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How DevOps Powered by AI and Machine Learning Is Delivering Business Transformation – DevOps.com

how devops powered by ai and machine learning is delivering business transformation – devops.com

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How DevOps Powered by AI and Machine Learning Is Delivering Business Transformation – DevOps.com























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How This Biotech Company Integrates ML Algorithms To Detect Breast Cancer

how this biotech company integrates ml algorithms to detect breast cancer

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Dr Manjiri Bakre who was then pursuing a PhD at Indian Institute of Science had a friend who was diagnosed with breast cancer. The cancer was detected early on and she underwent surgery soon after. While Dr Bakre and her friend rejoiced at the successful treatment, cancer unfortunately recurred in her body at 2-3 sites, while she was pursuing her post-doctoral fellowship. The cancer was aggressive and despite all the treatment options, she succumbed to it within 2-3 years of diagnosis. 

This unfortunate turn of events led Dr Bakre to think that there needs to be more awareness about the course of cancer. She strongly felt that knowing about the aggressiveness of cancer whether it is a ‘small or big’ tumour can help patients plan their treatment and life accordingly. Even if there were tests in the Western countries, they were out of reach of patients because of the cost or different biology in Indian patients. 

This led her to found OncoStem Diagnostics in 2011 for Indian patients to take tests that can predict the recurrence of cancer, just like the Western counterparts. 

In an interaction with Analytics India Magazine, Dr Bakre shared that OncoStem’s first focus has been the …

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Robot chef uses machine learning to perfect its omelette-making skills

robot chef uses machine learning to perfect its omelette-making skills

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From robots that flip burgers in California to ones that serve up bratwursts in Berlin, we are starting to see how machines can play sous-chef in kitchens around the world. But scientists at the University of Cambridge have been exploring how these culinary robots might not only do some of the heavy lifting but actually elevate the dining experience for the humans they serve, demonstrating some early success in a robot trained to cook omelettes.The research project is a collaboration between the University of Cambridge researchers and domestic appliance company Beko, with the scientists setting out to take robotic cooking into new territory. Where robot chefs have been developed to prepare pizzas, pancakes and other items, the team was interested in how it might be possible to optimize the robot’s approach and produce a tastier meal based on human feedback.“Cooking is a really interesting problem for roboticists, as humans can never be totally objective when it comes to food, so how do we as scientists assess whether the robot has done a good job?” says Dr Fumiya Iida from Cambridge’s Department of Engineering, who led the research.The team’s robot chef was trained to perform the …

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Machine learning predicts potential complications in CT-guided thoracic biopsies

machine learning predicts potential complications in ct-guided thoracic biopsies

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CT-guided transthoracic biopsy performed by expert radiologists is both accurate and safe, and when paired with machine learning, can help physicians predict the likelihood of complications.Doctors from the University of Pennsylvania’s Perelman School of Medicine examined nearly 800 CTTB procedures performed in a tertiary hospital for their research published in Academic Radiology. In 97% of these minimally invasive procedures, diagnostic yield proved to be “excellent,” with complications occurring in only 2% of cases.

While CTTB is relatively safe and preferred over other methods to biopsy thoracic lesions, first author Eduardo J. Mortani Barbosa Jr., MD, and colleagues found a neural network model could predict the severity of complications when they do happen in up to 94% of situations.

“These results suggest that sophisticated statistical models, especially with machine learning, may be accurate enough to allow pre-procedure prediction of CTTB related complications, based solely on imaging and clinical data, therefore allowing preemptive preventive measures to be applied in higher risk patients,” the authors added.

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The Machine Learning Fix For Healthcare Disbursement Delays

the machine learning fix for healthcare disbursement delays

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Posted on June 2, 2020 Financial institutions (FIs), businesses and healthcare providers are adjusting their operations to suit the new reality that the COVID-19 pandemic has caused. Healthcare provideers, for example, are turning to telehealth solutions to service patients for routine appointments as to keep those individuals from entering hospitals or doctors’ offices and risking exposure. Providers are also attempting to find ways to speed up the disbursements process to both doctors and to patients expecting funds from health claims. Innovating for speedy disbursements in the healthcare space can be tricky, because many of these payments come attached with reams of complicated paperwork with sensitive and personal medical information that must be verified before the money can be sent. Both consumers and physicians, however, are more frustrated by the longer wait times that come with check disbursements at a time when financial strains are more widespread.In the latest Disbursements Tracker®, PYMNTS examines how the COVID-19 pandemic is impacting the healthcare industry and how it is exposing issues with check disbursements. The Tracker also examines what digital technologies or payment methods may help to ease these frictions, as well as how the pandemic is continuing to affect government agencies and small businesses …

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Machine Learning Algorithm Predicts Financial Burden Due To Cancer Treatment – Oncology Nurse Advisor

machine learning algorithm predicts financial burden due to cancer treatment – oncology nurse advisor

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A machine-learning algorithm was able to predict the rate of financial stress on patients who underwent treatment for their breast cancer, according to a retrospective survey and algorithm-modeling study. These findings were presented during the ASCO 2020 Virtual Scientific Program.

Six
hundred and eleven patients who had undergone treatment for breast cancer at University
of Texas MD Anderson Cancer Center in Houston, Texas, were retrospectively
surveyed. All patients were adults who underwent either mastectomy or
lumpectomy. Data were collected as a FACT-COST patient-reported outcome
measurements as well as other financial indicators including insurance status
and income. Missing data were imputed by multiple imputation. A
LASSO-regularized linear model was used to train and validate their neural
network, in which instances were randomly assigned to training or validation
cohorts in a 3:1 ratio.

A
minority of women (48; 23.6%) reported financial burden due to their cancer
treatment. The machine learning algorithm predicted financial burden with a
high accuracy (83%), sensitivity (81%), and specificity (82%), and area under
the receiver operating curve (0.82)

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The
investigators identified the 2 key predictors of financial burden as
neo-adjuvant therapy (βregularized 0.12) and autologous
reconstruction (βregularized 0.10).

The
study authors concluded that their machine learning model could accurately
predict financial difficulties due to treatment of breast …

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GNY Demonstrates the World’s First Decentralized Machine Learning Tool for COVID-19 Analysis

gny demonstrates the world’s first decentralized machine learning tool for covid-19 analysis

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NEW YORK, NY / ACCESSWIRE / June 1, 2020 / GNY, the world’s first decentralized machine learning platform demonstrated its secure data collaboration and machine learning capabilities for analyzing COVID-19 mortality trends in different U.S. cities.The demo, which used mortality data from the John Hopkins University, showed how different states are able to share their data on a blockchain architecture and use GNY’s machine learning services without risking sensitive data theft.The data is never stored in a central location, meaning hackers have no server to attack. GNY’s machine learning platform processes the data where it is directly on the chain and shares the results back with the client. This means users worry about neither the data security nor the machine learning algorithms, as GNY offers both.”This global lockdown has forced us all to think about how our digital infrastructure can support new and better ways to collaborate with data more effectively in a secure environment,” said Cosmas Wong, CEO of GNY. “We are proud to deliver a solution that marries the power of two of the most exciting technologies to date; machine learning and the blockchain.”In this demo, GNY decentralized a Support Vector Machine (SVM) algorithm on-chain to detect which …

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Training agents to walk with purpose: Improving machine learning and relational data classification

training agents to walk with purpose: improving machine learning and relational data classification

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Using reinforcement learning, a search agent can be trained to walk on a relational data set more efficiently than a conventional “random” walk can do. For example, if a search agent needs to learn about a particular individual, it will be positively rewarded for interacting with entities that know the individual (blue dots), negatively rewarded for interacting with entities that do not know the individual (red dots) and unrewarded for interacting with entities that may or may not know the individual (white dots). Note that the agent may smartly interact with a few red dots to reach more blue dots. Credit: Akujuobi et al.

A classification algorithm for relational data that is more accurate, as well as orders of magnitude more efficient than previous schemes, has been developed through a research collaboration between KAUST and Nortonlifelock Research Group in France.

The new algorithm, which uses an approach called reinforcement learning, demonstrates the power of machining learning techniques in even tried-and-true tasks like classifying relational data.
One of the most common classes of data is relational data, where discrete data points or nodes are connected in some way to others. A social network is a good example, where each user is …

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Announcing DataGroomr, the App that Utilizes Machine Learning to Find Duplicates in Salesforce Automatically

announcing datagroomr, the app that utilizes machine learning to find duplicates in salesforce automatically

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PHILADELPHIA, June 1, 2020 /PRNewswire/ — Today, DataGroomr announced the release of its new Data Quality Management platform for Salesforce. A first of its kind, the platform utilizes Machine Learning algorithms to circumvent the need for any human intervention when it comes to identifying duplicates in Salesforce. Conveniently, the algorithms analyze every record in Salesforce to return a list of duplicates for review, saving admins the headache of designing and managing custom rules and filters. Similarly, by importing new records to Salesforce via Datagroomr, users can prevent new duplicates from being created.

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Eliminate Duplicates the Easy Way

The Easy-to-Use Deduplication App

Delivered as a Software-as-a-Service, the solution provides an intuitive interface for administrators to review duplicates, append record data, and merge faster than ever before. To ensure that Salesforce stays free of duplication, the platform includes robust automation capabilities for admins to schedule duplication analyses and mass merge tasks.

Co-Founder of DataGroomr, Steve Pogrebivsky explained that “the platform simplifies the approach to deduplication by harnessing the power of Machine Learning.  Grappling with duplicate rules, filters, and cumbersome Excel analyses are a thing of the past – this is truly a new era for the data quality focussed Salesforce Administrator.”
Steve went …

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