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Category Archives: Machine Learning

Mission Healthcare of San Diego Adopts Muse Healthcare’s Machine Learning Tool –

ST. PAUL, Minn., Jan. 19, 2021 /PRNewswire/ -- San Diego-based Mission Healthcare, one of the largest home health, hospice, and palliative care providers in California, will adopt Muse Healthcare's machine learning and predictive modeling tool to help deliver a more personalized level of care to their patients.

The Muse technology evaluates and models every clinical assessment, medication, vital sign, and other relevant data to perform a risk stratification of these patients. The tool then highlights the patients with the most critical needs and visually alerts the agency to perform additional care. Muse Healthcare identifies patients as "Critical," which means they have a greater than 90% likelihood of passing in the next 7-10 days. Users are also able to make accurate changes to care plans based on the condition and location of the patient. When agencies use Muse's powerful machine learning tool, they have an advantage and data proven outcomes to demonstrate they are providing more care and better care to patients in transition.

According to Mission Healthcare's Vice President of Clinical and Quality, Gerry Smith, RN, MSN, Muse will serve as an invaluable tool that will assist their clinicians to enhance care for their patients. "Mission Hospice strives to ensure every patient receives the care and comfort they need while on service, and especially in their final days. We are so excited that the Muse technology will provide our clinical team with additional insights to positively optimize care for patients at the end of life. This predictive modeling technology will enable us to intervene earlier; make better decisions for more personalized care; empower staff; and ultimately improve patient outcomes."

Mission Healthcare's CEO, Paul VerHoeve, also believes that the Muse technology will empower their staff to provide better care for patients. "Predictive analytics are a new wave in hospice innovation and Muse's technology will be a valuable asset to augment our clinical efforts at Mission Healthcare. By implementing a revolutionary machine learning tool like Muse, we can ensure our patients are receiving enhanced hands-on care in those critical last 7 10 days of life. Our mission is to take care of people, with Muse we will continue to improve the patient experience and provide better care in the final days and hours of a patient's life."

As the only machine learning tool in the hospice industry, the Muse transitions tool takes advantage of the implemented documentation within the EMR. This allows the agency to quickly implement the tool without disruption. "With guidance from our customers in the hundreds of locations that are now using the tool, we have focused on deploying time saving enhancements to simplify a clinician's role within hospice agencies. These tools allow the user to view a clinical snapshot, complete review of the scheduled frequency, and quickly identify the patients that need immediate attention. Without Muse HC, a full medical review must be conducted to identify these patients," said Tom Maxwell, co-Founder of Muse Healthcare. "We are saving clinicians time in their day, simplifying the identification challenges of hospice, and making it easier to provide better care to our patients. Hospice agencies only get one chance to get this right," said Maxwell.

CEO of Muse Healthcare, Bryan Mosher, is also excited about Mission's adoption of the Muse tool. "We welcome the Mission Healthcare team to the Muse Healthcare family of customers, and are happy to have them adopt our product so quickly. We are sure with the use of our tools,clinicians at Mission Healthcare will provide better care for their hospice patients," said Mosher.

About Mission Healthcare

As one of the largest regional home health, hospice, and palliative care providers in California, San Diego-based Mission Healthcare was founded in 2009 with the creation of its first service line, Mission Home Health. In 2011, Mission added its hospice service line. Today, Mission employs over 600 people and serves both home health and hospice patients through Southern California. In 2018, Mission was selected as a Top Workplace by the San Diego Union-Tribune. For more information visit

About Muse Healthcare

Muse Healthcare was founded in 2019 by three leading hospice industry professionals -- Jennifer Maxwell, Tom Maxwell, and Bryan Mosher. Their mission is to equip clinicians with world-class analytics to ensure every hospice patient transitions with unparalleled quality and dignity. Muse's predictive model considers hundreds of thousands of data points from numerous visits to identify which hospice patients are most likely to transition within 7-12 days. The science that powers Muse is considered a true deep learning neural network the only one of its kind in the hospice space. When hospice care providers can more accurately predict when their patients will transition, they can ensure their patients and the patients' families receive the care that matters most in the final days and hours of a patient's life. For more information visit

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Researchers Develop New Machine Learning Technique to Predict Progress of COVID-19 Patients | The Weather Channel – Articles from The Weather Channel…

An illustration of novel coronavirus SARS-CoV-2.

Researchers have published one of the first studies using a Machine Learning (ML) technique called "federated learning" to examine electronic health records to better predict how COVID-19 patients will progress.

The study, published in the Journal of Medical Internet Research - Medical Informatics, indicates that the emerging technique holds promise to create more robust machine learning models that extend beyond a single health system without compromising patient privacy.

These models, in turn, can help triage patients and improve the quality of their care. "Machine Learning models in health care often require diverse and large-scale data to be robust and translatable outside the patient population they were trained on," said co-author Benjamin Glicksberg, Assistant Professor at Mount Sinai.

Federated learning is a technique that trains an algorithm across multiple devices or servers holding local data samples but avoids clinical data aggregation, which is undesirable for reasons including patient privacy issues.

For the study, the researchers implemented and assessed federated learning models using data from electronic health records at five separate hospitals within the Health System to predict mortality in COVID-19 patients.

They compared the performance of a federated model against ones built using data from each hospital separately, referred to as local models.

After training their models on a federated network and testing the data of local models at each hospital, the researchers found the federated models demonstrated enhanced predictive power and outperformed local models at most of the hospitals.


The above article has been published from a wire agency with minimal modifications to the headline and text.

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Deep Learning Outperforms Standard Machine Learning in Biomedical Research Applications, Research Shows – Georgia State University News

ATLANTACompared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using deep learning to learn more about how mental illness and other disorders affect the brain.

Although deep learning models have been used to solve problems and answer questions in a number of different fields, some experts remain skeptical. Recent critical commentaries have unfavorably compared deep learning with standard machine learning approaches for analyzing brain imaging data.

However, as demonstrated in the study, these conclusions are often based on pre-processed input that deprive deep learning of its main advantagethe ability to learn from the data with little to no preprocessing. Anees Abrol, research scientist at TReNDS and the lead author on the paper, compared representative models from classical machine learning and deep learning, and found that if trained properly, the deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

We compared these models side-by-side, observing statistical protocols so everything is apples to apples. And we show that deep learning models perform better, as expected, said co-author Sergey Plis, director of machine learning at TReNDS and associate professor of computer science.

Plis said there are some cases where standard machine learning can outperform deep learning. For example, diagnostic algorithms that plug in single-number measurements such as a patients body temperature or whether the patient smokes cigarettes would work better using classical machine learning approaches.

If your application involves analyzing images or if it involves a large array of data that cant really be distilled into a simple measurement without losing information, deep learning can help, Plis said.. These models are made for really complex problems that require bringing in a lot of experience and intuition.

The downside of deep learning models is they are data hungry at the outset and must be trained on lots of information. But once these models are trained, said co-author Vince Calhoun, director of TReNDS and Distinguished University Professor of Psychology, they are just as effective at analyzing reams of complex data as they are at answering simple questions.

Interestingly, in our study we looked at sample sizes from 100 to 10,000 and in all cases the deep learning approaches were doing better, he said.

Another advantage is that scientists can reverse analyze deep-learning models to understand how they are reaching conclusions about the data. As the published study shows, the trained deep learning models learn to identify meaningful brain biomarkers.

These models are learning on their own, so we can uncover the defining characteristics that theyre looking into that allows them to be accurate, Abrol said. We can check the data points a model is analyzing and then compare it to the literature to see what the model has found outside of where we told it to look.

The researchers envision that deep learning models are capable of extracting explanations and representations not already known to the field and act as an aid in growing our knowledge of how the human brain functions. They conclude that although more research is needed to find and address weaknesses of deep-learning models, from a mathematical point of view, its clear these models outperform standard machine learning models in many settings.

Deep learnings promise perhaps still outweighs its current usefulness to neuroimaging, but we are seeing a lot of real potential for these techniques, Plis said.

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Project MEDAL to apply machine learning to aero innovation – The Engineer

Metallic alloys for aerospace components are expected to be made faster and more cheaply with the application of machine learning in Project MEDAL.

This is the aim of Project MEDAL: Machine Learning for Additive Manufacturing Experimental Design,which is being led by Intellegens, a Cambridge University spin-out specialising in artificial intelligence, the Sheffield University AMRC North West, and Boeing. It aims to accelerate the product development lifecycle of aerospace components by using a machine learning model to optimise additive manufacturing (AM) for new metal alloys.

How collaboration is driving advances in additive manufacturing

Project MEDALs research will concentrate on metal laser powder bed fusion and will focus on so-called parameter variables required to manufacture high density, high strength parts.

The project is part of the National Aerospace Technology Exploitation Programme (NATEP), a 10m initiative for UK SMEs to develop innovative aerospace technologies funded by the Department for Business, Energy and Industrial Strategy and delivered in partnership with the Aerospace Technology Institute (ATI) and Innovate UK.

In a statement, Ben Pellegrini, CEO of Intellegens, said: The intersection of machine learning, design of experiments and additive manufacturing holds enormous potential to rapidly develop and deploy custom parts not only in aerospace, as proven by the involvement of Boeing, but in medical, transport and consumer product applications.

There are many barriers to the adoption of metallic AM but by providing users, and maybe more importantly new users, with the tools they need to process a required material should not be one of them, added James Hughes, research director for Sheffield University AMRC North West. With the AMRCs knowledge in AM, and Intellegens AI tools, all the required experience and expertise is in place in order to deliver a rapid, data-driven software toolset for developing parameters for metallic AM processes to make them cheaper and faster.

Aerospace components must withstand certain loads and temperature resistances, and some materials are limited in what they can offer. There is also simultaneous push for lower weight and higher temperature resistance for better fuel efficiency, bringing new or previously impractical-to-machine metals into the aerospace sector.

One of the main drawbacks of AM is the limited material selection currently available and the design of new materials, particularly in the aerospace industry, requires expensive and extensive testing and certification cycles which can take longer than a year to complete and cost as much as 1m. Project MEDAL aims to accelerate this process.

The machine learning solution in this project can significantly reduce the need for many experimental cycles by around 80 per cent, Pellegrini said: The software platform will be able to suggest the most important experiments needed to optimise AM processing parameters, in order to manufacture parts that meet specific target properties. The platform will make the development process for AM metal alloys more time and cost-efficient. This will in turn accelerate the production of more lightweight and integrated aerospace components, leading to more efficient aircraft and improved environmental impact.

Project MEDAL to apply machine learning to aero innovation - The Engineer

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Machine Learning Shown to Identify Patient Response to Sarilumab in Rheumatoid Arthritis – Managed Markets Network

Machine learning was shown to identify patients with rheumatoid arthritis (RA) who present an increased chance of achieving clinical response with sarilumab, with those selected also showing an inferior response to adalimumab, according to an abstract presented at ACR Convergence, the annual meeting of the American College of Rheumatology (ACR).

In prior phase 3 trials comparing the interleukin 6 receptor (IL-6R) inhibitor sarilumab with placebo and the tumor necrosis factor (TNF-) inhibitor adalimumab, sarilumab appeared to provide superior efficacy for patients with moderate to severe RA. Although promising, the researchers of the abstract highlight that treatment of RA requires a more individualized approach to maximize efficacy and minimize risk of adverse events.

The characteristics of patients who are most likely to benefit from sarilumab treatment remain poorly understood, noted researchers.

Seeking to better identify the patients with RA who may best benefit from sarilumab treatment, the researchers applied machine learning to select from a predefined set of patient characteristics, which they hypothesized may help delineate the patients who could benefit most from either antiIL-6R or antiTNF- treatment.

Following their extraction of data from the sarilumab clinical development program, the researchers utilized a decision tree classification approach to build predictive models on ACR response criteria at week 24 in patients from the phase 3 MOBILITY trial, focusing on the 200-mg dose of sarilumab. They incorporated the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) algorithm, including 17 categorical and 25 continuous baseline variables as candidate predictors. These included protein biomarkers, disease activity scoring, and demographic data, added the researchers.

Endpoints used were ACR20, ACR50, and ACR70 at week 24, with the resulting rule validated through application on independent data sets from the following trials:

Assessing the end points used, it was found that the most successful GUIDE model was trained against the ACR20 response. From the 42 candidate predictor variables, the combined presence of anticitrullinated protein antibodies (ACPA) and C-reactive protein >12.3 mg/L was identified as a predictor of better treatment outcomes with sarilumab, with those patients identified as rule-positive.

These rule-positive patients, which ranged from 34% to 51% in the sarilumab groups across the 4 trials, were shown to have more severe disease and poorer prognostic factors at baseline. They also exhibited better outcomes than rule-negative patients for most end points assessed, except for patients with inadequate response to TNF inhibitors.

Notably, rule-positive patients had a better response to sarilumab but an inferior response to adalimumab, except for patients of the HAQ-Disability Index minimal clinically important difference end point.

If verified in prospective studies, this rule could facilitate treatment decision-making for patients with RA, concluded the researchers.


Rehberg M, Giegerich C, Praestgaard A, et al. Identification of a rule to predict response to sarilumab in patients with rheumatoid arthritis using machine learning and clinical trial data. Presented at: ACR Convergence 2020; November 5-9, 2020. Accessed January 15, 2021. 021. Abstract 2006.

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AI in Credit Decision-Making Is Promising, but Beware of Hidden Biases, Fed Warns – JD Supra

As financial services firms increasingly turn to artificial intelligence (AI), banking regulators warn that despite their astonishing capabilities, these tools must be relied upon with caution.

Last week, the Board of Governors of the Federal Reserve (the Fed) held a virtual AI Academic Symposium to explore the application of AI in the financial services industry. Governor Lael Brainard explained that particularly as financial services become more digitized and shift to web-based platforms, a steadily growing number of financial institutions have relied on machine learning to detect fraud, evaluate credit, and aid in operational risk management, among many other functions.[i]

In the AI world, machine learning refers to a model that processes complex data sets and automatically recognizes patterns and relationships, which are in turn used to make predictions and draw conclusions.[ii] Alternative data is information that is not traditionally used in a particular decision-making process but that populates machine learning algorithms in AI-based systems and thus fuels their outputs.[iii]

Machine learning and alternative data have special utility in the consumer lending context, where these AI applications allow financial firms to determine the creditworthiness of prospective borrowers who lack credit history.[iv] Using alternative data such as the consumers education, job function, property ownership, address stability, rent payment history, and even internet browser history and behavioral informationamong many other datafinancial institutions aim to expand the availability of affordable credit to so-called credit invisibles or unscorables.[v]

Yet, as Brainard cautioned last week, machine-learning AI models can be so complex that even their developers lack visibility into how the models actually classify and process what could amount to thousands of nonlinear data elements.[vi] This obscuring of AI models internal logic, known as the black box problem, raises questions about the reliability and ethics of AI decision-making.[vii]

When using AI machine learning to evaluate access to credit, the opaque and complex data interactions relied upon by AI could result in discrimination by race, or even lead to digital redlining, if not intentionally designed to address this risk.[viii] This can happen, for example, when intricate data interactions containing historical information such as educational background and internet browsing habits become proxies for race, gender, and other protected characteristicsleading to biased algorithms that discriminate.[ix]

Consumer protection laws, among other aspects of the existing regulatory framework, cover AI-related credit decision-making activities to some extent. Still, in light of the rising complexity of AI systems and their potentially inequitable consequences, AI-focused legal reforms may be needed. At this time, to help ensure that financial services are prepared to manage these risks, the Fed has called on stakeholdersfrom financial services firms to consumer advocates and civil rights organizations as well as other businesses and the general publicto provide input on responsible AI use.[x]

[i] Lael Brainard, Governor, Bd. of Governors of the Fed. Reserve Sys., AI Academic Symposium: Supporting Responsible Use of AI and Equitable Outcomes in Financial Services (Jan. 12, 2021), available at

[ii] Pratin Vallabhaneni and Margaux Curie, Leveraging AI and Alternative Data in Credit Underwriting: Fair Lending Considerations for Fintechs, 23 No. 4 Fintech L. Rep. NL 1 (2020).

[iii] Id.

[iv] Id.; Brainard, supra n. 1.

[v] Vallabhaneni and Margaux Curie, supra n.2; Kathleen Ryan, The Big Brain in the Black Box, Am. Bar Assoc. (May 2020),

[vi] Brainard, supra n.1; Ryan, supra n.5.

[vii] Brainard, supra n.1; Ryan, supra n.5.

[viii] Brainard, supra n.1.

[ix] Id. (citing Carol A. Evans and Westra Miller, From Catalogs to Clicks: The Fair Lending Implications of Targeted, Internet Marketing, Consumer Compliance Outlook (2019)).

[x] Id.

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