Page 2,455«..1020..2,4542,4552,4562,457..2,4602,470..»

What is a Referral Under the Anti-Kickback Statute? – Lexology

Central to the practice of medicine, referrals are an important part of patient care. Referrals are also a critical component when evaluating whether arrangements between parties violate the Anti-Kickback Statute (AKS), which prohibits knowingly or willfully offering, paying, soliciting, or receiving any remuneration in return for referring patients for services that are reimbursable by federal health care programs. However, the AKS does not establish a definition for referral leaving it up to the courts to interpret and apply.

On June 8, 2021 in Stop Illinois Health Care Fraud, LLC v. Sayeed et al., the U.S. District Court for the Northern District of Illinois determined that a group of defendants violated the AKS by paying a community care organization for access to their client files in order to market services to federal health care program beneficiaries. In its analysis, the court determined that paying for the file access constituted a referral, the fees paid were intended to be remuneration for the referral of federal health care program beneficiaries, and that the defendants had violated the AKS.

The defendants originally entered into a management services agreement with the community care organization purportedly to assess the feasibility of creating an Accountable Care Organization. However, the file access also involved using the information for data mining purposes to identify and solicit services to federal health care program beneficiaries. In its ruling, the court applied a broad definition of referral to include payments intended to induce the community care organization to indirectly refer its clients to the defendants even if the arrangement was intended for a different purpose.

With the courts determination focusing on the referral prong of the AKS, this ruling could have a far reaching impact for parties wanting to enter into arrangements involving the exchange of patient information. It will be important to carefully evaluate and analyze any sort of arrangement involving patient file access in order to make sure there is no underlying objective by either party to use such information to solicit patients.

Read the original post:

What is a Referral Under the Anti-Kickback Statute? - Lexology

Read More..

INTRUSION to Participate at the HC Wainwright Global Investment Conference on September 14 – GlobeNewswire

PLANO, Texas, Aug. 31, 2021 (GLOBE NEWSWIRE) -- INTRUSION Inc. (NASDAQ: INTZ), a leading provider of cyberattack prevention solutions including zero-days, announced today that Joe Head, Chief Technology Officer, Franklin Byrd, Chief Financial Officer, and Gary Davis, Chief Marketing Officer will participate at the 23rd Annual H.C. Wainwright Global Investment Conference, which is being held as a virtual event. Management is scheduled to host virtual meetings with investors throughout the day on Tuesday, September 14th.

Portfolio managers and analysts attending the conference who would like to request a meeting with management should contact their H.C. Wainwright representative. An on-demand replay of the Companys presentation will be available starting at 7:00 A.M. EDT on Monday, September 13, 2021 to conference participants and also accessible in the Investor Relations section of the Companys website at http://www.intrusion.com.

About INTRUSION Inc.

INTRUSION Inc. (NASDAQ: INTZ) protects any-sized company by leveraging advanced threat intelligence with real-time artificial intelligence to kill cyberattacks as they occur including zero-days. INTRUSIONs solution families include INTRUSION Shield, an advanced cyber-defense solution that kills cyberattacks in real-time using artificial intelligence (AI) and advanced cloud threat intelligence; INTRUSION TraceCop for identity discovery and disclosure; and INTRUSIONSavant for network data mining and advanced persistent threat detection. For more information, please visit http://www.intrusion.com.

Cautionary Statement Regarding Forward-Looking InformationThis release may contain certain forward-looking statements, including, without limitations, statements about the performance of protections provided by our INTRUSION Shield product, as well as any other statements which reflect managements expectations regarding future events and operating performance. These forward-looking statements speak only as of the date hereof and involve a number of risks and uncertainties, including, without limitation, the risks that our products and solutions do not perform as anticipated or do not meet with widespread market acceptance. These statements are made under the safe harbor provisions of the Private Securities Litigation Reform Act of 1995 and involve risks and uncertainties which could cause actual results to differ materially from those in the forward-looking statements, including, risks that we have detailed in the Companys most recent reports on Form 10-K and Form 10-Q, particularly under the heading Risk Factors.

IR ContactJoel Achramowicz sheltonir@sheltongroup.comP: 415-845-9964

See more here:

INTRUSION to Participate at the HC Wainwright Global Investment Conference on September 14 - GlobeNewswire

Read More..

What Is Web Scraping? – TechBullion

Share

Share

Share

Email

Everyone has heard of web scraping at some point or another, the process of collecting information from the internet. Scraping could be anything, from copying and pasting a piece of text to automatized data collection on a large scale. Even as you read this text, youre basically scraping for data. Read on to learn more about this process and who can benefit from it.

When someone refers to web scraping (also known as web crawling, data mining, or data extraction), they usually mean the automated process of collecting data with a piece of software. A fantastic example of this procedure would be gathering pricing data from Amazon or similar sites for insight into price fluctuation over a specific period. If you wanted to gather this data, youd have to send many automated requests to the site to get the information and register every change that occurs.

Modern web scraping tools gather information and convert it into a usable format. Its usually turned into spreadsheets for small scraping projects, but more elaborate ones can use JSON files or APIs, which generally offer better customization options. Either way, the procedure is more or less the same in most cases you run a program, set the formatting options, and tell it where to store this information.

Web scraping is a prevalent practice among data analysts, data scientists, different types of researchers, and developers. They all use it to gather large amounts of information they can analyze. Companies often use data crawling to monitor market trends, the competition, protect their brand, find new leads, and explore new markets. End users utilize web scraping to find the best deals and get their hands on hard-to-get items like special edition sneakers. You can visit https://iproyal.com/sneaker-proxies/ to find out more.

No aggregator app, website, or service would function without web scraping. News aggregators can pull in relevant articles from all over the world. Stock market monitoring apps can gather relevant data and make accurate predictions based on the current trends in the market. Booking sites use complex data gathering setups to get pricing from all over the world, whether its hotel accommodation, airfare deals, or anything else.

If youre interested in putting together a web scraping project of your own, the first thing you should figure out is what kind of data youre interested in and where to get it from. Once thats out of the way, its a fairly simple process thanks to different available solutions you can use, each offering specific advantages and disadvantages.

Once youve gathered your sources, you need to figure out where you want to store the gathered data. You can use local storage or use a cloud platform. You can code your own custom web scraper or find an existing solution that has the features you need. Depending on your projects complexity, you can go with simple scraping browser extensions, highly customizable software solutions, or anything in between.

Web scraping browser extensions are usually easy to get running because theyre a part of your browser. On the other hand, theyre often very limited and dont offer any advanced features you may need. If you need a massive data-gathering setup, its probably best to go with a specialized solution with advanced features you cant find in browser extensions or DIY setups.

If youre gathering publicly available data, web scraping is completely legal. However, certain websites have developed protection against it and can make things a bit challenging. Most of the time, they will block a particular IP address when they notice it sends a large number of requests toward the site. Others introduce limitations like CAPTCHAs to prevent automatic scraping.

The easiest way to deal with this is by using a proxy service with many residential proxy servers worldwide. By using proxies, your scraper becomes immune to all types of blocks thanks to IP rotation. Every single request comes with a different IP address indistinguishable from a genuine visitor. This protects your own IP address and privacy. If youre after geo-restricted information from a particular region, proxy servers from that location will make sure the data you gather is 100% accurate.

Data makes a huge part of our lives, so were all involved in some type of web scraping even if we dont know it. Whenever you read the news or use your favorite shopping app, web scraping makes finding what youre looking for easier. If you plan to get into web scraping, dont forget to get educated on the subject and pick a solution that works best.

Read the original:

What Is Web Scraping? - TechBullion

Read More..

Aion Therapeutic Provides Notice of Default – Benzinga – Benzinga

TORONTO, Aug. 31, 2021 /CNW/ - Aion Therapeutic Inc. (CSE:AION)("Aion Therapeutic" or the "Company") announces that it anticipates being late in filing its audited annual financial statements (the "Annual Financial Statements") and related management discussion and analysis ("MD&A") for the year ended April 30, 2021, by the prescribed deadline of August 30, 2021, and its interim financial statements (the "Interim Financial Statements")and related MD&A for the interim period ended July 31, 2021, by the prescribed deadline of September 29, 2021.

During the year ended April 30, 2021, the Company completed the acquisition of 1196691 B.C. Ltd. d/b/a PCAI Pharma. As a result of this acquisition, the Company is delayed in providing its independent auditors with the necessary valuations and impairment testing required to gain reasonable comfort to complete and file the Annual Financial Statements and MD&A.

The Company has made an application with the applicable securities regulators under National Policy 12-203 Cease Trade Orders for Continuous Disclosure Defaults ("NP 12-203") requesting that a management cease trade order be imposed in respect of the anticipated late filing rather than an issuer cease trade order. The issuance of a management cease trade order does not affect the ability of persons who have not been directors, officers or insiders of the Company to trade in their securities.

The Company anticipates that it will in a position to prepare and file the Annual Financial Statements and related MD&A on or prior to September 29, 2021, and prepare and file the Interim Financial Statements and related MD&A on or prior to October 29, 2021.

The Company confirms that it will satisfy the provisions of the alternative information guidelines under NP 12-203 by issuing bi-weekly default status reports in the form of news releases for so long as it remains in default of the filing requirements to file the Financial Statements, the Interim Financial Statements and related MD&A within the prescribed period of time. The Company confirms that there is no other material information relating to its affairs that has not been generally disclosed.

About Aion Therapeutic Inc.

Aion Therapeutic Inc. through its wholly-owned subsidiary, AI Pharmaceuticals Jamaica Limited, is in the business of research and development, treatment, data mining and state-of-the-art artificial intelligence (machine learning) techniques, focused on the development of combinatorial pharmaceuticals, nutraceuticals and cosmeceuticals utilizing compounds from cannabis (cannabinoids), psychedelic mushrooms (psilocybin), fungi (edible mushroom), natural psychedelic formulations (Ayahuasca), and other medicinal plants in a legal environment for this type of discovery. In addition, Aion Therapeutic is creating a strong international intellectual property portfolio related to its discoveries.

DISCLAIMER & READER ADVISORY

Certain information set forth in this news release may contain forward-looking information that involve substantial known and unknown risks and uncertainties. This forward-looking information is subject to numerous risks and uncertainties, certain of which are beyond the control of the Company, including, but not limited to, the impact of general economic conditions, industry conditions, and dependence upon regulatory approvals. Readers are cautioned that the assumptions used in the preparation of such information, although considered reasonable at the time of preparation, may prove to be imprecise and, as such, undue reliance should not be placed on forward-looking information. The parties undertake no obligation to update forward-looking information except as otherwise may be required by applicable securities law.

SOURCE Aion Therapeutic Inc.

View original content to download multimedia: http://www.newswire.ca/en/releases/archive/August2021/31/c1920.html

Read more from the original source:

Aion Therapeutic Provides Notice of Default - Benzinga - Benzinga

Read More..

Datamatics recognized in the Gartner Hype Cycle for Natural Language Technologies, 2021 – Equity Bulls

Datamatics, a global Digital Solutions, Technology, and BPM company, today announced that it is recognized in Gartner Hype Cycle for Natural Language Technologies, 2021 under Text Summarization and Intelligent Document Processing (IDP). This report is authored by analysts Bern Elliot, Anthony Mullen, Adrian Lee, and Stephen Emmott.

This marks the second year in a row that Datamatics has been named in this Hype Cycle research for Text Summarization and the first time for Intelligent Document Processing (IDP). According to the report, "Recent advances in artificial intelligence and machine learning have enabled innovative approaches and advances in the field of natural language technologies. This report will assist IT leaders in assessing how and where these new opportunities and methods can best be applied."

Datamatics has its own platform TruAI which is a comprehensive Artificial Intelligence and Cognitive Sciences solution that helps enterprises leverage use cases related to pattern detection, text & data mining. It allows enterprises to extract intelligence from high volumes of data, including structured, unstructured, and multi-structured data from diverse sources.

Datamatics TruCap+ is an AI-enabled Intelligent Document Processing (IDP) product that allows enterprises to realize faster time-to-value and achieve greater Straight-Through Processing (STP) with accuracy.

Commenting on the inclusion, Mr. Mitul Mehta, Senior Vice President - Marketing and Communications, Datamatics, said, "80% of an enterprise data is structured and semi-structured. Using AI/ML models, Datamatics TruAI and TruCap+ IDP enbles enterprises to automate data extraction with high accuracy and ingest it into downstream systems. This enables enterprises to achieve process automation at scale. We are happy to be recognized in the Gartner Hype Cycle for Natural Language Technologies, 2021. I believe this inclusion is a milestone in our journey to a true automation world."

Shares of Datamatics Global Services Limited was last trading in BSE at Rs. 333.45 as compared to the previous close of Rs. 315.5. The total number of shares traded during the day was 388440 in over 7797 trades.

The stock hit an intraday high of Rs. 347.05 and intraday low of 321. The net turnover during the day was Rs. 132684431.

Visit link:

Datamatics recognized in the Gartner Hype Cycle for Natural Language Technologies, 2021 - Equity Bulls

Read More..

Why Uganda does not need car digital monitoring and tracking technologies to fight crime – Daily Monitor

By Guest Writer

In the 1990s, the UK government procured tacit consent from her citizens to use Closed Circuit Televisions (CCTVs) dubbed as the miracle solution to criminality. A document titled CCTV-Looking Out for You was published in 1994 to reinforce support for the use of video surveillance as a faultless crime-fighting machine. With over 5.2 million cameras spread across the country (one camera for every 13 Britons), the crime rate per capita in the UK is still high although UK citizens are the most virtually herded people on planet earth.

For Ugandans now, imagine that one day you woke up and your freedom of movement (forget Covid-19 movement restrictions) has a price on it, and the price is that every time you leave your home using your car or motorcycle, the government will be able to know all your whereabouts, daily routines, capture and keep all these records in a database for a given period, not to mention predict your future movement using complex algorithms and data mining tools. This is exactly what the proposed car digital monitoring and tracking system will do, disguised as an Intelligent Transport Management System (ITMS). How will this be done? Simple! Have all automobiles fitted with Automatic Number Plate Recognition (ANPR) devices and install ANPR CCTV cameras on roads that will capture all details about the automobile like direction and a web of travel routes, timestamp, drivers details, make and color of automobile among others, all done in the mighty name of fighting crime. But do we need all these Big Brother intrusive technological surveillance systems to curb crime?

Crime is such a complex social phenomenon caused by a multivariate of factors whose prevention measures or models cannot simply be thought of, or assumed to be monolithic. To think or make such assumptions is to have a nave understanding of crime as a social construct. And to worship a system of hi-tech surveillance cameras as crime saviours is a mistaken belief in the powers of video surveillance known as the CCTV myth. No empirical research to date proves that hi-tech video surveillance systems have any general impact on crime reduction. Studies in the US and several commissioned by the UK Home Office to study the impact of video surveillance systems on crime found no statistically significant relationship between the two. A 2009 House of Lords Committee Report on video surveillance also noted that CCTV cameras were not as effective in preventing crime as earlier believed. CCTVs were instead found to be most effective in dissuading car thefts but not preventing them as such. Overall, studies show that areas manned with cameras do not outperform those without them in terms of crime prevention.

But why do governments still insist on justifying the use of hi-tech surveillance technologies as crime-fighting tools yet evidence shows that they arent? The always quick and blunt answer from government officials is if you are not doing anything wrong, you have nothing to worry about. On 8th June 1949, George Orwell, published a novel titled 1984. In the book, he warned that that societies would be doomed if they left unchecked the kind of totalitarian thinking taking root in the minds of policy maker and intellectuals. Many citizenspolicymakersworld are currently in this Orwellian state of affairs, where their governments unjustifiably seek measures of social control which instead restrict citizens fundamental rights and freedoms such as freedom of movement, privacy, and autonomy.

The ANPR technology was first developed in the UK in the 1970s by the Home Offices Scientific Development Branch and tested in the 1980s to aid investigations into allegedly stolen cars. This was initially done by comparing digital film shots of a vehicle number plate to a database of allegedly stolen vehicles. This technology was further developed into the ANPR network found in the UK today, with over 10 billion peoples data recorded and stored in the database. Londons transport system has over 1,400 cameras collecting number plate data of vehicles to keep tabs on traffic congestion and carbon emission. This is an ITMS led transportation system commonly found in heavy industrial complexes and smart cities to control traffic and reduce accidents. How feasible and sustainable is an ITMS in Uganda, or specifically for Kampala citys chaotic transport management system? And how will it help curb crime, since its part of the main justifications for the Russian deal?

On 24th July 2013, the Information Commissioners Office (ICO), UKs data protection authority issued an Enforcement Notice to the Hertfordshire Constabulary Police instructing it to halt its use of the vehicle number plate tracking system in Royston town it considered illegal and unlawful. This followed complaints by those concerned about the Polices use of ANPR to track all cars entering and leaving the city and that yet were installed devoid of any public debate or legal framework. The ICO held that the use of ANPR cameras and other forms of surveillance systems must be justified and proportionate to the problems it sought to address with a prior comprehensive assessment of their impact on the privacy of road users. However, despite being a much welcomed and fair ruling, the problem here is is at least twofold. Firstly, the ICO is a quasi-judicial body whose decisions are only advisory and not binding in law. Secondly, some activists think that calls for justification and proportionality in using surveillance systems only rubber stamps and legitimizes state intrusion of citizens privacy with no apparent overarching value.

So, do we as Ugandans need all these hi-tech intrusive surveillance systems to prevent crime? Well, someones misguided and ill-informed contention despite reading this article thus far may still assume that we do. Numerous evidence suggests that it is through communities, and not video technological surveillance that crimes can be reduced or even prevented. Such can be achieved through the creation and reinforcement of social communal bonds that enhance social cohesion and produce social capital to tackle crime. How will a system of digital car monitoring and tracking solve serious crimes such as corruption, land grabbing, defilement, murder and an array of cybercrimes Ugandans are currently facing? Instead, they can pose serious cyber (national) security issues if the system is not fully secured. Video surveillance has limited utility. Firstly, it can induce fear in the mind of a criminally motivated offender but does not in itself prevent him from committing a crime like the CCTV crusaders would like us to believe. If so was the case, there would be no crime in London and New York or other cities littered with surveillance cameras. Secondly, it can be used for evidentiary purposes in court. But how many have been adduced in court as evidence to support the prosecution of crimes in Uganda? We need a study on this too, otherwise the overall efficacy and effectiveness of video surveillance in crime control, prevention, and reduction should not be glorified and overstated beyond their capacity. To believe that digital number plates will meaningfully solve crime is utterly unfounded and grossly mistaken.

ByDaniel Adyera

Director, Centre for Criminology and Criminal Justice Policyemail: [emailprotected]

Continued here:

Why Uganda does not need car digital monitoring and tracking technologies to fight crime - Daily Monitor

Read More..

Basic Concepts in Machine Learning

Last Updated on August 15, 2020

What are the basic concepts in machine learning?

I found that the best way to discover and get a handle on the basic concepts in machine learning is to review the introduction chapters tomachine learning textbooks and to watch the videos from the first model inonlinecourses.

Pedro Domingos is a lecturer and professor on machine learning at the University of Washing and author of a new book titled The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.

Domingos has a free course on machine learning online at courser titled appropriately Machine Learning. The videos for each module can be previewedon Coursera any time.

In this post you will discover the basic concepts of machine learning summarized from Week One of Domingos Machine Learning course.

Basic Concepts in Machine LearningPhoto by Travis Wise, some rights reserved.

The first half of the lecture is on the general topic of machine learning.

Why do we need to care about machine learning?

A breakthrough in machine learning would be worth ten Microsofts.

Bill Gates, Former Chairman, Microsoft

Machine Learning is getting computers to program themselves. If programming is automation, then machine learning is automating the process of automation.

Writing software is the bottleneck, we dont have enough good developers. Let the data do the work instead of people. Machine learning is the way to make programming scalable.

Machine learning is like farming or gardening. Seeds is the algorithms, nutrientsis the data, thegardneris you and plants is the programs.

Traditional Programming vs Machine Learning

Sample applications of machine learning:

What is your domain of interest and how could you use machine learning in that domain?

There are tens of thousands of machine learning algorithms and hundreds of new algorithms are developed every year.

Every machine learning algorithm has three components:

All machine learning algorithms are combinations of these three components. A framework for understanding all algorithms.

There are four types of machine learning:

Supervised learning is the most mature, the most studied and the type of learning used bymost machine learning algorithms. Learning with supervision is much easier than learning without supervision.

Inductive Learning is where we are given examples of a function in the form of data (x) and the output of the function (f(x)). The goal of inductive learning is to learn the function for new data (x).

Machine learning algorithms are only a very small part of using machine learning in practice as a data analyst or data scientist. In practice, the process often looks like:

It is not a one-shot process, it is a cycle. You need to run the loop until you get a result that you can use in practice. Also, the data can change, requiring a new loop.

The second part of the lecture is on the topic of inductive learning. This is the general theory behind supervised learning.

From the perspective of inductive learning, we are given input samples (x) and output samples (f(x)) and the problem is to estimate the function (f). Specifically, the problem is to generalize from the samples and the mapping to be useful to estimate the output fornew samples in the future.

In practice it is almost always too hard to estimate the function, so we are looking for very good approximations of the function.

Some practical examples of induction are:

There are problems where inductive learning is not a good idea. It is important when to use and when not to use supervised machine learning.

4 problems where inductive learning might be a good idea:

We can write a program that works perfectly for the data that we have. This function will be maximally overfit. But we have no idea how well it will work on new data, it will likely be very badly because we may never see the same examples again.

The data is not enough. You canpredictanything you like. And this would be naive assume nothing about the problem.

In practice we are not naive. There is an underlying problem and we areinterested inan accurate approximation of the function. There is a double exponential number of possible classifiers in the number of input states. Finding a good approximate for the function is verydifficult.

There are classes of hypotheses that we can try. That is the form that the solution may take or the representation. We cannot know which is most suitable for our problem before hand. We have to use experimentation to discover what works on the problem.

Two perspectives on inductive learning:

You could be wrong.

In practice we start with a small hypothesis class and slowly grow the hypothesis class until we get a good result.

Terminology used inmachine learning:

Key issues in machine learning:

There are 3concerns for a choosing a hypothesis spacespace:

There are 3properties by which you could choose an algorithm:

In this post you discovered the basic concepts in machine learning.

In summary, these were:

These are the basic concepts that are covered in the introduction to most machine learning courses and in the opening chapters of any good textbook on the topic.

Although targeted at academics,as a practitioner, it is useful to have a firm footingin these concepts in order to better understand how machine learning algorithmsbehave in the general sense.

The rest is here:
Basic Concepts in Machine Learning

Read More..

Machine Learning Helps Clarify the Risk Connected to Age-Related Blood Condition – On Cancer – Memorial Sloan Kettering

Artificial intelligence (AI) and machine learning allow researchers to study databases that otherwise would be too large and complex. In a recent study, Sloan Kettering Institute computational biologist Quaid Morris and collaborators used models to study an aging-related blood condition called clonal hematopoiesis (CH).

Their research showed how evolution and natural selection influence CH and the effects that it may have on health outcomes. CH is relatively common in older people, affecting up to 10% of the population by age 80. The condition raises the risk of developing blood disorders including some blood cancers and cardiovascular disease.

One of the issues that we face in studying something complicated like CH is the interplay of many different factors, says Dr. Morris, who is co-senior author of a paper on CH published August 13, 2021, in Nature Communications. AI could eventually give us the tools to guide clinical decisions.

Hematopoietic stem cells are cells that will eventually develop into different types of blood cells. In people with CH, some of these hematopoietic stem cells instead form a group of cells that is genetically distinct from the rest of their counterparts.Some of these subsets of cells, or clones, may contain mutations linked to cancer. This process can eventually lead to problems.

The presence of these mutations in the blood doesnt mean that the person carrying them has or will definitely develop cancer, but studies have shown that people with CH are at higher risk of developing certain blood cancers, especiallymyelodysplastic syndromeandacute myeloid leukemia (AML). They are also at increased risk for cardiovascular disease, heart attacks, and strokes.

In January 2018, Memorial Sloan Kettering Cancer Center launched a clinic for cancer patients found to have CH. The clinic provides these patients with regular monitoring for signs of blood cancer and regular screening for cardiovascular disease risk. Early detection of cancer or heart disease allows doctors to step in right away with a treatment plan. The clinic also has an important forward-looking research component: trying to understand which patients with CH are at highest risk of future health problems.

In the current study, the researchers looked at how different CH-related mutations interact with each other to increase or decrease the chances that a cancer-causing clone will eventually rise to dominance and progress become to cancer.

This type of research requires complex statistical models, says Dr. Morris, a member of the Computational and Systems Biology Program. Deep learning and neural network techniques are AI methods that can help us to make inferences about whats going on in this population of hematopoietic cells and study the interplay of different subsets of cells.

The hope is that AI can help us make sense of patterns that are so complex that we'd never be able to see them on our own.

The researchers used blood samples collected as part of the European Prospective Investigation into Cancer and Nutrition, anongoing, multicenter study that has medical information on about 65,000 people spanning almost three decades. The analysis of blood samples with CH included 92 samples from people who eventually developed AML and 385 controls (people who did not have AML).

This research was done in collaboration with scientists at the Ontario Institute for Cancer Research (OICR) and the University of Toronto, where Dr. Morris worked before coming to MSK. The co-senior author, Philip Awadalla of OICR, is an expert in population genetics, a field that focuses on how genes change in response to evolution and natural selection.

Dr. Morris says data collected through MSKs CH clinic will make this kind of analysis much more precise and potentially more useful going forward. The data we used in the current study was retrospective and taken from a single snapshot in time, he explains. In contrast, he notes, the CH clinic is collecting multiple samples from the same patients over months or years. This means that models we build with this data will be more informed and more effective at studying patterns over time and help us to make better predictions, he adds.

CH research is an important component of calculating and understanding cancer risk, a major goal of MSKs Precision Interception and Prevention Program. The objective of this approach is to either prevent cancer from occurring or stop it at the earliest stages, when its easier to treat.

The hope is that AI can help us make sense of patterns that are so complex that wed never be able to see them on our own, Dr. Morris says.

Read the original post:
Machine Learning Helps Clarify the Risk Connected to Age-Related Blood Condition - On Cancer - Memorial Sloan Kettering

Read More..

Industry VoicesWhy the COVID-19 pandemic was a watershed moment for machine learning – FierceHealthcare

Times of crisis spark innovation and creativity, as evidenced in the way organizations have come together to innovate for the greater good during the COVID-19 pandemic.

Liquor distilleries started producing hand sanitizer, 3D printing companies made face shields and nasal swabs to meet massive demandsand auto companies shifted gears to make ventilators.

Machine learning (ML)computer systems that learn and adapt autonomously by using algorithms and statistical models to analyze and draw inferences from patterns in data to inform and automate processeshas also played an important role, supporting practically every aspect of healthcare. Amazon Web Services has supported customers as they enable remote patient care, develop predictive surge planning to help manage inpatient/ICU bed capacityand tackle the unprecedented feat of developing an messenger ribonucleic acid (mRNA)-based COVID-19 vaccine in under a year.

We now have the opportunity to build on our lessons from the past year to apply ML to help address several underlying problems that plague the healthcare and life sciences communities.

Telehealth was on the rise before COVID-19, but it revealed its true potential during the pandemic. Telehealth is often viewed simply as patients and providers interacting online via video platforms but has proven capable of doing much more. Applying ML to telehealth provides a unique opportunity to innovate, scale and offer more personalized experiences for patients and ensure they have access to the resources and care they need, no matter where they're located.

ML-based telehealth tools such as patient service chatbots, call center interactions to better triage and direct patients to the information and care they requireand online self-service prescreenings are helping optimize patient experiences and streamline provider assessments and diagnostics.

RELATED:Global investment in telehealth, artificial intelligence hits a new high in Q1 2021

For example, GovChat, South Africa's largest citizen engagement platform, launched a COVID-19 chatbot in less than two weeks using an artificial intelligence (AI) service for building conversational interfaces into any application using voice and text. The chatbot provides health advice and recommendations on whether to get a test for COVID-19, information on the nearest COVID-19 testing facility, the ability to receive test resultsand the option for citizens to report COVID-19 symptoms for themselves, their family membersor other household members.

In addition, early in the COVID-19 crisis, New York City-based MetroPlusHealth identified approximately 85,000 at-risk individuals (e.g., comorbid heart or lung disease, or immunocompromised) who would require additional support services while sheltering in place. In order to engage and address the needs of this high-risk population, MetroPlusHealth developed ML-enabled solutions including an SMS-based chatbot that guides people through self-screening and registration processes, SMS notification campaigns to provide alerts and updated pandemic informationand a community-based organizations referral platform, called Now Pow, to connect each individual with the right resource to ensure their specific needs were met.

By providing an easy way for patients to access the care, recommendationsand support they need, ML has given providers the ability to innovate and scale their telehealth platforms to support diverse and continuously changing community needs. Agile, scalableand accessible telehealth continues to be important as providers look for ways to reach and engage patients in hard-to-reach or rural areas and those with mobility issues. Organizations and policymakers globally need to make telehealth and easy access to care a priority now and going forward in order to close critical gaps in care.

Beyond the unprecedented shifts in the approach to engaging, supporting and treating patients, COVID-19 has dictated clear direction for the future of patient care: precision medicine.

Guidelines for patient care planning care have shifted from statistically significant outcomes gathered from a general population to outcomes based on the individual. This gives clinicians the ability to understand what type of patient is most prone to have a disease, not just what sort of disease a specific patient has. Being able to predict the probability of contracting a disease far in advance of its onset is important to determining and initiating preventative, intervening, and corrective measures that can be tailored to each individual's characteristics.

RELATED:What's on the horizon for healthcare beyond COVID-19? Cerner, Epic and Meditech executives share their takes

One of the best examples of how ML is enabling precision medicine is biotech company Modernas ability to accelerate every step of the process in developing an mRNA vaccine for COVID-19. Moderna began work on its vaccine the moment the novel coronaviruss genetic sequence was published. Within days, the company had finalized the sequence for its mRNA vaccine in partnership with the National Institutes of Health.

Moderna was able to begin manufacturing the first clinical-grade batch of the vaccine within two months of completing the sequencinga process that historically has taken up to 10 years.

Personalized health isn't only about treating disease, it's about providing access to resources and information specific to a patient's needs. ML is playing a key role in curating content that can help to educate and support patients, caregivers and their families.

Breastcancer.org allows individuals with breast cancer to upload their pathology report to a private and secure personal account. The organization uses ML-based natural language processing to analyze and understand the report and create personalized information for the patient based on their specific pathology.

RELATED:Healthcare AI investment will shift to these 5 areas in the next 2 years: survey

For the last decade, organizations have focused on digitizing healthcare. Today, making sense of the data being captured will provide the biggest opportunity to transform care. Successful transformation will depend on enabling data to flow where it needs to be at the right time while ensuring that all data exchange is secure.

Interoperability is by far one of the most important topics in this discussion. Today, most healthcare data is stored in disparate formats (e.g., medical histories, physician notes and medical imaging reports), which makes extracting information challenging. ML models trained to support healthcare and life sciences organizations help solve this problem by automatically normalizing, indexing, structuring and analyzing data.

ML has the potential to bring data together in a way that creates a more complete view of a patient's medical history, making it easier for providers to understand relationships in the data and compare specific data to the rest of the population. Better data management and analysis leads to better insights, which lead to smarter decisions. The net result is increased operational efficiency for improved care delivery and management, and most importantly, improved patient experiences and health outcomes.

Looking ahead, imagine a time when our pernicious medical conditions like cancer and diabetes can be treated with tailored medicines and care plans enabled by AI and ML. The pandemic was a turning point for how ML can be applied to tackle some of the toughest challenges in the healthcare industry, though we've only just scratched the surface of what it can accomplish.

Taha Kass-Hout is the director of machine learning for Amazon Web Services.

See more here:
Industry VoicesWhy the COVID-19 pandemic was a watershed moment for machine learning - FierceHealthcare

Read More..

How to upskill your team to tackle AI and machine learning – VentureBeat

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!

Women in the AI field are making research breakthroughs, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to emphasize the importance of their voices, work, and experience and to shine a light on some of these leaders. In this series, publishing Fridays, were diving deeper into conversations with this years winners, whom we honored recently at Transform 2021. Check out last weeks interview with a winner of our AI rising star award.

No one got more nominations for a VentureBeat AI award this year than Katia Walsh, a reflection of her career-long effort to mentor women in AI and data science across the globe.

For example, Mark Minevich, chair of AI Policy at International Research Center of AI under UNESCO, said, Katia is an impressive, values-driven leader [who has] been a diversity champion and mentor of women, LGBTQ, and youth at Levi Strauss & Co, Vodafone, Prudential, Fidelity, Forrester, and in academia over many years. And Inna Saboshchuk, a current colleague of Walshs at Levi Strauss & Co, said, a single conversation with her will show you how much she cares for the people around her, especially young professionals within AI.

In particular, these nominators and many others highlighted Walshs efforts to upskill team members. Most recently, she launched a machine learning bootcamp that allowed people with no prior experience to not only learn the skills, but apply them every day in their current roles.

VentureBeat is thrilled to present Walsh with this much-deserved AI mentorship award. We recently caught up with her to learn more about the early success of her latest bootcamp, the power of everyday mentorship, and the role it can play in humanizing AI.

This interview has been edited for brevity and clarity.

VentureBeat: You received a ton of nominations for this award, so clearly youre making a real impact. How would you describe your approach to AI mentorship?

Katia Walsh: My approach is not specific to AI mentorship, but rather overall leadership. I consider myself to be a servant leader, and I see my job as serving the people on my teams, my partners teams, and at the companies that I have the privilege to work for. My job is to remove barriers to help them grow, learn, engage, and mobilize others to succeed. So that extends to AI, but its not limited to that alone.

VentureBeat: Can you tell us about some of the specific initiatives youve launched? I know at Levi Strauss & Co, for example, you recently created a machine learning bootcamp to train more than 100 employees who had no prior machine learning experience, most of them women. Thats amazing.

Walsh: Absolutely. So we are still in the process. We just started our first cohort between April and May, where we took people with absolutely no experience in coding or statistics from all areas of the company including warehouses, distribution centers, and retail stores and sought to make sure we gave people across geographies and across the company the opportunity to learn machine learning and practice that in their day job, regardless of what that day job was.

So we trained the first cohort with 43 people, 63% of whom were women in 14 different locations around the world. And thats very important because diversity comes in so many different ways, including cultural and geographic diversity. And so that was very successful; every single one of those employees completed the bootcamp. And now were about to start our second cohort with 60 people, which will start in September and complete in November.

VentureBeat: Im glad you mentioned those different aspects of diversity, because the industry is full of conversations around diversity, inclusion efforts, and ethical AI some of them more genuine than others. So how does AI mentorship ladder up to all that?

Walsh: I see it as just yet another platform to make an impact. AI is such an exciting field, but it can also be seen as intimidating. Some people dont know if its technology or business, but the answer is both. In fact, AI is actually part of our personal lives as well. One of my goals is to humanize the field of AI so that everyone understands the benefits and feels the freedom and the power to contribute to it. And by feeling that, they will in turn help make it even more diverse. At the end of the day at this point, at least AI is the product of human beings, with all of human beings mindsets, capabilities, and limitations. And so, its also imperative to ensure that when we create algorithms, use data, and deliver digital products, we do our very best to really reflect the world we live in.

VentureBeat: We talked about initiatives, but of course mentorship is also about those everyday mentorship-like interactions, such as with ones manager or an industry connection. How important are these not just for personal development, but also running a business and being part of a team?

Walsh: Thats actually probably the most important stage. Our daily lives revolve around what might be considered the mundane meetings, tasks, assignments, deadlines and thats actually where we can make the most impact. Mentorship is really not about doing something special and extra, but rather making sure that as part of our daily lives and daily responsibilities and jobs, we ensure we think about if were being equitable, fair, and doing everything we can to bring diversity. But it cant be a box to check; it has to become a part of how we think and act every hour in every single day.

VentureBeat: Are there any misconceptions about mentorship you think are important to clear up, or often overlooked aspects of mentorship you think everyone should know about?

Walsh: One thing that comes to mind is this idea that women can only be mentored by other women. Thats actually not the case. And in my own experience, Ive had the great privilege of working with men who have themselves taken the chance on me, given me opportunities, and given me responsibilities even before I felt ready. And I really appreciate that. So everyone can be a mentor to women and all genders including fluid genders regardless of their own gender, job, or role.

VentureBeat: And do you have any advice for everyone, but especially business leaders, about how they can be better mentors? Or what about advice for people looking to be mentored about how to make the most out of those relationships and everyday interactions?

Walsh: Ill address the mentee question first. Ive really been impressed with people who, even at a very young age, have had the courage, incentive, and initiative to reach out and say, I want to learn from you. Can you spend a few minutes with me? I always take the call. So I really encourage people to feel that strength and to take that initiative to reach out to people they think they can learn from. And I encourage those who are mentors to also take that call and to proactively encourage others to stay connected with them. One of the things I did was actually give my cell phone number to everyone in my company. Its not commonly done, but Ive put it in our own town hall chat because I want people to feel that connection. I dont want anyone to feel intimidated by a title or where someone sits in a company. AI, data, and digital are truly transversal. Theyre horizontal and cut across everything in a company. So its part of what I do in my function, but its also part of really wanting to contribute to diversity and mentorship.

Read more:
How to upskill your team to tackle AI and machine learning - VentureBeat

Read More..