from Audioset, and the rest of the datasets were used as background noises for data augmentation so that this model could be generalized for various background noises in public places.

The dataset was augmented by mixing coughing sounds and other sounds from Audioset and background noises with the ratio of 0.15 to 0.75, then the overall volume was adjusted to 0.25 to 1.0 times to generalize the model for various distances.

The training and evaluation datasets were constructed by dividing the augmented dataset by 9:1, and the test dataset was recorded separately in a real office environment.

In the optimization procedure of the network model, training was conducted with various combinations of five acoustic features including spectrogram, Mel-scaled spectrogram and Mel-frequency cepstrum coefficients with seven optimizers. The performance of each combination was compared with the test dataset. The best test accuracy of 87.4% was achieved with Mel-scaled Spectrogram as the acoustic feature and ASGD as the optimizer.

The trained cough recognition model was combined with a sound camera. The sound camera is composed of a microphone array and a camera module. A beamforming process is applied to a collected set of acoustic data to find out the direction of incoming sound source. The integrated cough recognition model determines whether the sound is cough or not. If it is, the location of cough is visualized as a contour image with a ‘cough’ label at the location of the coughing sound source in a video image.

A pilot test of the cough recognition camera in an office environment shows that it successfully distinguishes cough events and other events even in a noisy environment. In addition, it can track the location of the person who coughed and count the number of coughs in real time. The performance will be improved further with additional training data obtained from other real environments such as hospitals and classrooms.

Professor Park said, “In a pandemic situation like we are experiencing with COVID-19, a cough detection camera can contribute to the prevention and early detection of epidemics in public places. Especially when applied to a hospital room, the patient’s condition can be tracked 24 hours a day and support more accurate diagnoses while reducing the effort of the medical staff.”

This study was conducted in collaboration with SM Instruments Inc.

/Public Release. The material in this public release comes from the originating organization and may be of a point-in-time nature, edited for clarity, style and length. View in full here.

The post Deep Learning-Based Cough Recognition Model Helps Detect Location of Coughing Sounds in Real Time appeared first on Artificial Intelligence.

Patient safety, data privacy, and health equity are key considerations for the use of chatbots powered by artificial intelligence in healthcare, according to a viewpoint piece published in JAMA.

With the emergence of COVID-19 and social distancing guidelines, more healthcare systems are exploring and deploying automated chatbots, the authors noted. However, there are several key considerations organizations should keep in mind before implementing these tools.

“We need to recognize that this is relatively new technology and even for the older systems that were in place, the data are limited,” said the viewpoint’s lead author, John D. McGreevey III, MD, an associate professor of Medicine in the Perelman School of Medicine at the University of Pennsylvania.

“Any efforts also need to realize that much of the data we have comes from research, not widespread clinical implementation. Knowing that, evaluation of these systems must be robust when they enter the clinical space, and those operating them should be nimble enough to adapt quickly to feedback.”

The authors outlined 12 different focus areas that leaders should consider when planning to implement a chatbot or conversational agent (CA) in clinical care. For chatbots that use natural language processing, the messages these agents send to patients are extremely significant, as are patient’s reactions to them.

“It is important to recognize the potential, as noted in the NAM report, that CAs will raise questions of trust and may change patient-clinician relationships. A most basic question is to what extent CAs should extend the capabilities of clinicians (augmented intelligence) or replace them (artificial intelligence),” the authors said.

“Likewise, determining the scope of the authority of CAs requires examination of appropriate clinical scenarios and the latitude for patient engagement.”

The authors considered the example of someone telling a chatbot something as serious as “I want to hurt myself.” In this case, the patient safety element is brought to the forefront, as someone would need to be monitoring the chatbot often.

This hypothetical situation also raises the question of whether patients would take a response from a chatbot seriously, as well as who is responsible if the chatbot fails in its task.

“Even though technologies to determine mood, tone, and intent are becoming more sophisticated, they are not yet universally deployed in CAs nor validated for most populations,” the authors said.

“Moreover, there is no mention of CAs in the US Food and Drug Administration’s (FDA) proposed regulatory framework for AI or machine learning for software as a medical device nor is there a user’s guide for deploying these platforms in clinical settings.”

The authors also noted that regulatory organizations like the FDA should develop frameworks for appropriate classification and oversight of CAs in healthcare. For example, policymakers could classify CAs as low risk versus higher risk.

“Low-risk CAs might be less automated, structured for a specialized task, and have relatively minor consequences if they fail. A CA that guides patients to appointments might be one such example,” the authors wrote.

“In contrast, higher-risk CAs would involve more automation (natural language processing, machine learning), unstructured, open-ended dialogue with patients, and have potentially serious patient consequences in the event of system failure. Examples of higher-risk CAs might be those that advise patients after hospital discharge or offer recommendations to patients about titrating medications.”

Additionally, the authors noted that in partnerships between vendors and healthcare organizations to use CAs, all should be mindful of converging incentives and work to balance these goals with attention to each of the domains.

“Given the potential of CAs to benefit patients and clinicians, continued innovation should be supported. However, hacking of CA systems (as with other medical systems) represents a cybersecurity threat, perhaps allowing individuals with malicious intent to manipulate patient-CA interactions and even offer harmful recommendations, such as quadrupling an anticoagulant dose,” the authors stated.

The authors stated that ultimately, the successful and effective deployment of chatbots in healthcare will depend on the industry’s ability to assess these tools.

“Conversational agents are just beginning in clinical practice settings, with COVID-19 spurring greater interest in this field. The use of CAs may improve health outcomes and lower costs. Researchers and developers, in partnership with patients and clinicians, should rigorously evaluate these programs,” the authors concluded.

“Further consideration and investigation involving CAs and related technologies will be necessary, not only to determine their potential benefits but also to establish transparency, appropriate oversight, and safety.”

Healthcare leaders will need to ensure they continually evaluate the capacity of these tools to improve care delivery.

“It’s our belief that the work is not done when the conversational agent is deployed,” McGreevey said. “These are going to be increasingly impactful technologies that deserve to be monitored not just before they are launched, but continuously throughout the life cycle of their work with patients.”

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Its creators are confident it is the world’s first laboratory studying assisted living that researchers can use remotely.

We all need help now and then, but people with physical and mental challenges need it much more, especially if they want to live independently.

Real life human helpers have been the norm until now but the coronavirus pandemic has changed things rapidly and radically.

That is why the new multi-disciplinary laboratory is aiming to create affordable technology that can support the care of vulnerable people cut off from human contact.

It’s not just the care that will be delivered at a distance – the lab itself will be open and remotely accessible.

The Laboratory for Ambient Assisted Living (OpenAAL) will be available to researchers all over the UK. In the longer term it is hoped it will be open worldwide.

It is being led by Dr Mauro Dragone, an assistant professor at Heriot-Watt University.

He says researchers will be able to use Augmented Reality to “teleport themselves into the laboratory” and hopes it will “help people co-design new technologies”.

It is part of the National Robotarium, a UK centre of excellence which is a partnership between Heriot-Watt and Edinburgh universities.

Clever, caring robots

The applications could include supporting the more than 100,000 people in the UK Dr Dragone says are living alone with dementia, or any of the people whose care has been disrupted by the social isolation forced on the pandemic: people with multi-morbidity conditions, disabilities, or in the acute stages of mental ill health.

And it won’t be a top-down process. The “co-design” idea means involving the people on the receiving end of the care process.

That’s why the lab wants more collaborators from the care sector to join in.

The Coalition of Care and Support Providers in Scotland is to play a key role in connecting the project to its more than 80 members in Scotland’s third sector.

The approach will go a lot further than creating clever, caring robots.

It will involve experts in microsystems, wireless sensing, antennas, microwave and embedded systems, signal processing, data science, machine learning, the Internet of Things, artificial intelligence, and human-computer interaction.

It will mean being able to unobtrusively monitor a vulnerable person’s vital signs, detect patterns and trends in their behaviour and health, identify problems, help communication and social connections, and provide social and physical assistance.

Dr Dragone says this is likely to include helping people receive virtual visits from friends and family.

Robotic pets may be another way to help overcome isolation.

The project is being funded by the UK’s Engineering and Physical Sciences Research Council, under its Impact Acceleration Accounts scheme.

It has already gained support from NHS Lothian, The Digital Health and Care Institute, Blackwood Home and Care Group, Consequential Robotics, Alcuris Ltd, Cyberselves and The Data Lab.

Heriot-Watt are hoping more organisations will want to join in, virtually.

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