“Canada eh, they started a country and nobody showed up”, Rodney Dangerfield once quipped. He was probably referring to the fact that Canada has the second largest land mass of any other country in the world, but only 36 million people living there (about the same number of people living in California.) The difference of course between Californians and Canadians is that Canadians have a sense of humour, and they know how to spell it.

In charge of all that land is a man famous for his boyish good looks and firm handshake, Justin Trudeau, who is leading the 18th largest country in the world by GDP and winning lots of friends by letting everyone there smoke weed. In 2017, his government pledged $125 million for a national artificial intelligence (AI) strategy which aims to increase the number of skilled graduates and researchers in the field of AI, and establish cities like Edmonton, Montreal, and Toronto as research hubs for artificial intelligence. While U.S. companies may be trying to steal Canada’s top AI talent, it hasn’t stopped Canada from birthing a large number of AI startups, some of which we looked at in our article on “9 Canadian AI Startups Making Canada Great Again”. In this article, we’ll look at the ten most funded AI startups in Canada.


Click to go to company websiteWe’ve first looked at this “AI-as-a-service” offering about a year ago, and since then lots has happened. Founded in 2016, Montreal startup Element AI has received a whopping $102 million from the likes of Microsoft Ventures, Intel Capital and Nvidia to create a platform that marries academic AI research with real-world business implementations.

Rather than offering out of the box solutions, the company engages with clients by developing a sector and client-specific roadmap to implement AI algorithms into each business, then executes these projects on a case-by-case basis aiming to maximize return on investment (ROI). The team has been busy since their huge investment arrived last summer, opening new offices in London and Toronto, and going on a hiring spree. It’s now the largest privately-owned artificial intelligence R&D lab in Canada.

Click for company websiteFounded in 2013, Toronto startup Rubikloud has raised $45.5 million to develop a suite of software-as-a-service products for the retail industry, with Intel Capital as a lead investor. The solutions include a promotion manager and a customer lifecycle manager, a big data platform that hosts machine learning applications, and a machine learning library. These tools promise to double sell-through rates and provide accurate forecasts for campaigns, decrease marketing overhead by 50%, and improve sales by 10%.

Rubikloud's AI platform

Obligatory graphic from marketing department showing AI doing something cool

Retail enterprises can take all the data they already have, plug it into the big data framework, and the AI algorithms figure out the best course of action. The A.S Watson Group, a Hong Kong-based retailer of health and beauty, operates 13,000 stores in 25 markets. After a 10-month pilot, Watson Group reported an 8% increase in campaign sell-through rates and has since expanded Rubikloud’s machine learning suite across their entire company.


Click for company websiteFounded in 2011, Toronto startup Maropost has raised $37 million in funding to develop their own take on AI-based marketing and sales platforms. Taking on CRM software giants like Oracle or Salesforce, Maropost’s applications make giant promises, like a 63X return on investment for your marketing spend.

Maropost's platform

The team has a solid client base including some high-profile names like Rolling Stone magazine, the New York Post and Mercedes Benz Canada. The latter reported 3x the click rates and 4x the open rates of their targeted marketing collateral compared to the industrial average thanks to the new solution. Maropost grew in the early stages by word of mouth but is now scaling rapidly, having doubled its workforce to 150 employees in 2017 and having been named Canada’s 3rd fastest growing company by Deloitte.


Click for company websiteFounded in 2012, Toronto startup Analytics 4 Life has raised $29 million in funding to develop a new medical imaging technology for cardiac diagnostics using AI algorithms. The application maps patients’ heartbeats using a visualization technique called Phase Space Tomography which is then analyzed by algorithms and forwarded to a doctor.

Analytics 4 Life's Medical Imaging Technology

Credit: Analytics 4 Life

The imaging device is being developed specifically for the monitoring of coronary artery disease, and unlike other methods, there is no need for radiation, heart rate acceleration, or injections of contrast agents. The company’s machine learning algorithms are currently in the clinical trial stage, and are being tested in 13 hospitals in Canada and the U.S.

Click for company websiteFounded in 2015, Ottawa startup Interset has raised $24 million to develop a cybersecurity solution based on machine learning. That’s nothing new, since we’ve talked about quite a few firms using AI for cybersecurity. In a familiar story, Interset’s tool is built on an open big data platform that is scalable to the size of each client. Unsupervised machine learning algorithms use all that delicious big data to look at the context of a threat to arrive at a conclusion, reducing false positives and flagging high-risk threats without the need for humans. Interset’s marketing team did a good job of dedicating an entire page to the inevitable “how are you different” question which can be summarized as follows:

After extensive testing, the U.S. intelligence community determined that Interset’s visionary architecture can achieve threat detection that’s faster and more accurate than any other analytics-based product.

Being better than everyone else is a good start. The company’s strategy is to connect and augment existing security systems like data loss prevention, endpoint detection and response, and identity and access management. In most cases these are separate applications that don’t talk to each other, making it impossible to view cybersecurity holistically and prioritize threat levels. Interset adds an overlay that orchestrates all cybersecurity operations and fills gaps, so clients don’t need to remove and replace current systems that cover certain areas fine.

Click for company websiteFounded in 2012, Montreal startup mnubo has raised $17 million to develop an Internet of Things (IoT) analytics solutions for consumer products and industrial assets. Clients can use mnubo’s software-as-a-service platform to receive close to real-time information on the usage and state of connected products ranging from coffee makers to mining machinery. It all boils down to a comprehensive view on product usage, which in turn translates to increased customer engagement and stickiness, predictive trends, and better product development.

Sensors in your washing machine tell its manufacturer which programs you use, when, and how frequently, for example. This information allows for predictive maintenance and might be useful to product managers as well. In industrial settings, companies can maximize utilized capacity, like scheduling repairs on car assembly robots and arranging replacements weeks ahead of the needed repairs. Mnubo also offers IoT consulting for companies looking to develop their IoT strategy from scratch and provides the machine learning framework to make sense out of all your delicious big data. The company recently opened an office in Japan, despite the fact that not a single person in the entire country will be able to pronounce “mnubo”.


Click for company websiteFounded in 2014, Toronto startup Deep Genomics came across our radar a few years ago when we wrote about how they were “applying deep learning to gene editing“. The startup has raised $16.7 million in funding from the likes of Khosla Ventures to create an AI platform for gene-based drug development, using deep learning to analyze genomic data and identify genes responsible for certain diseases, then building drugs to address the behavior of these faulty genes. Their team has built a “library” of tens of billions of chemical compounds that can be searched efficiently using their algorithms, and which, based on their qualities, might become drug candidates. Current research is focused on genetically defined metabolic and neurodegenerative disorders (these happen when neurons lose their function or die, like in the case of Parkinson’s or Alzheimer’s). Deep Genomics has also teamed up with Wave Life Sciences (WVE) to explore drug candidates for the treatment of neuromuscular disorders that impair proper functioning of muscles.


Click for company websiteFounded in 2012, Toronto startup Statflo has raised $14.4 million to develop a sales acceleration tool combining big data analytics and human sales coaching. At implementation, Statflo’s data engine imports sales and customer relationship data, removes duplicates, and categorizes customer action items. The output is a so-called Smart List that gives the retailer a list of clients that are “low hanging fruit”. Client calls can be made by sales reps in their free time or can also be handled by Statflo’s Customer Success team.

Statflo screenshot

Credit: Statflo

The combination of AI analytics and live sales coaching results in double-digit sales increases in 60 days and a minimum of four times return on investment (ROI), Statflo claims. The company specifically targets wireless and technology retailers at the moment. The application interface looks like a step-by-step sales coach for dummies, something that may come in handy at offshore call centers.

Click for company websiteFounded in 2011, Edmonton startup Granify is another company helping e-commerce retailers maximize sales using big data, something we touched on before in an article last year titled “7 Examples of AI in Retail and e-Commerce“. Granify has raised $13.5 million in funding so far to develop a solution that monitors the minute details of customer behavior on websites like products viewed, scroll speed, and mouse movements to come up with an optimal customer journey and automatically handle potential objections of customers. When a prospect wants to leave the website, Granify sends a message to him or her handling the most probable objection.

What Granify Does

What Granify Does – Credit: Granify

The platform analyzes more than 400 data points per second in real-time and claims to deliver a 3-5% increase in revenues within 90 days of implementation by enhancing conversion rates. Considering that a big part of an e-commerce marketing budget is spent on obtaining traffic, the results achieved by Granify directly strengthen a retailer’s bottom line. Granify charges for their product based on performance fees, which shows how strongly they believe in their product, and which helps reduce those pesky “cost objections”.

Click for company websiteFounded in 2015, Waterloo startup ApplyBoard has raised $13.5 million to develop an AI-assisted online marketplace for international high school, undergraduate, and postgraduate applications. The company’s platform analyzes applicant profiles including academic background, desired studies, and financial situation to recommend the best matches for each student. Thanks to this vetting process, ApplyBoard boasts an impressive 95% acceptance rate among the 10,000+ students who used the service so far by answering six questions.

The ApplyBoard Process
The ApplyBoard Process

ApplyBoard currently offers positions in more than 750 schools in the US and Canada, and was founded by a group of international students facing the exact challenges they are trying to alleviate now, and is getting lots of positive feedback from students and institutions alike for their great communication and customer service skills. It’s great to see international startup success being built on a valuable cause like education – very Canadian.

So there you have it, ten Canadian AI startups with the most funding to date, 40% of which are dabbling in the retail space. As Bono once said, “the world needs more Canada”, and it looks like that’s what they’re going to get.

Read Source Article: nanalyze

#ArtificialIntelligence #startups #MachineLearning #DeepLearning #DataScience #Technology #AI #TechGiants #entrepreneur

In recent years, as artificial intelligence has been used to develop a “vast explosion of applications” that can change how business is done, there’s been some disagreement over just how quickly customer service will undergo a sea change. Some experts have argued that the AI revolution will be slower to come to customer-facing fields, since “customers still want to talk to a human being,” and technology “can’t replace the human touch.”

My own view is that we’ll be seeing major changes sooner rather than later. I think AI will create a massive disruption to customer service in 5 to 10 years—probably closer to 5.

Are customers giving up on the “human touch?” No. But it turns out that they don’t need that “human touch” to always come from a human. IBM saysthere’s general agreement that “advancing the ability of computers to interact with us in a more natural way is critical for the AI-human relationship to reach its fullest potential.” And to that end, within 3 to 5 years, “advances in AI will make the conversational capabilities of computers vastly more sophisticated.”

This may sound all well and good, but truly excellent customer service representatives don’t just speak in a natural way. They also tailor their messages and their styles to each customer. Can AI really compete in this way?

Increasingly, yes. A team of Scottish researchers reported that, just as people align their communication when they interact, “there is strong evidence” human-computer interaction can achieve the same.

But what about unspoken cues? Customer service personnel need to take note of changes in a buyer’s tone of voice, facial expressions and body language. This presents AI with perhaps its steepest challenge. “Analyzing emotions in real time is a mathematical challenge of astronomical proportions,” according to Nova. Learning to decipher such things means wading through a “tsunami of data.” But it’s happening. Steadily, AI is being taught to analyze and even predict emotional responses.

In fact, AI is already doing a much better job than most people realize. A survey found that only 37% of respondents say they’ve used AI. But among those who thought they had not, 63% actually had.

Before joining my current company, Nextiva, I worked in retail. I believe it will be one of the first industries to see seismic impacts. These will take place not just when buying things online, but also in brick-and-mortar stores. Expect to see more interactive kiosks to recommend and help you find items, and new technologies at checkout that suggest other purchases you might want to make, either right then or the next time you visit. (These technologies will then send you reminders.)

Already, we’ve seen humanoid robots taken out for a spin at Asian retail stores. Amazon , which controls nearly half the e-commerce market and 5% of all retail spending, is already implementing AI across its operations. And fierce competition will give more and more brands the incentive to follow suit.

In any industry, the businesses that do the best job of harnessing AI for customer service will be those that know their customers’ journey and their personas. If business leaders don’t understand their customers and their needs to begin with, AI can’t help them. (In the case of B2B organizations, this means knowing not only the buyers’ needs, but also the needs of their customers.)

While these changes will eventually mean less need for hiring people to answer phone calls and handle live chats, there will be an even greater need for employees to help build and improve these new AI technologies and to make them work for your company. So it doesn’t mean jobs will disappear altogether, but it will likely put more pressure on low-skilled workers and open up more opportunities for data scientists, for example.

Of course, the crystal ball is always a bit fuzzy. Just how soon all this will happen we can’t be sure. But it’s likely that a recession could move up the time frame, since “interest in automation accelerates during economic downturns.” Already, the algorithms that power AI are getting more powerful every day, and businesses of all sizes are starting to experiment.

So it’s time to set aside proclamations that the AI revolution in customer service is far off. Based on what we know today, it may be just beyond the horizon.

Charles Ingram is chief product officer at Nextiva.

Read Source Article: Barron's

#AI #CustomerCare #MachineLearning #Service #Automation 

As we move into 2019, two things remain on all our minds: how did the year pass by so quickly, and how long will it be before artificial intelligence conquers the world and subjugates humankind?

I jest of course, but even when we put our fears of the unknown aside, the rapid development of artificial intelligence and machine learning technology is nothing to scoff at. That’s because AI and ML technologies have already changed customer experiences, marketing, manufacturing, retail, farming, world governments, transportation, and everything in between.  In fact, the global AI market is set to grow to $89.8 billion by 2025, which would mean a growth rate of 20x between 2017 and 2025.

2018 certainly played its role in that year-on-year growth. It was a year of progress for the AI and ML space, giving the CMSWire team a whole lot of questions to ask, news to cover, and fears to allay. Here are CMSWire’s top ten AI and ML editorials from 2018.

10. The Challenges Facing Today's Artificial Intelligence Strategies

The hype surrounding artificial intelligence (AI) is intense despite that fact that as yet, artificial intelligence (AI) for most enterprises is still at an early, or planning, stage. While a lot has been done, there is a lot more to do before it becomes commonplace.

9. How 5 Companies Successfully Introduced AI Into the Customer Experience

Do we still need humans to power customer experiences? Yes, according to Forrester. In its Digital CX Trends 2018 report released today, Forrester researchers found that "while AI, intelligent agents, and chatbots were central to the business conversation in 2017, most companies discovered they lack the design acumen and technical chops to seize the opportunities."

8. 8 Ways to Measure Chatbot Program Success

Some of your customers prefer to work with chatbots as they connect with you, according to Inbenta’s Chatbot Consumer and Business Survey. Their data shows that 50 percent of consumers prefer chatbots when shopping online, rather than calling support. And 72 percent of consumers say that chatbots hold the answer to frustration-free customer service.

7. How Artificial Intelligence Will Impact the Future of Work

We are rapidly moving toward a workplace where people interact with machines on a routine basis. With the advent of artificial intelligence (AI), and the chatbots that it powers, technology is now interwoven into many of our everyday job tasks. In fact, it has been reported that more than 80 percent of businesses plan to be using chatbots by 2020.

6. The Future of Customer Experience Is AI: Are You Ready?

“The robots are coming, the robots are coming!” said my colleague and artificial intelligence expert Kimberly Nevala in a tongue-in-cheek teaser for her new ebook, “Making Sense of AI.” She is right. In fact, in the context of digital transformation and customer experience, artificial intelligence (AI) already has a foot in the door. And that foot is poised to kick the door wide open.

5. 6 Ways Artificial Intelligence Will Impact the Future Workplace

Most employers do not feel threatened by artificial intelligence. According to recent data from work benefits giant MetLife, 56 percent of employers demonstrated a positive view of automation technologies like artificial intelligence (AI), analytics and even robots.

4. Why Artificial Intelligence Will Create More Jobs Than it Destroys

According to Gartner's 2017 hype cycle for emerging technologies, artificial intelligence (AI) will automate 1.8 million people out of work by 2020. While the job losses generate the most interest and headlines, the losses only tell part of the story.

3. Setting Expectations on Excel's Addition of AI

Microsoft has announced this week, at its Ignite conference in Orlando, that it is adding four new AI features to its ubiquitous and beloved Excel spreadsheet, continuing a development trend that it first announced at the beginning of the year.

2. 11 Industries Being Disrupted By AI

In the world of technology, the mantra "innovate or die" is truer for organizations than ever, and artificial intelligence (AI) is redefining industries by providing greater personalization to users, automating processes, and disrupting how we work.

1. 7 Ways Artificial Intelligence Is Reinventing Human Resources

Organizational leaders and human resources executives have faith that merging artificial intelligence (AI) into HR functions like onboarding and administration of benefits can and will improve the overall employee experience.

Read Source Artice : CMS Wire

#ArtificialIntelligence #MachineLearning #

CAMBRIDGE, Mass. — Hal Abelson, a renowned computer scientist at the Massachusetts Institute of Technology, was working the classroom, coffee cup in hand, pacing back and forth. The subject was artificial intelligence, and his students last week were mainly senior policymakers from countries in the 36-nation Organization for Economic Cooperation and Development.

Mr. Abelson began with a brisk history of machine learning, starting in the 1950s. Next came a description of how the technology works, a hands-on project using computer-vision models and then case studies. The goal was to give the policymakers from countries like France, Japan and Sweden a sense of the technology’s strengths and weaknesses, emphasizing the crucial role of human choices.

“These machines do what they do because they are trained,” Mr. Abelson said.

The class was part of a three-day gathering at M.I.T., including expert panels, debate and discussion, as the Organization for Economic Cooperation and Development seeks to agree on recommendations for artificial intelligence policy by this summer.

But where are policymakers supposed to even start? Artificial intelligence seems to be everywhere, much hyped, much feared yet little understood. Some proclaim A.I. will be an elixir of prosperity, while others warn it will be a job killer, even an existential threat to humanity.

The organization’s declarations, when they come, will not carry the force of law. But its recommendations have a track record of setting standards in many countries, including guidelines, going back to 1980, that called on nations to enact legislation to protect privacy and defined personal data as any information that can be used to identify an individual.

The recommendations carry weight because the organization’s mission is to foster responsible economic development, balancing innovation and social protections.

Hal Abelson of M.I.T. led a session about artificial intelligence for the attendees, who represented countries in the Organization for Economic Cooperation and Development.CreditKayana Szymczak for The New York Times
Hal Abelson of M.I.T. led a session about artificial intelligence for the attendees, who represented countries in the Organization for Economic Cooperation and Development.CreditKayana Szymczak for The New York Times

“We’re hoping to get out in front and help create some sort of policy coherence,” said Andrew Wyckoff, the group’s director for science, technology and innovation.

Here are a few themes that emerged at the gathering — ideas that could help shape the debate for years to come.

Regulation is coming. That’s a good thing. Rules of competition and behavior are the foundation of healthy, growing markets.

That was the consensus of the policymakers at M.I.T. But they also agreed that artificial intelligence raises some fresh policy challenges.

Today’s machine-learning systems are so complex, digesting so much data, that explaining how they make decisions may be impossible. So do you just test for results? Do you put self-driving cars through a driver’s test? If an A.I. system predicts breast cancer better than humans on average, do you just go with the machine? Probably.

“It’s very clear — you have to use it,” said Regina Barzilay, an M.I.T. computer scientist and a breast cancer survivor.

But handing off a growing array of decisions is uncomfortable terrain. Practical rules that reassure the public are the only path toward A.I. adoption.

CreditKayana Szymczak for The New York Times

“If you want people to trust this stuff, government has to play a role,” said Daniel Weitzner, a principal research scientist at the M.I.T. Computer Science and Artificial Intelligence Laboratory, who was a policy adviser in the Obama administration.

New regulation is often equated with slower growth. But the policymakers at the event said they did not want to stop the A.I. train. Instead, they said, they want their countries fully on board. Nations that have explicit A.I. strategies, like France and Canada, consider the technology an engine of growth, and seek to educate and recruit the next generation of researchers.

“Machine learning is the next truly disruptive technology,” said Elissa Strome, who oversees A.I. strategy at the Canadian Institute for Advanced Research, a government-funded organization. “There are huge opportunities for machine learning in fields like energy, environment, transportation and health care.”

International cooperation, the attendees said, would help ensure that policymaking was not simply left by default to the A.I. superpowers: the United States, which is a member of the Organization for Economic Cooperation and Development, and China, which is not.

“We think there can be a new model for the development of artificial intelligence that differs from China or California,” said Bertrand Pailhès, the national coordinator for France’s A.I. strategy.

In the view of Mr. Pailhès and others, China is a government-controlled surveillance state. In the American model, coming from Silicon Valley in California, a handful of internet companies become big winners and society is treated as a data-generating resource to be strip mined.

“The era of moving fast and breaking everything is coming to a close,” said R. David Edelman, an adviser in the Obama administration and the director of the project on technology, policy and national security at M.I.T.

From left, Susumu Hirano, Osamu Sudoh and Nobuhisa Nishigata, advisers to the government in Japan, which is investing heavily in the development of A.I. technology.CreditKayana Szymczak for The New York Times

In Japan, artificial intelligence is being seized as a lever to spur dynamism in its stodgy, hierarchical corporate culture. Japan is investing heavily to encourage the development of A.I. technology with a particular emphasis on “start-ups, small companies and young people,” said Osamu Sudoh, a professor at the University of Tokyo and a senior adviser to the Japanese government on A.I. strategy.

One specific policy issue dominated all others: the collection, handling and use of data.

Fast computers and clever algorithms are important, but the recent explosion of digital data — from the web, smartphones, sensors, genomics and elsewhere — is the oxygen of modern A.I.

“Access to data is going to be the most important thing” for advancing science, said Antonio Torralba, director of the M.I.T. Quest for Intelligence project. So much data is held privately that without rules on privacy and liability, data will not be shared and advances in fields like health care will by stymied.

Artificial intelligence can magnify the danger of data-driven injustice. Public-interest advocates point to the troubling missteps with the technology — software, for example, that fails to recognize the faces of black women or crime-prediction programs used in courtrooms that discriminate against African-Americans.

In such cases, data is the problem. The results were biased because the data that went into them was biased — skewed toward white males for facial recognition and the comparatively high percentage of African-Americans in the prison population.

“Are we just going to make the current racist system more effective, or are we going to get rid of embedded bias?” asked Carol Rose, executive director of the American Civil Liberties Union of Massachusetts.

These are issues of both technology design and policy. “Who is being mistreated? Who is being left out?” Mr. Abelson asked the class. “As you think about regulation, that is what you should be thinking about.”

Read Source Article: nytimes

#AI #MachineLearning #DeepLearning #MIT #ArtificialIntelligence

Here are five recent studies that investigate applications for AI in healthcare, as covered by Becker's Hospital Review:

1. AI outperforms clinicians, Pap smears in detecting cervical cancer

Researchers created a visual evaluation algorithm that uses deep learning to detect cervical precancer and cancer more accurately than human clinicians, according to a study published in the Journal of the National Cancer Institute.

To train the algorithm, researchers used archived photos of cervixes from a previous study that followed 9,406 women ages 18-94 in Costa Rica for seven years.

The algorithm detected more suspicious areas and cancers than human clinicians visually inspecting the cervix, researchers found. The algorithm also identified more cancers than Pap smear tests.

Researchers said the technology could improve cervical cancer screening and treatment for women in low- and middle-income countries, where 90 percent of cervical cancer deaths occur.


2. Boston Children's researchers tap machine learning for better flu surveillance

Researchers created a surveillance model that uses machine learning to provide highly accurate estimates of local flu activity, according to a study published in Nature Communications.

For the study, researchers from the Computational Health Informatics Program at Boston Children's Hospital combined two forecasting methods with machine learning to estimate flu activity. 

The first model, ARGO, uses data from EHRs, flu-related Google searches and historical flu activity for a specific location. The second model analyzes information on spatial-temporal flu activity for nearby locations. Researchers trained the new machine learning model, called ARGOnet, using flu predictions from both models and actual flu data.

"The system continuously evaluates the predictive power of each independent method and recalibrates how this information should be used to produce improved flu estimates," senior author Mauricio Santillana, PhD, a CHIP faculty member, said in a press release.

Researchers used ARGOnet to analyze flu seasons from September 2014 to May 2017 and found the model made more accurate predictions than ARGO in more than 75 percent of states included in the analysis.


3. Why this tool to predict readmission risk may have a blind spot

The "LACE index," a tool physicians and nurses often use to determine hospital patients' readmission risk, may have a blind spot, according to new research from Morgantown-based West Virginia University.

LACE stands for length of stay, acuity, comorbidity and emergency department (the four readmission risk factors the index considers). Patients who score higher in these four areas usually have an increased readmission risk.

But the researchers found the LACE index does not consider a key variable that may improve its predictions: whether patients are on Medicaid.

"LACE was validated and tested in Ontario, Canada," said researcher Jennifer Mallow, PhD, MSN. "The LACE index didn't look at things like payer because they have universal healthcare."

To evaluate the index's predictive value, the researchers compared patients' 30-day readmission rates to their LACE index scores, insurance status and functional issues such as illiteracy and substance misuse.

The only LACE variable that was linked to increased readmission rates was comorbidities, and the correlation was not very strong, the researchers found.

Additionally, LACE scores were typically higher for patients who did not return to the hospital, even though its design says the opposite.

The researchers found payer type had a significant relationship to readmission rates, and determining whether patients are on Medicaid could help providers better predict their readmission risk.

Including insurance status in the LACE index or more reliable measures of health disparities may help providers determine which patients have the highest risk of readmission, the researchers said.


4. AI tool can measure physicians' diagnostic performance, study finds

Researchers have developed a way to quantitatively measure physicians' clinical reasoning abilities using artificial intelligence and machine learning, according to a study published in JAMA Network. 

The research team engaged 1,738 physicians, residents and medical students for the study, during which the participants used software from the Human Diagnosis Project to solve case simulations. The researchers used automated techniques to measure the participants' performance,based on three categories: accuracy, efficiency and a combined score known as Diagnostic Acumen Precision Performance. Their performance also was weighed against individual level of medical training.

Attending physicians earned the highest scores in accuracy using the AI tool, followed by residents, interns and medical students, respectively. Attending physicians also earned the highest scores in efficiency, followed by residents, interns and medical students.

Moreover, attending physicians from a U.S. News and World Report-ranked institution scored higher than nonaffiliated attending physicians.

"The data suggest that diagnostic performance is higher in those with more training and that [Diagnostic Acumen Precision Performance] scores may be a valid measure to appraise diagnostic performance," the study authors write. "This diagnostic assessment tool allows individuals to receive immediate feedback on performance through an openly accessible online platform."

To access the complete study in JAMA Networkclick here.


5. How machine learning can reduce tests, improve treatments for ICU patients

Researchers from Princeton (N.J.) University are using machine learning to design a system that could reduce the frequency of tests and improve the timing of critical treatments for intensive care unit patients.

o create the system, the researchers used data from more than 6,060 patients admitted to the ICU between 2001 and 2012. The research team presented its results Jan. 6 at the Pacific Symposium on Biocomputing in Hawaii.

The analysis looked at four blood tests measuring lactate, creatinine, blood urea nitrogen and white blood cells. These indicators help diagnose two serious problems for ICU patients: kidney failure or sepsis.

"Since one of our goals was to think about whether we could reduce the number of lab tests, we started looking at the [blood test] panels that are most ordered," said co-lead study author Li-Fang Cheng.

The team's algorithm uses a "reward function" that encourages a test order based on how informative the test is at a given time. In other words, there is greater reward in giving a patient a test if there is a higher probability that the patient's state is significantly different from the previous measurement.

To test the utility of the lab-testing policy they created, the researchers compared the reward function values that would have resulted from applying their system with the testing regimens that were actually used for the 6,060 patients in the study. 

The researchers found the policy the machine learning algorithm generated would have yielded more information on the patient's condition than the actual testing their clinicians followed.

Additionally, when looking at white blood cell tests, the algorithm could have reduced the number of lab test orders by up to 44 percent.

They also found their approach would have helped alert clinicians to intervene sometimes hours sooner when a patient's condition started to deteriorate.

"With the lab test-ordering policy that this method developed, we were able to order labs to determine that the patient's health had degraded enough to need treatment, on average, four hours before the clinician actually initiated treatment based on clinician-ordered labs," said senior study author Barbara Engelhardt, PhD.


Read Source Article: beckershospitalreview

#AI #Healthcare #Technology #Development #Research

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