Collaboration will enable AI-powered decentralized clinical trials

BOSTON, May 10, 2022 /PRNewswire/ -- Beacon Biosignals, which applies AI to EEG to unlock precision medicine for brain conditions,today announced a partnership with Stratus, the nation's leading provider of EEG services, to enable expanded clinical trial service capabilities by leveraging Beacon's machine learning neuroanalytics platform.

EEG is standard of care in the clinical diagnosis and management of many neurologic diseases and sleep disorders, yet features of clinical significance often are difficult to extract from EEG data. Broader adoption of EEG technology has been further limited by labor-intensive workflows and variability in clinician expert interpretation. By linking their platforms, Beacon and Stratus will unlock AI-powered at-home clinical trials, addressing these challenges head-on.

"The benefits of widely incorporating EEG data into pharmaceutical trials has been desired for years, but the challenge of uniformly capturing and interpreting the data has been an issue," said Charlie Alvarez, chief executive officer for Stratus. "Stratus helps solve data capture issues by providing accessible, nationwide testing services that reduce the variability in data collection and help ensure high-quality data across all sites. Stratus is proud to partner with Beacon and its ability to complete the equation by providing algorithms to ensure the quality of EEG interpretations."

Stratus offers a wide variety of EEG services, including monitored long-term video studies and routine EEGs conducted in the hospital, clinic, and in patients' homes. Stratus has a strong track record of high-quality data acquisition, enabled by an industry-leading pool of registered EEG technologists and a national footprint for EEG deployment logistics. The announced agreement establishes Stratus as a preferred data acquisition partner for Beacon's clinical trial and neurobiomarker discovery efforts using Beacon's analytics platform.

"Reliable and replicable quantitative endpoints help drive faster, better-powered trials," said Jacob Donoghue, MD, PhD, co-founder of Beacon Biosignals. "A barrier to their development, along with performing the necessary analysis, can often be the acquisition of quality EEG at scale. Partnering with Stratus and benefiting from its infrastructure and platform eliminates that hurdle and paves the way toward addressing the unmet need for endpoints, safety tools and computational diagnostics."

Beacon's platform provides an architectural foundation for discovery of robust quantitative neurobiomarkers that subsequently can be deployed for patient stratification or automated safety or efficacy monitoring in clinical trials. The powerful and validated algorithms developed by Beacon's machine learning teams can replicate the consensus interpretation of multiple trained epileptologists while exceeding human capabilities over many hours or days of recording. These algorithms can be focused on therapeutic areas such as neurodegenerative disorders, epilepsy, sleep disorders and mental illness.For example, Beacon is currently assessing novel EEG signatures in Alzheimer's disease patients to identify which patients may or may not benefit from a specific type of therapy.

"This collaboration will enable at-home studies for diseases like Alzheimer's," Donoghue said. "It has traditionally been difficult to obtain clinical-grade EEG for these patients at the scale required for phase 3 and phase 4 clinical trials. Stratus' extensive expertise in scaling EEG operations in at-home settings unlocks real opportunities to harness brain data to evaluate treatment efficacy."

About Beacon BiosignalsBeacon's machine learning platform for EEG enables and accelerates new treatments that transform the lives of patients with neurological, psychiatric or sleep disorders. Through novel machine learning algorithms, large clinical datasets, and advances in software engineering, Beacon Biosignals empowers biopharma companies with unparalleled tools for efficacy monitoring, patient stratification, and clinical trial endpoints from brain data. For more information, visit https://beacon.bio/. For careers, visit https://beacon.bio/careers; for partnership inquiries, visit https://beacon.bio/contact. Follow us on Twitter (@Biosignals) or LinkedIn (https://www.linkedin.com/company/beacon-biosignals).

About StratusStratus is the nation's leading provider of EEG solutions, including ambulatory in-home video EEG. The company has served more than 80,000 patients across the U.S. Stratus offers technology, services, and proprietary software solutions to help neurologists accurately and quickly diagnose their patients with epilepsy and other seizure-like disorders. Stratus also provides mobile cardiac telemetry to support the diagnostic testing needs of the neurology community. To learn more, visit http://www.stratusneuro.com.

MEDIA CONTACTMegan MoriartyAmendola Communications for Beacon Biosignals913.515.7530[emailprotected]

SOURCE Beacon Biosignals

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Beacon Biosignals announces partnership with Stratus to advance at-home brain monitoring and machine learning-enabled neurodiagnostics - PR Newswire

2022 MAY 09 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Research findings on artificial intelligence are discussed in a new report. According to news reporting out of the University of California Irvine by NewsRx editors, research stated, This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS).

Financial supporters for this research include Casualty Actuarial Society Award: NA.

Our news correspondents obtained a quote from the research from University of California Irvine: These datasets include bodily injury (BI), property damage (PD) and collision (COLL) coverage, each documenting policy characteristics and claims across a four-year period. The datasets are explored, including summaries of all variables, then the methods for modeling are set forth. Models are tuned and the tuning results are displayed, after which we train the final models and seek to explain select predictions. Data were provided by a private insurance carrier to the CAS after anonymizing the dataset. These data are available to actuarial researchers for well-defined research projects that have universal benefit to the insurance industry and the public.

According to the news reporters, the research concluded: Our hope is that the methods demonstrated here can be a good foundation for future ratemaking models to be developed and tested more efficiently.

For more information on this research see: Machine Learning in Ratemaking, an Application in Commercial Auto Insurance. Risks, 2022,10(80):80. (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks10040080.

Our news editors report that more information may be obtained by contacting Spencer Matthews, Department of Statistics, Donald Bren School of Information and Computer Science, University of California Irvine, Irvine, CA 92697, USA. Additional authors for this research include Brian Hartman.

(Our reports deliver fact-based news of research and discoveries from around the world.)

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Researchers From University Of California Irvine Publish Research In Machine Learning (Machine Learning In Ratemaking, An Application In Commercial...

Overfitting is a basic problem in supervised machine learning where the model shows well generalisation capabilities on seen data but poorly performs on unseen data. Overfitting occurs as a result of the existence of noise, the small size of the training set, and the complexity involved in algorithms. In this article, we will be discussing different strategies to overcome the overfitting of machine learners while at the training stage. Following are the topics to be covered.

Lets start with the overview of overfitting in the machine learning model.

Model is overfitting data when it memorises all the specific details of the training data and fails to generalise. It is a statistical error caused by poor statistical judgments. Because it is too closely tied to the data set, it adds bias to the model. Overfitting limits the models relevance to its data set and renders it irrelevant to other data sets.

Definition according to statistics

In the presence of a hypothesis space, a hypothesis is said to overfit the training data if there exists some alternative hypothesis with a smaller error than the hypothesis over the training examples, but the alternative hypothesis with a smaller overall error than the entire distribution of instances.

Are you looking for a complete repository of Python libraries used in data science,check out here.

Detecting overfitting is almost impossible before you test the data. During the training, there are two errors: training error and validation error when the training is constantly decreasing but the validation error decreases for a period and then starts to increase but meanwhile the training error is still decreasing. This kind of scenario is overfitting.

Lets understand the mitigation strategies for this statistical problem.

There are different stages in a machine learning project where different mitigation techniques could be applied to mitigate the overfitting.

High dimensional data lead to model overfitting because in these data the number of observations is much less than the number of features. This will result in indeterministic answers to the problem.

Ways to mitigate

During the process of data wrangling, one can face the problem of outliers in the data. As these outliers increase the variance in the dataset and due to this the model will train itself to these outliers and will result in an output which has high variance and low bias. Hence the bias-variance tradeoff is disturbed.

Ways to mitigate

They either require particular attention or should be utterly ignored, depending on the circumstances. If the data set contains a significant number of outliers, it is critical to utilise a modelling approach that is resistant to outliers or to filter out the outliers.

Cross-validation is a resampling technique used to assess machine learning models on a small sample of data. Cross-validation is primarily used in applied machine learning to estimate a machine learning models skill on unseen data. That is, to use a small sample to assess how the model will perform in general when used to generate predictions on data that was not utilised during the models training.

Evaluation Procedure using K-fold cross-validation

The above is the process of K fold when k is 5 this is known as 5 folds.

This method is used to prevent the learning speed slow-down problem. Because of noise learning, the accuracy of algorithms stops improving beyond a certain point or even worsens.

The green line represents the training error, and the red line represents the validation error, as illustrated in the picture, where the horizontal axis is an epoch and the vertical axis is an error. If the model continues to learn after the point, the validation error will rise while the training error will fall. So the goal is to pinpoint the precise time at which to discontinue training. As a result, we achieved an ideal fit between under-fitting and overfitting.

Way to achieve the ideal fit

To compute the accuracy after each epoch and stop training when the accuracy of test data stops improving, and then use the validation set to figure out a perfect set of values for the hyper-parameters, and then use the test set to complete the final accuracy evaluation. When compared to directly using test data to determine hyper-parameter values, this method ensures a better level of generality. This method assures that, at each stage of an iterative algorithm, bias is reduced while variance is increased.

Noise reduction, naturally, becomes one study path for overfitting inhibition. Pruning is recommended to lower the size of final classifiers in relational learning, particularly in decision tree learning, based on this concept. Pruning is an important principle that is used to minimise classification complexity by removing less useful or irrelevant data, and then to prevent overfitting and increase classification accuracy. There are two types of pruning.

In many circumstances, the amount and quality of training datasets may have a considerable impact on machine learning performance, particularly in the domain of supervised learning. The model requires enough data for learning to modify parameters. The sample count is proportional to the number of parameters.

In other words, an extended dataset can significantly enhance prediction accuracy, particularly in complex models. Existing data can be changed to produce new data. In summary, there are four basic techniques for increasing the training set.

When creating a predictive model, feature selection is the process of minimising the number of input variables. It is preferable to limit the number of input variables to lower the computational cost of modelling and, in some situations, to increase the models performance.

The following are some prominent feature selection strategies in machine learning:

Regularisation is a strategy for preventing our network from learning an overly complicated model and hence overfitting. The model grows more sophisticated as the number of features rises.

An overfitting model takes all characteristics into account, even if some of them have a negligible influence on the final result. Worse, some of them are simply noise that has no bearing on the output. There are two types of strategies to restrict these cases:

In other words, the impact of such ineffective characteristics must be restricted. However, there is uncertainty in the unnecessary characteristics, so minimise them altogether by reducing the models cost function. To do this, include a penalty word called regularizer into the cost function. There are three popular regularisation techniques.

Instead of discarding those less valuable qualities, it assigns lower weights to them. As a result, it can gather as much information as possible. Large weights can only be assigned to attributes that improve the baseline cost function significantly.

Hyperparameters are selection or configuration points that allow a machine learning model to be tailored to a given task or dataset. To optimise them is known as hyperparameter tuning. These characteristics cannot be learnt directly from the standard training procedure.

They are generally resolved before the start of the training procedure. These parameters indicate crucial model aspects such as the models complexity or how quickly it should learn. Models can contain a large number of hyperparameters, and determining the optimal combination of parameters can be thought of as a search issue.

GridSearchCV and RandomizedSearchCV are the two most effective Hyperparameter tuning algorithms.

GridSearchCV

In the GridSearchCV technique, a search space is defined as a grid of hyperparameter values, and each point in the grid is evaluated.

GridSearchCV has the disadvantage of going through all intermediate combinations of hyperparameters, which makes grid search computationally highly costly.

Random Search CV

The Random Search CV technique defines a search space as a bounded domain of hyperparameter values that are randomly sampled. This method eliminates needless calculation.

Image source

Overfitting is a general problem in supervised machine learning that cannot be avoided entirely. It occurs as a result of either the limitations of training data, which might be restricted in size or comprise a large amount of data, or noises, or the restrictions of algorithms that are too sophisticated and need an excessive number of parameters. With this article, we could understand the concept of overfitting in machine learning and the ways it could be mitigated at different stages of the machine learning project.

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Steps to perform when your machine learning model overfits in training - Analytics India Magazine

Baseten formally launched with its product that makes going from machine learning model to production-grade applications fast and easy by giving data science and machine learning teams the ability to incorporate machine learning into business processes without backend, frontend or MLOps knowledge. The product has been in private beta since last summer with well-known brands that have used it for everything from abuse detection to fraud prevention.It is in public beta at this time.

Its clear that the performance and capabilities of machine learning models are no longer the limiting factor to widespread machine learning adoption instead, practitioners are struggling to integrate their models with real world business processes because of the enormous engineering effort required to do so. With Baseten, were reducing this burden and accelerating time to value by productizing the various skills needed to bring models to the real world, said Tuhin Srivastava, co-founder and CEO of Baseten.

Over the last decade, theres been enormous progress in advancing the capabilities of machine learning, driven primarily by new model architectures and the ever-decreasing cost of compute. But the critical step of integrating models with real-world business processes is still a lengthy, expensive process that prevents the majority of businesses from seeing a return on machine learning investments. While a typical machine learning model may take just a few weeks to train, building the infrastructure, APIs and UI so that the model can be used by businesses can take more than six months and requires additional resources in the form of MLOps, backend and frontend engineers.

This is a problem that Basetens co-founders Tuhin Srivastava (CEO), Amir Haghighat (CTO) and Philip Howes (Chief Scientist) encountered first hand at Gumroad. There Haghighat was the head of engineering and Srivastava and Howes were both data scientists who had to learn to become full-stack engineers so they could use machine learning to detect fraud and moderate content. The systems they built at Gumroad are still in use and have screened hundreds of millions of dollars of transactions to date.

The trio founded Baseten so that data scientists dont have to learn to become full-stack engineers in order to build web applications for their machine learning models. Baseten lowers the barrier to usable machine learning by enabling data science and machine learning teams to incorporate their machine learning models into production-grade applications within hours instead of months. With Baseten, data science and machine learning teams can easily serve their models, build backends and frontends and ship applications that solve critical business problems including operations optimization, content moderation, fraud detection and lead scoring.

Customers on Baseten:

Analysts on Baseten:

Baseten Raises $20 Million in Seed and Series A Funding

Baseten also announced that it has raised $8 million in seed funding co-led by Greylock and South Park Commons Fund and $12 million in Series A funding led by Greylock. Baseten is using the funding to expand its engineering and go-to-market teams.

Greylock General Partner and Baseten Board Member Sarah Guo said: Despite the broad understanding that AI has the capability to revolutionize business, most organizations struggle to drive real ROI from theirmachine learning efforts, stymied by the high upfront investment required. Baseten radically reduces the time, specialized expertise, cost and cross-team coordination required to successfully ship machine learning apps to production. Its end-to-end platform frees data science and machine learning teams from grunt work and empowers them to spend more time innovating and iterating to maximize impact. The Baseten team has experienced this pain first-hand, and that authenticity and care shows in the solution theyve designed. Were thrilled to partner with them to democratize access to the revolution in machine learning.

Other participants in the seed round include AI Fund, Caffeinated Capital and angel investors Lachy Groom (ex-Stripe), Greg Brockman (co-founder and CTO of OpenAI), Dylan Field (co-founder and CEO of Figma), Mustafa Suleyman (co-founder of DeepMind) and DJ Patil (ex-Chief Data Scientist of the United States Office of Science and Technology Policy).

Other participants in the A round include South Park Commons and angel investors Lachy Groom, Cristina Cordova (ex-Stripe), Dev Ittycheria (CEO of MongoDB), Jay Simon (ex-President of Atlassian) and Jean-Denis Greze (CTO of Plaid).

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In every election, after the polls close and the votes are counted, there comes a time for reflection. Pundits appear on cable news to offer theories, columnists pen op-eds with warnings and advice for the winners and losers, and parties conduct postmortems.

The 2020 U.S. presidential election in which Donald Trump lost to Joe Biden was no exception.

For Caltech undergrad Sreemanti Dey, the election offered a chance to do her own sort of reflection. Dey, an undergrad majoring in computer science, has a particular interest in using computers to better understand politics. Working with Michael Alvarez, professor of political and computational social science, Dey used machine learning and data collected during the 2020 election to find out what actually motivated people to vote for one presidential candidate over another.

In December, Dey presented her work on the topic at the fourth-annual International Conference on Applied Machine Learning and Data Analytics, which was held remotely and was recognized by the organizers as having the best paper at the conference.

We recently chatted with Dey and Alvarez, who is co-chair of the Caltech-MIT Voting Project, about their research, what machine learning can offer to political scientists, and what it is like for undergrads doing research at Caltech.

Sreemanti Dey: I think that how elections are run has become a really salient issue in the past couple of years. Politics is in the forefront of people's minds because things have gotten so, I guess, strange and chaotic recently. That, along with a lot of factors in 2020, made people care a lot more about voting. That makes me think it's really important to study how elections work and how people choose candidates in general.

Sreemanti: I've learned from Mike that a lot of social science studies are deductive in nature. So, you pick a hypothesis and then you pick the data that would best help you understand the hypothesis that you've chosen. We wanted to take a more open-ended approach and see what the data itself told us. And, of course, that's precisely what machine learning is good for.

In this particular case, it was a matter of working with a large amount of data that you can't filter through yourself without introducing a lot of bias. And that could be just you choosing to focus on the wrong issues. Machine learning and the model that we used are a good way to reduce the amount of information you're looking at without bias.

Basically it's a way of reducing high-dimensional data sets to the most important factors in the data set. So it goes through a couple steps. It first groups all the features of the data into these modules so that the features within a module are very correlated with each other, but there is not much correlation between modules. Then, since each module represents the same type of features, it reduces how many features are in each module. And then at the very end, it combines all the modules together and then takes one last pass to see if it can be reduced by anything else.

Mike: This technique was developed by Christina Ramirez (MS' 96, PhD '99), a PhD graduate of our program now at UCLA. Christina is someone who I've collaborated with quite a bit. Sreemanti and I were meeting pretty regularly with Christina and getting some advice from her along the way about this project and some others that we're thinking about.

Sreemanti: I think we got pretty much what we expected, except for what the most partisan-coded issues are. Those I found a little bit surprising. The most partisan questions turned out to be about filling the Supreme Court seats. I thought that it was interesting.

Sreemanti: It's really incredible. I find it astonishing that a person like Professor Alvarez has the time to focus so much on the undergraduates in lab. I did research in high school, and it was an extremely competitive environment trying to get attention from professors or even your mentor.

It's a really nice feature of Caltech that professors are very involved with what their undergraduates are doing. I would say it's a really incredible opportunity.

Mike: I and most of my colleagues work really hard to involve the Caltech undergraduates in a lot of the research that we do. A lot of that happens in the SURF [Summer Undergraduate Research Fellowship] program in the summers. But it also happens throughout the course of the academic year.

What's unusual a little bit here is that undergraduate students typically take on smaller projects. They typically work on things for a quarter or a summer. And while they do a good job on them, they don't usually reach the point where they produce something that's potentially publication quality.

Sreemanti started this at the beginning of her freshman year and we worked on it through her entire freshman year. That gave her the opportunity to really learn the tools, read the political science literature, read the machine learning literature, and take this to a point where at the end of the year, she had produced something that was of publication quality.

Sreemanti: It was a little bit strange, first of all, because of the time zone issue. This conference was in a completely different time zone, so I ended up waking up at 4 a.m. for it. And then I had an audio glitch halfway through that I had to fix, so I had some very typical Zoom-era problems and all that.

Mike: This is a pandemic-era story with how we were all working to cope and trying to maintain the educational experience that we want our undergraduates to have. We were all trying to make sure that they had the experience that they deserved as a Caltech undergraduate and trying to make sure they made it through the freshman year.

We have the most amazing students imaginable, and to be able to help them understand what the research experience is like is just an amazing opportunity. Working with students like Sreemanti is the sort of thing that makes being a Caltech faculty member very special. And it's a large part of the reason why people like myself like to be professors at Caltech.

Sreemanti: I think I would want to continue studying how people make their choices about candidates but maybe in a slightly different way with different data sets. Right now, from my other projects, I think I'm learning how to not rely on surveys and rely on more organic data, for example, from social media. I would be interested in trying to find a way to study their candidatepeople's candidate choice from their more organic interactions with other people.

Sreemanti's paper, titled, "Fuzzy Forests for Feature Selection in High-Dimensional Survey Data: An Application to the 2020 U.S. Presidential Election," was presented in December at the fourth-annual International Conference on Applied Machine Learning and Data Analytics," where it won the best paper award.

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Politics, Machine Learning, and Zoom Conferences in a Pandemic: A Conversation with an Undergraduate Researcher - Caltech

CONCORD, Mass., April 27, 2022 /PRNewswire/ -- Applied BioMath (www.appliedbiomath.com), the industry-leader in providing model-informed drug discovery and development (MID3) support to help accelerate and de-risk therapeutic research and development (R&D), today announced their participation at the Bio-IT World Conference and Expo occurring May 3-5, 2022 in Boston, MA.

Kas Subramanian, PhD, Executive Director of Modeling at Applied BioMath will present "Applications of Machine Learning in Preclinical Drug Discovery" within the conference track, AI for Drug Discovery and Development on Thursday, May 5, 2022 at 1:05 p.m. E.T. In this presentation, Dr. Subramanian will discuss how machine learning methods can improve efficiency in therapeutic R&D decision making. He will review case studies that demonstrate machine learning applications to target validation and lead optimization.

"Traditionally, therapeutic R&D requires experiments on many different targets, hits, leads, and candidates that are based on best guesses," said John Burke, PhD, Co-founder, President and CEO of Applied BioMath. "By utilizing artificial intelligence and machine learning, project teams can computationally work with more data to better inform experiments and develop better therapeutics."

To learn more about Applied BioMath's presence at the Bio-IT World Conference and Expo, please visit http://www.appliedbiomath.com/BioIT22.

About Applied BioMath

Founded in 2013, Applied BioMath's mission is to revolutionize drug invention. Applied BioMath applies biosimulation, including quantitative systems pharmacology, PKPD, bioinformatics, machine learning, clinical pharmacology, and software solutions to provide quantitative and predictive guidance to biotechnology and pharmaceutical companies to help accelerate and de-risk therapeutic research and development. Their approach employs proprietary algorithms and software to support groups worldwide in decision-making from early research through all phases of clinical trials. The Applied BioMath team leverages their decades of expertise in biology, mathematical modeling and analysis, high-performance computing, and industry experience to help groups better understand their therapeutic, its best-in-class parameters, competitive advantages, patients, and the best path forward into and in the clinic to increase likelihood of clinical concept and proof of mechanism, and decrease late stage attrition rates. For more information about Applied BioMath and its services and software, visitwww.appliedbiomath.com.

Applied BioMath and the Applied BioMath logo are registered trademarks of Applied BioMath, LLC.

Press Contact: Kristen Zannella ([emailprotected])

SOURCE Applied BioMath, LLC

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Applied BioMath, LLC to Present on Machine Learning in Drug Discovery at Bio-IT World Conference and Expo - PR Newswire

Machine Learning (ML) systems are complex, and this complexity increases the chances of failure as well. Knowing what may go wrong is critical for developing robust machine learning systems.

Machine Learning (ML) initiatives fail 85% of the time, according to Gartner. Worse yet, according to the research firm, this tendency will continue until the end of 2022.

There are a number of foreseeable reasons why machine learning initiatives fail, many of which may be avoided with the right knowledge and diligence. Here are some of the most common challenges that machine learning projects face, as well as ways to prevent them.

All AI/ML endeavors require data, which is needed for testing, training, and operating models. However, acquiring such data is a stumbling block because most organizational data is dispersed among on-premises and cloud data repositories, each with its own set of compliance and quality control standards, making data consolidation and analysis that much more complex.

Another stumbling block is data silos. When teams use multiple systems to store and handle data sets, data silos collections of data controlled by one team but not completely available to others can form. That might, however, be a result of a siloed organizational structure.

In reality, no one knows everything. It is critical to have at least one ML expert on the team, to be able to do the foundational work, for the successful adoption and implementation of ML in enterprise projects. Being overly confident, without the right skill, sets in the team will only add to the chances of failure.

Organizations are nearly drowning in large volumes of observational data. Thanks to developments in technology such as integrated smart devices and telematics as well as relatively inexpensive and available big data storage and a desire to incorporate more data science into business decisions. However, a high level of data availability might result in observational data dumpster diving.

Also Read: How Enterprises can Keep Machine Learning Models on Track with Crucial Guard Rails

When adopting a strong tool like machine learning, it pays to be more aware about what organizations are searching for. Businesses should take advantage of their large observational data resources to uncover potentially valuable insights, but evaluate those hypotheses through AB or multivariate testing to distinguish reality from fiction.

The ability to evaluate the overall performance of a trained model is crucial in machine learning. Its critical to assess how well the model performs when compared to both training and test data. This data will be used to choose the model to use, the hyper-parameters to utilize, and decide if the model is ready for production use.

It is vital to select the right assessment measures for the job at hand when evaluating model performance.

Machine learning has become more accessible in various ways. There are far more machine learning tools available today than there were even a few years ago, and data science knowledge has multiplied.

Having a data science team to work on an AI and ML project in isolation, on the other hand, might drive the organization down the most difficult path to success. They may come across unanticipated difficulties unless they have prior familiarity with them. Unfortunately, they can also get into the thick of a project before recognizing they are not adequately prepared.

Its imperative to make sure that domain specialists like process engineers and plant operators are not left out of the process because they are familiar with its complexity and the context of relevant data.

Check Out The NewEnterprisetalk Podcast.For more such updates follow us on Google NewsEnterprisetalk News.

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Five Machine Learning Project Pitfalls to Avoid in 2022 - EnterpriseTalk

Selected by QB3 and UCSF for R2D2 TB Networks Scale Up Your TB Diagnostic Solution Program

BELLEVUE, Wash., April 26, 2022 (GLOBE NEWSWIRE) -- AI Dynamics, an organization founded on the belief that everyone should have access to the power of artificial intelligence (AI) to change the world, has been selected for the Rapid Research in Diagnostics Development for TB Networks (R2D2 TB Network) Scale Up Your TB Diagnostic Solution Program, hosted by QB3 and the UCSF Rosenman Institute. With 1.5 million deaths reported each year, Tuberculosis (TB) is the worldwide leading cause of death from a single infectious disease agent. The goal of the program is to harness machine learning technology for triaging TB using simple and affordable tests that can be performed on easy-to-collect samples such as cough sounds.

Currently, two weeks of cough sound data is widely used to determine who requires costly confirmatory testing, which delays the initiation of the treatment. AI Dynamics will build a proof-of-concept machine learning model to triage TB patients more accurately, quickly, simply and inexpensively using cough sounds, relieving patients from paying for unnecessary molecular and culture TB tests. Due to the prevalence of TB in under-resourced and remote locations, access to affordable early detection options is necessary to prevent disease transmissions and deaths in such countries.

At the core of AI Dynamics mission is providing equal access to the power of AI to everyone and we are committed to working with like-minded companies that recognize the positive impact innovative technology can have on the world, Rajeev Dutt, Founder and CEO of AI Dynamics said. The collaboration and accessible datasets that the R2D2 TB Network provides help to facilitate life-changing diagnostics for the most vulnerable populations.

The R2D2 TB Network offers a transparent and partner-engaged process for the identification, evaluation and advancement of promising TB diagnostics by providing experts and data and facilitating rigorous clinical study evaluation. AI Dynamics will build and validate a model using cough sounds collected from sites worldwide through the R2D2 TB Network.

About AI Dynamics:

AI Dynamics aims to make artificial intelligence (AI) accessible to organizations of all sizes. The company's NeoPulse Framework is an intuitive development and management platform for AI, which enables companies to develop and implement deep neural networks and other machine learning models that can improve key performance metrics. The company's team brings decades of experience in the fields of machine learning and artificial intelligence from leading companies and research organizations. For more information, please visit aidynamics.com.

About The R2D2 TB Network:

The Rapid Research in Diagnostics Development for TB Network (R2D2 TB Network) brings together various TB experts with highly experienced clinical study sites in 10 countries. For further information, please visit their website at https://www.r2d2tbnetwork.org/.

Media Contact:

Justine GoodielUPRAISE Marketing + PR for AI Dynamicsaidynamics@upraisepr.com

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AI Dynamics Will Employ Machine Learning to Triage TB Patients More Accurately, Quickly, Simply and Inexpensively Using Cough Sound Data, Bringing...

SAN FRANCISCO--(BUSINESS WIRE)--Mperativ, the Revenue Marketing Platform that aligns marketing with sales, customer success, and finance on the cause and effect relationships between marketing activities and revenue outcomes, today announced the appointment of Nohyun Myung as Vice President of Applied Data Science, Machine Learning and AI. In this new role, Nohyun will lead the development of new Mperativ platform capabilities to help marketers realize the value of AI predictions and seamlessly connect data across the customer journey without having to build a data science practice.

Nohyun has unique and important experience in data science, analytics and AI that will be critical to the growth of the Mperativ Data Science and AI practices, said Jim McHugh, CEO and co-founder of Mperativ. He not only brings the knowledge and skill set to help accelerate the evolution of the Mperativ platform, but his involvement in the technical side of sales organizations will give us a unique perspective on how AI and forecasting can be used to help address the challenges go-to-market teams face.

Nohyun brings over 20 years of experience as a data and analytics practitioner. Prior to Mperativ he built and scaled high-functioning, multi-disciplinary teams in his roles as Vice President of Global Solution Engineering & Customer Success at OmniSci and as Vice President of Global Solution Engineering at Kinetica. He has worked closely with industry leaders across Telco, Utilities, Automotive and Government verticals to deliver enterprise-grade AI and advanced analytics capabilities to their data practices, pioneering work across autonomous vehicle deployments to telecommunications network optimization and uncovering anomalies from object-detected features of satellite imagery. Nohyuns prior experience has led to the advancement of enterprise-class AI capabilities spanning Autonomous Vehicles, automating Object Detection from optical imagery and Global-Scale Smart Infrastructure initiatives across various industries.

Throughout my career Ive become acutely familiar with the immense challenges that go-to-market teams face when trying to get a comprehensive and accurate picture of the customer journey, said Nohyun. As the world sprints towards becoming more prescriptive and predictive, having operational tools and platforms that can augment business without having to build it in-house will become essential across B2B organizations. I look forward to working with the talented team at Mperativ to bring the true value of AI to marketing leaders so they can better execute engagement strategies that produce their desired revenue outcomes.

About Mperativ

Mperativ provides the first strategic platform to align marketing with sales, customer success, and finance on the cause and effect relationships between marketing activities and revenue outcomes. Despite pouring significant effort into custom analytics, marketers are struggling to convey the value of their initiatives. By recentering marketing metrics around revenue, Mperativ makes it possible to uncover data narratives and extract trends across the entire customer journey, with beautifully-designed interactive visualizations that demonstrate the effectiveness of marketing in a new revenue-centric language. As a serverless data warehouse, Mperativ eliminates the complexity of surfacing compelling marketing insights. Connect marketing strategy to revenue results with Mperativ. To learn more, visit us at http://www.mperativ.io or contact us at info@mperativ.io.

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Mperativ Adds New Vice President of Applied Data Science, Machine Learning and AI to Advance Vision for AI in Revenue Marketing - Business Wire

CHICAGO, April 26, 2022 (GLOBE NEWSWIRE) -- VelocityEHS,the global leader in cloud-based environmental, health, safety (EHS) and environmental, social, and corporate governance (ESG) software, announced the latest additions to the Accelerate Platform, including a highly anticipated new feature,Active Causes & Controls, to its award-winning Industrial Ergonomics Solution. Rooted in ActiveEHS the proprietary VelocityEHS methodology that leverages AI & machine learning to help non-experts produce expert-level results this enhancement kicks off a new era in the prevention of musculoskeletal disorders (MSDs).

Designed, engineered, and embedded with expertise by an unmatched group of board-certified ergonomists, the ActiveEHS powered Active Causes and Controls feature helps companies reduce training time, maintain process consistency across locations, and focus on implementing changes that maximize business results. Starting with the industrys best sensorless, motion-capture technology, which performs ergonomics assessments faster, easier, and more accurately than any human could, the solution then guides users through suggested root causes and job improvement controls. Recommendations are based on AI and machine learning insights fed by data collected from hundreds of global enterprise customers and millions of MSD risk data points.

The result is an unparalleled opportunity to prevent MSD risk, reduce overall injury costs, drive productivity, and provide employees with quality-of-life changing improvements in the workplace.

These are exciting times for anyone who cares about EHS and ESG, said John Damgaard, CEO of VelocityEHS. While its true, the job of a C-suite executive or EHS professional has never been more challenging and complex; its also true that leaders have never had this kind of advanced, highly usable, and easy-to-deploy technology at their fingertips. Ergonomics is just the start; ActiveEHS will transform how we think about health, safety, and sustainability going forward. It is the key to evolving from a reactive documentation and compliance mindset to a proactive continuous improvement cycle of prediction, intervention, and outcomes.

MSDs are a major burden on workers and a huge cost to employers.According to the Bureau of Labor Statistics, for employers in the U.S. private sector alone, MDSs cause more than 300,000 days away from work and per OSHA, are responsible for $20 billioneveryyear in workers compensation claims.

Also Announced Today: New Training & Learning Content, Enhancements to Automated Utility Data Management, and Improved workflows for the Control of Work Solution.

The VelocityEHS Safety Solution, which includes robust Training & Learning capabilities, is undergoing a major expansion of its online training content library. To enable companies to meet more of their training responsibilities, the training content library is growing from approximately 100 courses to over 750. They will be available in multiple languages, including 300+ courses in Spanish. The new content will feature microlearning modules, which have gained popularity in recent years as workers prefer shorter, easily digestible training sessions. This results in less time in front of the screen for workers, while employers report better engagement and overall retention of the material.

The VelocityEHS Climate Solution continues to capitalize on the VelocityEHS partnership with Urjanet the engine behind the recently announced Automated Utility Data Management capabilities. Now, in addition to saving time and reducing costs related to the collection of utility data, users can automatically port their energy, gas and water usage data into the VelocityEHS Climate Solution to perform GHG calculations and report on Scope 1,2, and 3 emissions, without any manual effort.

The Companys Control of Work Solution boasts a new streamlined navigation and enhanced functionality that allows customers to add new, pre-approved roles for improved compliance and approval workflows.

Industrial Ergonomics, Safety, Climate, and Control of Work solutions are all part of the VelocityEHS AcceleratePlatform, which delivers best-in-class performance in the areas of health, safety, risk, ESG, and operational excellence. Backed by the largest global software community of EHS experts and thought leaders, the software drives expert processes so every team member can produce outstanding results.

For more information about VelocityEHS and its complete offering of award-winning software solutions, visit http://www.EHS.com.

AboutVelocityEHS Trusted by more than 19,000 customers worldwide, VelocityEHS is the global leader in true SaaS enterprise EHS technology. Through the VelocityEHS Accelerate Platform, the company helps global enterprises drive operational excellence by delivering best-in-class capabilities for health, safety, environmental compliance, training, operational risk, and environmental, social, and corporate governance (ESG). The VelocityEHS team includes unparalleled industry expertise, with more certified experts in health, safety, industrial hygiene, ergonomics, sustainability, the environment, AI, and machine learning than any EHS software provider. Recognized by the EHS industrys top independent analysts as a Leader in the Verdantix 2021 Green Quadrant AnalysisVelocityEHS is committed to industry thought leadership and to accelerating the pace of innovation through its software solutions and vision.

VelocityEHS is headquartered in Chicago, Illinois, with locations in Ann Arbor, Michigan; Tampa, Florida; Oakville, Ontario; London, England; Perth, Western Australia; and Cork, Ireland. For more information, visit http://www.EHS.com.

Media Contact Brad Harbaugh 312.881.2855 bharbaugh@ehs.com

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VelocityEHS Industrial Ergonomics Solution Harnesses AI and Machine Learning to Drive ... - KULR-TV