Category Archives: Machine Learning

Word in the tech community is that Apple ($NASDAQ:AAPL) employees are begnning to report hiring freezes for certain groups within the company. But other reports are that hiring is continuing at the Cupertino tech giant. In fact, we've reported on the former.

It turns out that both reports are correct. For some divisions, like Marketing and Corporate Functions, openings have been reduced. But for others, like Hardware and Machine Learning, openings and subsequent hiring appear to be as brisk as ever.

To be clear, overall, job listings at Apple have been cut back.

As recently as mid-March, Apple job listings were nearing the 6,000 mark, which would have been the company's most prolific hiring spree in history. But in late March, it became clear that no one would be going into the office any time soon, and openings quickly began disappearing from Apple's recruitment site. As of this week, openings at Apple are down to 5,240, signaling a decrease in hiring of about 13%.

But not all divisions are stalling their job listings. NeitherApple's "Hardware" or"Machine Learning and AI" groups show a decline in job listings of note.

Hardware openings are flat at worst. Today's 1,570 openings isn't significantly different than a high of 1,600 in March.

Apple's "Machine Learning and AI" group remains as healthy as ever when it comes to new listings being posted to the company's careers sites. As of this week, the team has 334 openings. Last month, that number was 300, an 11% increase in hiring activity.

However, other groups at Apple have seen significant decreases in job listings, including "Software and Services", "Marketing", and "Corporate Functions".

Apple's "Software and Services" team saw a siginificant drop in openings, particularly on April 10, when around 110 openings were cut from the company's recruiting website overnight. Since mid-March, openings on the team have fallen by about 12%.

Between April 14 and April 23, the number of listings for Apple's "Marketing" team dropped by 84. In late March, Apple was seeking 311 people for its Marketing team. Since then, openings have fallen by 36% for the team.

"Corporate Functions" jobs at Apple, which include everything from HR to Finance and Legal, have also seen a steep decline in recent weeks. In late March, Apple listed more than 300 openings for the team. As of this week, it has just around 200 openings, a roughly 1/3 hiring freeze.

So is Apple in the middle of a hiring freeze? Some parts of the company appear frozen. Others appear as hot as ever. Given the in-person nature of Marketing and Corporate Functions jobs, it's not surprising that the company would tap the breaks on interviewing for such positions. On the other hand, engineers working on hardware and machine learning can be remote interviewed and onboarded with equipment delivery.

So, yes, and yes. Apple is, and is not, in the middle of a hiring freeze.

Thinknum tracks companies using the information they post online - jobs, social and web traffic, product sales and app ratings - andcreates data sets that measure factors like hiring, revenue and foot traffic. Data sets may not be fully comprehensive (they only account for what is available on the web), but they can be used to gauge performance factors like staffing and sales.

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Apple is on a hiring freeze ... except for its Hardware, Machine Learning and AI teams - Thinknum Media

The Weather Channel is now using artificial intelligence and weather data to help people make better decisions about going outdoors based on the likelihood of suffering from allergy symptoms.

Amid the COVID-19 pandemic, most people are taking precautionary measures in an effort to ward off coronavirus, which is highly communicable and dangerous. It's no surprise that we gasp at every sneeze, cough, or even sniffle, from others and ourselves. Allergy sufferers may find themselves apologizing awkwardly, quickly indicating they don't have COVID-19, but have allergies, which are often treated with sleep-inducing antihistamines that cloud critical thinking.

The most common culprits and indicators to predict symptomsragweed, grass, and tree pollen readingsare often inconsistently tracked across the country. But artificial intelligence (AI) innovation from IBM's The Weather Channel is coming to the rescue of those roughly 50 million Americans that suffer from allergies.

The Weather Channel's new tool shows a 15-day allergy forecast based on ML.

Image: Teena Maddox/TechRepublic

IBM's The Weather Channel is now using machine learning (ML) to forecast allergy symptoms. IBM data scientists developed a new tool on The Weather Channel app and weather.com, "Allergy Insights with Watson" to predict your risk of allergy symptoms.

Weather can also drive allergy behaviors. "As we began building this allergy model, machine learning helped us teach our models to use weather data to predict symptoms," said Misha Sulpovar, product leader, consumer AI and ML, IBM Watson media and weather. Sulpovar's role is focused on using machine learning and blockchain to develop innovative and intuitive new experiences for the users of the Weather Channel's digital properties, specifically, weather.com and The Weather Channel smart phone apps.

SEE: IBM's The Weather Channel launches coronavirus map and app to track COVID-19 infections (TechRepublic)

Any allergy sufferer will tell you it can be absolutely miserable. "If you're an allergy sufferer, you understand that knowing in advance when your symptom risk might change can help anyone plan ahead and take action before symptoms may flare up," Sulpovar said. "This allergy risk prediction model is much more predictive around users' symptoms than other allergy trackers you are used to, which mostly depend on pollenan imperfect factor."

Sulpovar said the project has been in development for about a year, and said, "We included the tool within The Weather Channel app and weather.com because digital users come to us for local weather-related information," and not only to check weather forecasts, "but also for details on lifestyle impacts of weather on things like running, flu, and allergy."

He added, "Knowing how patients feel helps improve the model. IBM MarketScan (research database) is anonymized data from doctor visits of 100 million patients."

Daily pollen counts are also available on The Weather Channel app.

Image: Teena Maddox/TechRepublic

"A lot of what drives allergies are environmental factors like humidity, wind, and thunderstorms, as well as when specific plants in specific areas create pollen," Sulpovar said. "Plants have predictable behaviorfor example, the birch tree requires high humidity for birch pollen to burst and create allergens. To know when that will happen in different locations for all different species of trees, grasses, and weeds is huge, and machine learning is a huge help to pull it together and predict the underlying conditions that cause allergens and symptoms. The model will select the best indicators for your ZIP code and be a better determinant of atmospheric behavior."

"Allergy Insights with Watson" anticipates allergy symptoms up to 15 days in advance. AI, Watson, and its open multi-cloud platform help predict and shape future outcomes, automate complex processes, and optimize workers' time. IBM's The Weather Channel and weather.com are using this machine learning Watson to alleviate some of the problems wrought by allergens.

Sulpovar said, "Watson is IBM's suite of enterprise-ready AI services, applications, and tooling. Watson helps unlock value from data in new ways, at scale."

Data scientists have discovered a more accurate representation of allergy conditions. "IBM Watson machine learning trained the model to combine multiple weather attributes with environmental data and anonymized health data to assess when the allergy symptom risk is high, Sulpovar explained. "The model more accurately reflects the impact of allergens on people across the country in their day-to-day lives."

The model is challenged by changing conditions and the impact of climate change, but there has been a 25% to 50% increase in better decision making, based on allergy symptoms.

It may surprise long-time allergy sufferers who often cite pollen as the cause of allergies that "We found pollen is not a good predictor of allergy risk alone and that pollen sources are unreliable and spotty and cover only a small subset of species," Sulpovar explained. "Pollen levels are measured by humans in specific locations, but sometimes those measurements are few and far between, or not updated often. Our team found that using AI and weather data instead of just pollen data resulted in a 25-50% increase in making better decisions based on allergy symptoms."

Available on The Weather Channel app for iOS and Android, you can also find the tool online atwww.weather.com. Users of the tool will be given an accurate forecast, be alerted to flare-ups, and be provided with practical tips to reduce seasonal allergies.

This story was updated on April 23, 2020 to correct the spelling of Misha Sulpovar's name.

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Image: Getty Images/iStockphoto

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IBM's The Weather Channel app using machine learning to forecast allergy hotspots - TechRepublic

Ever since we started relying on machines and automation, people have been worried about the future of work and, specifically, whether robots will take over their jobs. And it seems this worry is becoming increasingly justified, as an estimated 40% of jobs could be replaced by robots for automated tasks by 2035. There is even a website dedicated to workers worried about whether they could eventually be replaced by robots.

While machines and artificial intelligence are becoming more complex and, therefore, more able to replace humans for menial tasks, that doesnt necessarily apply to a wide number of industries. Here, well go through the sectors that continue to require the human touch.

Despite scientists best efforts, the language and translation industry cannot be replaced by machines. Currently, automatic translation programmes are being developed with deep learning, a form of artificial intelligence which allows the computer to identify and correct its own mistakes through prolonged use and understanding. However, this still isnt enough to guarantee a correct translation, as deep learning requires external factors, like language itself, to remain the same over time. As we know, language is constantly developing, often with changes so subtle, you cant tell its happening. For a machine to be able to accurately translate texts or speech, it would need to be constantly updated with every new modification, across all languages.

Machines are also less able to pick up on the nuances found in speech or text. Things like sarcasm, jokes, or pop culture references are not easily translated, as the new audience may not understand them. Translating idioms is a particularly common example of this, as these phrases are generally unique to their dialect. In the UK, for example, the phrase its raining cats and dogs means its raining heavily. You would not want this translated on a literal level. As London Translations state in an article on the importance of using professionals for financial text translation, literal translations are technically correct, but read awkwardly and can be difficult to comprehend due to poor knowledge of the source language. Needless to say, these issues would be totally unacceptable in a document as important as a financial report.

Translating with accuracy not only requires fluency in both languages, but also a complete understanding of cultural differences and how they can be compared. Machines are simply not able to naturally make these connections without having the information already inputted by a person.

Finding the perfect candidate for a role can get stressful, especially if you have a pool of excellent potential employees to choose from. However, there are now algorithms that recruiters can use to help speed the process up and, theoretically, pick the most suitable person for the job. The technology is being praised for its ability to remove discrimination, as it simply examines raw data, and thus omits any sense of natural prejudice. It can also work to speed up the hiring process, as a computer can quickly sift through applicants and present the most relevant ones, saving someone the job of having to manually read through every application before making a decision.

However, in practice, its not that simple. Recruiting the right candidate should be based on more than qualifications and experience. Personality, attitude, and cultural fit should also be considered when recruiters are finding a candidate, none of which can be picked up on by machines.

One way of minimising this risk could be to introduce the algorithm at an earlier stage, through targeted ads or to help sift through initial applications. This allows recruiters to look at relevant candidates, rather than those that wouldnt have passed the initial screening anyway. However, this could conversely work to introduce bias to the recruitment process. The Harvard Business Review found that the algorithm effectively shapes the pool of candidates, giving a selection of applications that are all similar, fitting the mould that the computer is looking for. The study found that targeted ads on social media for a cashier role were shown to 85% of women, while cab driver ads were shown to an audience that was around 75% black. This happened as the algorithm reproduced bias from the real world, without human intervention. Having people physically checking the applications can serve to prevent this bias, introducing a more conscious effort to carefully screen each candidate on their own merits.

More people than ever before are meeting their partners online, according to a study published by Stanford University. And while a matchmaking algorithm sounds like a dream for singletons, it doesnt mean that they are able to effectively set you up with your life partner. As these algorithms are actually the intellectual property of each app, Dr Samantha Joel, assistant professor at London, Canadas Western University, created her own app with colleagues. Volunteers were asked to complete a questionnaire about themselves and ideal partners, much like typical dating websites would. After answering over 100 questions, the data was analysed and volunteers were set up on four-minute-long speed dates with potential candidates. Joel then asked the volunteers about their feelings towards any of their dates.

These results then identified the three things needed to predict romantic interest: actor desire (how much people liked their dates), partner desire (how much people were liked by dates), and attractiveness. The researchers were able to subtract attractiveness from the scores of romantic interest, giving a measure of compatibility. However, while the algorithm could accurately predict actor and partner desire, it failed on compatibility. Instead, it may be worth sticking to the second most common way of meeting a partner through a mutual friend. Your friends will be able to make educated decisions about relationships, as they have a deeper understanding of preferences and compatibility in a way that a machine simply cant replicate.

Author Bio: Syna Smith is a chief editor of Business usa today. She has also good experience in digital marketing.

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The industries that can't rely on machine learning - The Urban Twist

According toBlueWeave Consulting, The globalArtificial Intelligence market&Advanced Machinehas reached USD 29.8 Billion in 2019 and projected to reach USD 281.24 Billion by 2026 and anticipated to grow with CAGR of 37.95% during the forecast period from 2020-2026, owing to increasing overall global investment in Artificial Intelligence Technology.

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Artificial Intelligence (AI) is a computer science algorithm and analytics-driven approach to replicate human intelligence in a machine and Machine learning (ML) is an enhanced application of artificial intelligence, which allows software applications to predict the resulted accurately. The development of powerful and affordable cloud computing infrastructure is having a substantial impact on the growth potential of artificial intelligence and advanced machine learning market. In addition, diversifying application areas of the technology, as well as a growing level of customer satisfaction by users of AI & ML services and products is another factor that is currently driving the Artificial Intelligence & Advanced Machine Learning market. Moreover, in the coming years, applications of machine learning in various industry verticals is expected to rise exponentially. Proliferation in data generation is another major driving factor for the AI & Advanced ML market. As natural learning develops, artificial intelligence and advanced machine learning technology are paving the way for effective marketing, content creation, and consumer interactions.

In the organization size segment, large enterprises segment is estimated to have the largest market share and the SMEs segment is estimated to grow at the highest CAGR over the forecast period of 2026. The rapidly developing and highly active SMEs have raised the adoption of artificial intelligence and machine learning solutions globally, as a result of the increasing digitization and raised the cyber risks to critical business information and data. Large enterprises have been heavily adopting artificial intelligence and machine learning to extract the required information from large amounts of data and forecast the outcome of various problems.

Predictive analysis and machine learning and is rapidly used in retail, finance, and healthcare. The trend is estimated to continue as major technology companies are investing resources in the development of AI and ML. Due to the large cost-saving, effort-saving, and the reliable benefits of AI automation, machine learning is anticipated to drive the global artificial intelligence and Advanced machine learning market during the forecast period of 2026.

Digitalization has become a vital driver of artificial intelligence and advanced machine learning market across the region. Digitalization is increasingly propelling everything from hotel bookings, transport to healthcare in many economies around the globe. Digitalization had led to rising in the volume of data generated by business processes. Moreover, business developers or crucial executives are opting for solutions that let them act as data modelers and provide them an adaptive semantic model. With the help of artificial intelligence and Advanced machine learning business users are able to modify dashboards and reports as well as help users filter or develop reports based on their key indicators.

Geographically, the Global Artificial Intelligence & Advanced Machine Learning market is bifurcated into North America, Asia Pacific, Europe, Middle East, Africa & Latin America. The North America is dominating the market due to the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. North America has rapidly changed, and the most competitive global market in the world. The Asia-pacific region is estimated to be the fastest-growing region in the global AI & Advanced ML market. The rising awareness for business productivity, supplemented with competently designed machine learning solutions offered by vendors present in the Asia-pacific region, has led Asia-pacific to become a highly potential market.

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Artificial Intelligence & Advanced Machine Learning Market: Competitive Landscape

The major market players in the Artificial Intelligence & Advanced Machine Learning market are ICarbonX, TIBCO Software Inc., SAP SE, Fractal Analytics Inc., Next IT, Iflexion, Icreon, Prisma Labs, AIBrain, Oracle Corporation, Quadratyx, NVIDIA, Inbenta, Numenta, Intel, Domino Data Lab, Inc., Neoteric, UruIT, Waverley Software, and Other Prominent Players are expanding their presence in the market by implementing various innovations and technology.

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Artificial Intelligence & Advanced Machine learning Market is expected to grow at a CAGR of 37.95% from 2020-2026 - Latest Herald

As budgets tighten amidst a global crisis, marketers are scrambling to find better sources of truth. Whether its good prospecting performance, campaign management, or audience optimisation, there are many areas of success when it comes to the programmatic landscape. To meet this need, programmatic advertising is increasingly being driven by machine learning. So why would anyone doubt machine learning?

Machine learning models are, in many ways expert liars. Machine learning optimises by any means necessary, and if blurring the truth or taking into account irrelevant information helps to optimise, then this is what occurs. Its scary to think how much an unchecked model could get away with in the fast-paced world of programmatic, where seconds count.

In fact, Artificial Intelligence (AI) researcher, Sandra Wachter, actually calls machine learning algorithms black boxes saying: There is a lack of transparency around machine learning decisions and theyre not necessarily justified or legitimate.

So, how can anyone ensure a machine learning model is telling the truth? The best way is to treat the model like a job interview candidate; that is, any statements made should be treated with the due amount of scepticism, and facts must always be checked.

When it comes to performance, everyone wants it better. However, while a model might offer better performance on face value, its important to ask how exactly is that measured:

Machine learning technology can be time consuming and expensive, and its remarkably easy to waste money on a bad algorithm. Having good, solid proof a model works is a great way to avoid wasting budget. Fact-checking and asking for more evidence is vital if unsure of results, and if the model vendor cant offer access to an analyst who can back up the numbers with the work, move on.

Just because all data is accessible, doesnt mean it should be used, or that each point of data is as important as another.

Is knowing whether someone has bought a product before as important as the colour of their socks? If all data in the machine learning model is being used, marketers must ask how and why. Why is all of the data used? Why is all this important? What tests were run to prove it? Is the model even allowed to use all the data?

Everyones familiar with the concept and purpose of GDPR and similar global legislation. So, you must make sure you ask the question about how data is being used, or run the risk of severe fines.

Brands have clear metrics to hit and its the job of client services, together with data engineering, to ensure the machine learning optimises towards the KPIs. However, the beauty of machine learning is it frees up the client services team to do more than just achieve the brands KPI; it can help brands achieve business goals, too.

With thousands of successful campaigns under their belts, client services know what works and what doesnt. Users should expect to be able to contact a specialist at any time to make sure its doing what the clients want.

When talking about purchasing machine learning with a vendor who cant (or wont) answer your questions, its time to bail. Marketers must feel empowered to ask any and all questions of vendors, and just like a job interview, if the answer isnt a good fit then neither is the candidate.

Not knowing about or not understanding machine learning is accepted. However, whats not acceptable is to not be allowed to question machine learning just does it. In order to innovate, especially in volatile environments, everyone needs to better understand machine learning and to achieve this, a two-way conversation is vital.

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Learning to Trust AI in Troubled Times - AiThority

Dozens of hospitals across the country are using an artificial intelligence system created by Epic, the big electronic health record vendor, to predict which Covid-19 patients will become critically ill, even as many are struggling to validate the tools effectiveness on those with the new disease.

The rapid uptake of Epics deterioration index is a sign of the challenges imposed by the pandemic: Normally hospitals would take time to test the tool on hundreds of patients, refine the algorithm underlying it, and then adjust care practices to implement it in their clinics.

Covid-19 is not giving them that luxury. They need to be able to intervene to prevent patients from going downhill, or at least make sure a ventilator is available when they do. Because it is a new illness, doctors dont have enough experience to determine who is at highest risk, so they are turning to AI for help and in some cases cramming a validation process that often takes months or years into a couple weeks.

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Nobody has amassed the numbers to do a statistically valid test of the AI, said Mark Pierce, a physician and chief medical informatics officer at Parkview Health, a nine-hospital health system in Indiana and Ohio that is using Epics tool. But in times like this that are unprecedented in U.S. health care, you really do the best you can with the numbers you have, and err on the side of patient care.

Epics index uses machine learning, a type of artificial intelligence, to give clinicians a snapshot of the risks facing each patient. But hospitals are reaching different conclusions about how to apply the tool, which crunches data on patients vital signs, lab results, and nursing assessments to assign a 0 to 100 score, with a higher score indicating an elevated risk of deterioration. It was already used by hundreds of hospitals before the outbreak to monitor hospitalized patients, and is now being applied to those with Covid-19.

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At Parkview, doctors analyzed data on nearly 100 cases and found that 75% of hospitalized patients who received a score in a middle zone between 38 and 55 were eventually transferred to the intensive care unit. In the absence of a more precise measure, clinicians are using that zone to help determine who needs closer monitoring and whether a patient in an outlying facility needs to be transferred to a larger hospital with an ICU.

Meanwhile, the University of Michigan, which has seen a larger volume of patients due to a cluster of cases in that state, found in an evaluation of 200 patients that the deterioration index is most helpful for those who scored on the margins of the scale.

For about 9% of patients whose scores remained on the low end during the first 48 hours of hospitalization, the health system determined they were unlikely to experience a life-threatening event and that physicians could consider moving them to a field hospital for lower-risk patients. On the opposite end of the spectrum, it found 10% to 12% of patients who scored on the higher end of the scale were much more likely to need ICU care and should be closely monitored. More precise data on the results will be published in coming days, although they have not yet been peer-reviewed.

Clinicians in the Michigan health system have been using the score thresholds established by the research to monitor the condition of patients during rounds and in a command center designed to help manage their care. But clinicians are also considering other factors, such as physical exams, to determine how they should be treated.

This is not going to replace clinical judgement, said Karandeep Singh, a physician and health informaticist at the University of Michigan who participated in the evaluation of Epics AI tool. But its the best thing weve got right now to help make decisions.

Stanford University has also been testing the deterioration index on Covid-19 patients, but a physician in charge of the work said the health system has not seen enough patients to fully evaluate its performance. If we do experience a future surge, we hope that the foundation we have built with this work can be quickly adapted, said Ron Li, a clinical informaticist at Stanford.

Executives at Epic said the AI tool, which has been rolled out to monitor hospitalized patients over the past two years, is already being used to support care of Covid-19 patients in dozens of hospitals across the United States. They include Parkview, Confluence Health in Washington state, and ProMedica, a health system that operates in Ohio and Michigan.

Our approach as Covid was ramping up over the last eight weeks has been to evaluate does it look very similar to (other respiratory illnesses) from a machine learning perspective and can we pick up that rapid deterioration? said Seth Hain, a data scientist and senior vice president of research and development at Epic. What we found is yes, and the result has been that organizations are rapidly using this model in that context.

Some hospitals that had already adopted the index are simply applying it to Covid-19 patients, while others are seeking to validate its ability to accurately assess patients with the new disease. It remains unclear how the use of the tool is affecting patient outcomes, or whether its scores accurately predict how Covid-19 patients are faring in hospitals. The AI system was initially designed to predict deterioration of hospitalized patients facing a wide array of illnesses. Epic trained and tested the index on more than 100,000 patient encounters at three hospital systems between 2012 and 2016, and found that it could accurately characterize the risks facing patients.

When the coronavirus began spreading in the United States, health systems raced to repurpose existing AI models to help keep tabs on patients and manage the supply of beds, ventilators and other equipment in their hospitals. Researchers have tried to develop AI models from scratch to focus on the unique effects of Covid-19, but many of those tools have struggled with bias and accuracy issues, according to a review published in the BMJ.

The biggest question hospitals face in implementing predictive AI tools, whether to help manage Covid-19 or advanced kidney disease, is how to act on the risk score it provides. Can clinicians take actions that will prevent the deterioration from happening? If not, does it give them enough warning to respond effectively?

In the case of Covid-19, the latter question is the most relevant, because researchers have not yet identified any effective treatments to counteract the effects of the illness. Instead, they are left to deliver supportive care, including mechanical ventilation if patients are no longer able to breathe on their own.

Knowing ahead of time whether mechanical ventilation might be necessary is helpful, because doctors can ensure that an ICU bed and a ventilator or other breathing assistance is available.

Singh, the informaticist at the University of Michigan, said the most difficult part about making predictions based on Epics system, which calculates a score every 15 minutes, is that patients ratings tend to bounce up and down in a sawtooth pattern. A change in heart rate could cause the score to suddenly rise or fall. He said his research team found that it was often difficult to detect, or act on, trends in the data.

Because the score fluctuates from 70 to 30 to 40, we felt like its hard to use it that way, he said. A patient whos high risk right now might be low risk in 15 minutes.

In some cases, he said, patients bounced around in the middle zone for days but then suddenly needed to go to the ICU. In others, a patient with a similar trajectory of scores could be managed effectively without need for intensive care.

But Singh said that in about 20% of patients it was possible to identify threshold scores that could indicate whether a patient was likely to decline or recover. In the case of patients likely to decline, the researchers found that the system could give them up to 40 hours of warning before a life-threatening event would occur.

Thats significant lead time to help intervene for a very small percentage of patients, he said. As to whether the system is saving lives, or improving care in comparison to standard nursing practices, Singh said the answers will have to wait for another day. You would need a trial to validate that question, he said. The question of whether this is saving lives is unanswerable right now.

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AI used to predict Covid-19 patients' decline before proven to work - STAT