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Machine learning removes bias from algorithms and the hiring process – PRNewswire

November 6, 2020 | Author: admin

Arena Analytics' Chief Data Scientist unveils a cutting edge technique that removes latent bias from algorithmic models.

Currently, the primary methods of reducing the impact of bias on models has been limited to adjusting input data or adjust models after-the-fact to ensure there is no disparate impact.

Recent reporting from the Wall Street Journal confirmed these as the most recent advances, concluding, "It's really up to the software engineers and leaders of the company to figure out how to fix it [or] go into the algorithm and tweak some of the main factors it considers in making its decisions."

For several years, Arena Analytics was also limited to these approaches, but that all changed 9 months ago. Up until then, Arena removed all data from the models that could correlate to protected classifications and then measured demographic parity.

"These efforts brought us in line with EEOC compliance thresholds - also known as the or 80% rule," explains Myra Norton, President/COO of Arena. "But we've always wanted to go further than a compliance threshold.We've wanted to surface a MORE diverse slate of candidates for every role in a client organization.And that's exactly what we've accomplished, now surpassing 95% in our representation of different classifications."

Chief Data Scientist Patrick Hagerty will explain at MLConf the way he and his team have leveraged techniques known asadversarial networks,an aspect of Generative Adversarial Networks (GAN's), tools that pit one algorithm against another.

"Arena's primary model predicts the outcomes our clients want, and Model Two is a Discriminator designed to predict a classification," says Hagerty. "The Discriminator attempts to detect the race, gender, background, and any other protected class data of a person. This causes the Predictor to adjust and optimize while eliminating correlations with the classifications the Discriminator is detecting."

Arena trained models to do this until achieving what's known as the Nash Equilibrium. This is the point at which the predictor and discriminator have reached peak optimization.

Arena's technology has helped industrious individuals find a variety of jobs - from RNs to medtechs, caregivers to cooks, concierge to security. Job candidates who Arena predicted for success include veterans with no prior experience in healthcare or senior/assisted living, recent high school graduates whose plans to work while attending college were up-ended, and former hospitality sector employees who decided to apply their dining service expertise to a new setting.

"We succeeded in our intent to reduce bias and diversify the workforce, but what surprised us was the impact this approach had on our core predictions. Data once considered unusable, such as commuting distance, we can now analyze because we've removed the potentially-associated protected-class-signal," says Michael Rosenbaum, Arena's founder and CEO. "As a result, our predictions are stronger AND we surface a more diverse slate of candidates across multiple spectrums. Our clients can now use their talent acquisition function to really support and lead out front on Diversity and Inclusion."

About Arena (https://www.arena.io/) applies predictive analytics and machine learning to solve talent acquisition challenges. Learning algorithms analyze a large amount of data topredict with high levels of accuracy the likelihood of different outcomes occurring, such as someone leaving, being engaged, having excellent attendance, and more. By revealing each individual's likely outcomes in specific positions, departments, and locations, Arena is transforming the labor market from one based on perception and unconscious bias, to one based on outcomes. Arena is currently growing dramatically within the healthcare and hospitality industry and expanding its offerings to other people intensive industries. For more information contact [emailprotected]arena.io

SOURCE Arena

https://www.arena.io/

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Using machine learning to track the pandemic’s impact on mental health – MIT News

November 6, 2020 | Author: admin

Dealing with a global pandemic has taken a toll on the mental health of millions of people. A team of MIT and Harvard University researchers has shown that they can measure those effects by analyzing the language that people use to express their anxiety online.

Using machine learning to analyze the text of more than 800,000 Reddit posts, the researchers were able to identify changes in the tone and content of language that people used as the first wave of the Covid-19 pandemic progressed, from January to April of 2020. Their analysis revealed several key changes in conversations about mental health, including an overall increase in discussion about anxiety and suicide.

We found that there were these natural clusters that emerged related to suicidality and loneliness, and the amount of posts in these clusters more than doubled during the pandemic as compared to the same months of the preceding year, which is a grave concern, says Daniel Low, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT and the lead author of the study.

The analysis also revealed varying impacts on people who already suffer from different types of mental illness. The findings could help psychiatrists, or potentially moderators of the Reddit forums that were studied, to better identify and help people whose mental health is suffering, the researchers say.

When the mental health needs of so many in our society are inadequately met, even at baseline, we wanted to bring attention to the ways that many people are suffering during this time, in order to amplify and inform the allocation of resources to support them, says Laurie Rumker, a graduate student in the Bioinformatics and Integrative Genomics PhD Program at Harvard and one of the authors of the study.

Satrajit Ghosh, a principal research scientist at MITs McGovern Institute for Brain Research, is the senior author of the study, which appears in the Journal of Internet Medical Research. Other authors of the paper include Tanya Talkar, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT; John Torous, director of the digital psychiatry division at Beth Israel Deaconess Medical Center; and Guillermo Cecchi, a principal research staff member at the IBM Thomas J. Watson Research Center.

A wave of anxiety

The new study grew out of the MIT class 6.897/HST.956 (Machine Learning for Healthcare), in MITs Department of Electrical Engineering and Computer Science. Low, Rumker, and Talkar, who were all taking the course last spring, had done some previous research on using machine learning to detect mental health disorders based on how people speak and what they say. After the Covid-19 pandemic began, they decided to focus their class project on analyzing Reddit forums devoted to different types of mental illness.

When Covid hit, we were all curious whether it was affecting certain communities more than others, Low says. Reddit gives us the opportunity to look at all these subreddits that are specialized support groups. Its a really unique opportunity to see how these different communities were affected differently as the wave was happening, in real-time.

The researchers analyzed posts from 15 subreddit groups devoted to a variety of mental illnesses, including schizophrenia, depression, and bipolar disorder. They also included a handful of groups devoted to topics not specifically related to mental health, such as personal finance, fitness, and parenting.

Using several types of natural language processing algorithms, the researchers measured the frequency of words associated with topics such as anxiety, death, isolation, and substance abuse, and grouped posts together based on similarities in the language used. These approaches allowed the researchers to identify similarities between each groups posts after the onset of the pandemic, as well as distinctive differences between groups.

The researchers found that while people in most of the support groups began posting about Covid-19 in March, the group devoted to health anxiety started much earlier, in January. However, as the pandemic progressed, the other mental health groups began to closely resemble the health anxiety group, in terms of the language that was most often used. At the same time, the group devoted to personal finance showed the most negative semantic change from January to April 2020, and significantly increased the use of words related to economic stress and negative sentiment.

They also discovered that the mental health groups affected the most negatively early in the pandemic were those related to ADHD and eating disorders. The researchers hypothesize that without their usual social support systems in place, due to lockdowns, people suffering from those disorders found it much more difficult to manage their conditions. In those groups, the researchers found posts about hyperfocusing on the news and relapsing back into anorexia-type behaviors since meals were not being monitored by others due to quarantine.

Using another algorithm, the researchers grouped posts into clusters such as loneliness or substance use, and then tracked how those groups changed as the pandemic progressed. Posts related to suicide more than doubled from pre-pandemic levels, and the groups that became significantly associated with the suicidality cluster during the pandemic were the support groups for borderline personality disorder and post-traumatic stress disorder.

The researchers also found the introduction of new topics specifically seeking mental health help or social interaction. The topics within these subreddit support groups were shifting a bit, as people were trying to adapt to a new life and focus on how they can go about getting more help if needed, Talkar says.

While the authors emphasize that they cannot implicate the pandemic as the sole cause of the observed linguistic changes, they note that there was much more significant change during the period from January to April in 2020 than in the same months in 2019 and 2018, indicating the changes cannot be explained by normal annual trends.

Mental health resources

This type of analysis could help mental health care providers identify segments of the population that are most vulnerable to declines in mental health caused by not only the Covid-19 pandemic but other mental health stressors such as controversial elections or natural disasters, the researchers say.

Additionally, if applied to Reddit or other social media posts in real-time, this analysis could be used to offer users additional resources, such as guidance to a different support group, information on how to find mental health treatment, or the number for a suicide hotline.

Reddit is a very valuable source of support for a lot of people who are suffering from mental health challenges, many of whom may not have formal access to other kinds of mental health support, so there are implications of this work for ways that support within Reddit could be provided, Rumker says.

The researchers now plan to apply this approach to study whether posts on Reddit and other social media sites can be used to detect mental health disorders. One current project involves screening posts in a social media site for veterans for suicide risk and post-traumatic stress disorder.

The research was funded by the National Institutes of Health and the McGovern Institute.

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Using machine learning to track the pandemic's impact on mental health - MIT News

AI Recognizes COVID-19 in the Sound of a Cough Machine Learning Times – The Predictive Analytics Times

November 6, 2020 | Author: admin

Originally published in IEEE Spectrum, Nov 4, 2020.

Based on a cellphone-recorded cough, machine learning models accurately detect coronavirus even in people with no symptoms.

Again and again, experts have pleaded that we need more and faster testing to control the coronavirus pandemicand many have suggested that artificial intelligence (AI) can help. Numerous COVID-19 diagnostics in development use AI to quickly analyze X-ray or CT scans, but these techniques require a chest scan at a medical facility.

Since the spring, research teams have been working toward anytime, anywhere apps that could detect coronavirus in the bark of a cough. In June, a team at the University of Oklahoma showed it was possible to distinguish a COVID-19 cough from coughs due to other infections, and now a paper out of MIT, using the largest cough dataset yet, identifies asymptomatic people with a remarkable 100 percentdetection rate.

If approved by the FDA and other regulators, COVID-19cough apps, in which a person records themselves coughing on command,could eventually be used for free, large-scale screening of the population.

With potential like that, the field is rapidly growing: Teams pursuing similar projects include a Bill and Melinda Gates Foundation-funded initiative, Cough Against Covid, at the Wadhwani Institute for Artificial Intelligence in Mumbai; the Coughvid project out of the Embedded Systems Laboratory of the cole Polytechnique Fdrale de Lausanne in Switzerland; and the University of Cambridges COVID-19 Sounds project.

The fact that multiple models can detect COVID in a cough suggeststhat there is no such thing astruly asymptomatic coronavirus infectionphysical changes alwaysoccurthat change the way a person produces sound. There arent many conditions that dont give you any symptoms, says Brian Subirana, director of the MIT Auto-ID lab and co-author on the recent study, published in the IEEE Open Journal of Engineering in Medicine and Biology.

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The consistency of machine learning and statistical models in predicting clinical risks of individual patients – The BMJ – The BMJ

November 6, 2020 | Author: admin

Now, imagine a machine learning system with an understanding of every detail of that persons entire clinical history and the trajectory of their disease. With the clinicians push of a button, such a system would be able to provide patient-specific predictions of expected outcomes if no treatment is provided to support the clinician and patient in making what may be life-or-death decisions[1] This would be a major achievement. The English NHS is currently investing 250 million in Artificial Intelligence (AI). Part of this AI work could help to identify patients most at risk of diseases such as heart disease or dementia, allowing for earlier diagnosis and cheaper, more focused, personalised prevention. [2] Multiple papers have suggested that machine learning outperforms statistical models including cardiovascular disease risk prediction. [3-6] We tested whether it is true with prediction of cardiovascular disease as exemplar.

Risk prediction models have been implemented worldwide into clinical practice to help clinicians make treatment decisions. As an example, guidelines by the UK National Institute for Health and Care Excellence recommend that statins are considered for patients with a predicted 10-year cardiovascular disease risk of 10% or more. [7] This is based on the estimation of QRISK which was derived using a statistical model. [8] Our research evaluated whether the predictions of cardiovascular disease risk for an individual patient would be similar if another model, such as a machine learning models were used, as different predictions could lead to different treatment decisions for a patient.

An electronic health record dataset was used for this study with similar risk factor information used across all models. Nineteen different prediction techniques were applied including 12 families of machine learning models (such as neural networks) and seven statistical models (such as Cox proportional hazards models). It was found that the various models had similar population-level model performance (C-statistics of about 0.87 and similar calibration). However, the predictions for individual CVD risks varied widely between and within different types of machine learning and statistical models, especially in patients with higher CVD risks. Most of the machine learning models, tested in this study, do not take censoring into account by default (i.e., loss to follow-up over the 10 years). This resulted in these models substantially underestimating cardiovascular disease risk.

The level of consistency within and between models should be assessed before they are used for treatment decisions making, as an arbitrary choice of technique and model could lead to a different treatment decision.

So, can a push of a button provide patient-specific risk prediction estimates by machine learning? Yes, it can. But should we use such estimates for patient-specific treatment-decision making if these predictions are model-dependant? Machine learning may be helpful in some areas of healthcare such as image recognition, and could be as useful as statistical models on population level prediction tasks. But in terms of predicting risk for individual decision making we think a lot more work could be done. Perhaps the claim that machine learning will revolutionise healthcare is a little premature.

Yan Li, doctoral student of statistical epidemiology, Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester.

Matthew Sperrin, senior lecturer in health data science, Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester.

Darren M Ashcroft, professor of pharmacoepidemiology, Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester.

Tjeerd Pieter van Staa, professor in health e-research, Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester.

Competing interests: None declared.

References:

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The consistency of machine learning and statistical models in predicting clinical risks of individual patients - The BMJ - The BMJ

PathAI and Gilead Report Data from Machine Learning Model Predictions of Liver Disease Progression and Treatment Response at AASLD’s The Liver Meeting…

November 6, 2020 | Author: admin

BOSTON (PRWEB) November 06, 2020

PathAI, a global provider of AI-powered technology applied to pathology research, today announced the results of a research collaboration with Gilead that retrospectively analyzed liver biopsies from participants in clinical trials evaluating treatments for NASH or CHB (1). Using digitized hematoxylin and eosin (H&E)-, picrosirius red-, and trichrome-stained biopsy slides, PathAIs machine learning (ML) models were able to accurately predict changes in features traditionally used as markers for liver disease progression in clinical practice and clinical trials, including fibrosis, steatosis, hepatocellular ballooning, and inflammation. The new results will be presented in an oral presentation and 4 poster sessions at The Liver Meeting Digital Experience (TLMdX) that will be held from November 13-16, 2020.

The data builds upon PathAIs previous success in retrospectively staging liver biopsies from clinical trials by showing that ML models may uncover patterns of histological features that correlate with disease progression or treatment response. Furthermore, ML models were able to estimate the hepatic venous pressure gradient (HVPG) in study subjects with NASH related cirrhosis and quantify fibrosis heterogeneity from digitized slides, which are measures that are not reliably captured by traditional pathology methods. After appropriate clinical validation, these new tools could be useful in staging disease more accurately than can be done with current approaches.

"We continue to use machine learning to advance our understanding of liver diseases, including NASH and hepatitis B, as a foundation for developing new methods to track disease progression and assess response to therapeutics, said PathAI co-founder and Chief Executive Officer Andy Beck MD, PhD. Our long-standing partnership with Gilead continues to demonstrate the power of AI-based pathology to support development efforts to bring new therapies to patients."

Highlights include:

Data presented at AASLD demonstrate the potential of machine learning approaches to improve our assessment of liver disease severity, reduce the variability of human interpretation of liver biopsies, and identify novel features associated with disease progression, said Rob Myers, MD, Vice President, Liver Inflammation/Fibrosis, Gilead Sciences. We are proud of our ongoing partnership with PathAI and look forward to continued collaboration toward our shared goals of enhancing research efforts and improving outcomes of patients with liver disease.

The antiviral drug TDF effectively suppresses hepatitis B virus in patients with CHB, but a small subset of patients have persistently elevated serum ALT despite virologic suppression. ML-models were applied to biopsy data from registrational studies of TDF to examine this small subgroup of non-responders. Analyses of the ML-model predicted histologic features showed that persistently elevated ALT after five years of TDF treatment is associated with a higher steatosis score at BL and increases in steatosis during follow-up. These data suggest that subjects with elevated ALT despite TDF treatment may have underlying fatty liver disease that impacts biochemical response.Machine Learning Enables Quantitative Assessment of Histopathologic Signatures Associated with ALT Normalization in Chronic Hepatitis B Patients Treated with Tenofovir Disoproxil Fumarate (TDF) Oral Abstract #18

ML-models were deployed on biopsies from registrational trials of TDF in CHB to identify cellular and tissue-based phenotypes associated with HBV DNA and hepatitis B e-antigen (HBeAg). The study demonstrated that proportionate areas of ML-model-predicted hepatocellular ballooning at BL and Yr 5, and lobular inflammation at Yr 5 were higher in subjects that did not achieve virologic suppression. In addition, lymphocyte density across the tissue and within regions of lobular inflammation correlated with HBeAg loss, supporting the importance of an early immune response for viral clearance.Machine Learning Based Quantification of Histology Features from Patients Treated for Chronic Hepatitis B Identifies Features Associated with Viral DNA Suppression and dHBeAg Loss Poster Number #0848

Standard manual methods for staging liver fibrosis have limited sensitivity and reproducibility. Application of a ML-model to evaluate changes in fibrosis in response to treatment in the STELLAR and ATLAS trials enabled development of the DELTA (Deep Learning Treatment Assessment) Liver Fibrosis Score. This scoring method accounts for the heterogeneity in fibrosis severity that can be detected by ML-models and reflects changes in fibrotic patterns that occur in response to treatment. Application of the DELTA Liver Fibrosis Score to biopsies from the Phase 2b ATLAS trial demonstrated a reduction in fibrosis in response to treatment with the investigational combination of cilofexor and firsocostat that was not detected by standard staging methods. Validation of a Machine Learning-Based Approach (DELTA Liver Fibrosis Score) for the Assessment of Histologic Response in Patients with Advanced Fibrosis Due to NASH Poster Number #1562

Integration of tissue transcriptomic data with histologic information is likely to reveal new insights into disease. Using liver tissue obtained during the STELLAR trials evaluating NASH subjects with advanced fibrosis, RNA-seq-generated, tissue-level gene expression profiles were integrated with ML-predicted histology. This analysis revealed five key genes strongly correlated with proportionate areas of portal inflammation and bile ducts, features that are themselves predictive of disease progression in NASH. High levels of expression of these genes was associated with an increased risk of progression to cirrhosis in subjects with bridging (F3) fibrosis (hazard ratio [HR] 2.1; 95% CI 1.25, 3.49) and liver-related clinical events among those with cirrhosis (HR 4.05; 95% CI 1.4, 14.36). Integration of Machine Learning-Based Histopathology and Hepatic Transcriptomic Data Identifies Genes Associated with Portal Inflammation and Ductular Proliferation as Predictors of Disease progression in Advanced Fibrosis Due to NASH Poster Number #595

The severity of portal hypertension as assessed by HPVG predicts the risk of hepatic complications in patients with liver disease but is not simple to measure. ML-models were trained on images of 320 trichrome-stained liver biopsies from a phase 2b trial of investigational simtuzumab in subjects with compensated cirrhosis due to NASH to recognize patterns of fibrosis that correlate with centrally-read HVPG measurements. Deployed on a test set of slides, ML-calculated HVPG scores strongly correlated with measured HVPG and could discriminate subjects with clinically-significant portal hypertension (HVPG 10 mm Hg).A Machine Learning Model Based on Liver Histology Predicts the Hepatic Venous Pressure Gradient (HVPG) in Patients with Compensated Cirrhosis Due to Nonalcoholic Steatohepatitis (NASH) Poster Number #1471

(1) Trials include STELLAR, ATLAS, and NCT01672879 for investigation of NASH therapies, and registrational studies GS-US-174-102/103 for tenofovir disoproxil fumarate [TDF] for CHB.

About PathAIPathAI is a leading provider of AI-powered research tools and services for pathology. PathAIs platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine and deep learning. Based in Boston, PathAI works with leading life sciences companies and researchers to advance precision medicine. To learn more, visit pathai.com.

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PathAI and Gilead Report Data from Machine Learning Model Predictions of Liver Disease Progression and Treatment Response at AASLD's The Liver Meeting...

Google Introduces New Analytics with Machine Learning and Predictive Models – IBL News

November 6, 2020 | Author: admin

IBL News | New York

Google announcedthe introduction of its new Google Analytics with machine learning at its core, which is privacy-centric by design. They are built on the foundation of the App + Web propertypresentedlast year.

The goal of the giant searching company is to help users to get better ROI and improve their marketing decisions. It follows what a survey from Forrester Consulting points out that improving the use of analytics is a top priority for marketers.

The machine learning models include will allow the ability to alert on trends in data, like products seeing rising demand, and help to anticipate future actions from customers. For example, it calculates churn probability so you can more efficiently invest in retaining customers at a time when marketing budgets are under pressure, says in a blog-postVidhya Srinivasan,Vice President, Measurement, Analytics, and Buying Platforms at Google.

It also adds new predictive metrics indicating the potential revenue that can be earned from a particular group of customers. This allows you to create audiences to reach higher-value customers and run analyses to better understand why some customers are likely to spend more than others, so you can take action to improve your results, wroteVidhya Srinivasan.

The new Google Analytics providescustomer-centric measurement, including conversion from YouTube video views, Google and non-Google paid channels, search, social, and email. The setup works with or without cookies or identifiers.

They come by default for new web properties. In order toreplace the existing setup, Google encourages tocreate a new Google Analytics 4 property (previously called an App + Web property). Enterprise marketers are currently using a beta version with an Analytics 360 version with SLAs and advanced integrations with tools like BigQuery.

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Google Introduces New Analytics with Machine Learning and Predictive Models - IBL News

Free Webinar | Machine Learning and Data Analytics in the Pandemic Era – MIT Sloan

November 6, 2020 | Author: admin

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Global Predictive Analytics Market (2020 to 2025) – Advent of Machine Learning and Artificial Intelligence is Driving Growth – PRNewswire

November 6, 2020 | Author: admin

DUBLIN, Nov. 3, 2020 /PRNewswire/ -- The "Predictive Analytics Market by Solution (Financial Analytics, Risk Analytics, Marketing Analytics, Web & Social Media Analytics, Network Analytics), Service, Deployment Mode, Organization Size, Vertical, and Region - Global Forecast to 2025" report has been added to ResearchAndMarkets.com's offering.

The global predictive analytics market size to grow from USD 7.2 billion in 2020 to USD 21.5 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 24.5% during the forecast period.

Various factors such as the growing focus on digital transformation, rise adoption of big data and AI and ML technologies, increasing focus on remote monitoring in support of the COVID-19 pandemic, and the need to forecast possible future financial scenarios to answer specific business questions are expected to drive the adoption of the predictive analytics market. The objective of the report is to define, describe, and forecast the predictive analytics market size based on component, organization size, deployment mode, vertical, and region.

The spread of COVID-19 has generated a massive disruption in daily activities. It has forced people to follow social distancing policies, temporarily suspend many business activities, and limit travel. Under such circumstances, the healthcare vertical has emerged as the biggest user of big data and predictive analytics to understand the virus and its spread. Predictive modeling has provided organizations with transportation fleets to create future business insights with a significant degree of accuracy. Predictive analytics companies are witnessing a slowdown in their growth, owing to the lockdowns imposed worldwide.

Healthcare and life sciences and BFSI verticals have been least impacted by the COVID-19 and are continuing the adoption of predictive analytics solutions. The healthcare vertical has sought to use big data and predictive analytics tools to better understand the virus and its spread. Predictive analytics has helped researchers around the world to build predictive analytics models that can track COVID-19 surges in different countries. The competition among key predictive analytics companies is expected to intensify as most upcoming analytics projects have been put on hold owing to the pandemic. Businesses have already started making efforts to return to the normal and are facing multiple challenges at customer and operational levels. New practices, such as work-from-home and social distancing, have led to the requirement of remote health monitoring of patients and assets and smart payment technologies, as well as the development of digital infrastructures for large-scale technology deployments.

The services segment to grow at a higher CAGR during the forecast period

The predictive analytics market is segmented on the basis of components, such as solutions and services. The services segment is expected to grow at a rapid pace during the forecast period. Factors such as pre-sales and post-sales support and the lack of technical skills and capabilities needed for assistance during the up-gradation of software drive the adoption of predictive analytics services.

The risk analytics solution segment to have the largest market size during the forecast period

The Predictive analytics market by solution has been segmented into financial analytics, risk analytics, marketing analytics, sales analytics, customer analytics, web and social media analytics, supply chain analytics, network analytics, and others (HR analytics and legal analytics). The risk analytics solution in predictive analytics facilitates enterprises to establish a baseline for measuring risks across verticals, such as BFSI, healthcare, and life sciences, and retail and eCommerce, by incorporating all the facets of risks together into a single unified system that provides key decision-makers with adequate clarity in identifying, viewing, understanding, and managing risks leading to its adoption in the predictive analytics market.

The BFSI segment to grow to have the largest market size during the forecast period

The predictive analytics market by vertical has been segmented into BFSI, retail and eCommerce, manufacturing, government and defense, healthcare and life sciences, energy and utilities, transportation and logistics, telecommunications and IT, and others(media and entertainment, travel and hospitality, and education). BFSI vertical is expected to register the largest market size during the forecast period due to significant financial data's sensitivity and need to coordinate with numerous other sectors, including stock exchanges, tax authorities, central banks, securities controlling authorities, and revenue departments. The emergence of predictive analytics in finance has necessitated the development of predictive analytics solutions capable of handling it in real-time.

Among regions, Asia Pacific (APAC) to grow at the highest CAGR during the forecast period

APAC is expected to grow at the highest CAGR during the forecast period. The increasing investments by the tech companies in major APAC countries, such as China, and Japan, increasing the increasing adoption of AI and deep learning algorithms are expected to drive the growth of the market in APAC. Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights4.1 Attractive Opportunities in Predictive Analytics Market4.2 Market, by Solution4.3 Market, by Region4.4 Market, by Solution and Vertical

5 Market Overview and Industry Trends5.1 Introduction5.2 Market Dynamics5.2.1 Drivers5.2.1.1 Rising Adoption of Big Data and Other Related Technologies5.2.1.2 Advent of Machine Learning and Artificial Intelligence5.2.1.3 Cost Benefits of Cloud-Based Predictive Analytics Solutions5.2.2 Restraints5.2.2.1 Changing Regional Data Regulations Leading to the Time-Consuming Restructuring of Predictive Models5.2.3 Opportunities5.2.3.1 Rising Internet Proliferation and Growing Usage of Connected and Integrated Technologies5.2.3.2 Increasing Demand for Real-Time Streaming Analytics Solutions to Track and Monitor the COVID-19 Spread5.2.4 Challenges5.2.4.1 Growing Demand for Diversified Data Models Based on Business Needs5.2.4.2 Ownership and Privacy of Collected Data5.2.5 Cumulative Growth Analysis5.3 Impact of COVID-19 on the Predictive Analytics Market5.4 Predictive Analytics: Evolution5.5 Predictive Analytics: Ecosystem5.6 Case Study Analysis5.7 Patent Analysis5.8 Value Chain Analysis5.9 Technology Analysis5.10 Pricing Analysis5.11 Regulatory Implications

6 Predictive Analytics Market, by Component6.1 Introduction6.1.1 Components: Market Drivers6.1.2 Components: COVID-19 Impact6.2 Solutions6.2.1 Financial Analytics6.2.1.1 Fraud Detection6.2.1.2 Profitability Management6.2.1.3 Governance, Risk, and Compliance Management6.2.1.4 Others6.2.2 Risk Analytics6.2.2.1 Cyber Risk Management6.2.2.2 Operational Risk Management6.2.2.3 Credit and Market Risk Management6.2.2.4 Others6.2.3 Marketing Analytics6.2.3.1 Predictive Modelling6.2.3.2 Yield Management6.2.3.3 Product and Service Development Strategies6.2.3.4 Others6.2.4 Sales Analytics6.2.4.1 Sales Life Cycle Management6.2.4.2 Sales Rep Efficiency Management6.2.4.3 Others6.2.5 Customer Analytics6.2.5.1 Customer Segmentation and Clustering6.2.5.2 Customer Behavior Analysis6.2.5.3 Monitoring Customer Loyalty and Satisfaction6.2.5.4 Others6.2.6 Web and Social Media Analytics6.2.6.1 Social Media Management6.2.6.2 Search Engine Optimization6.2.6.3 Performance Monitoring6.2.6.4 Competitor Benchmarking6.2.7 Supply Chain Analytics6.2.7.1 Distribution and Logistics Optimization6.2.7.2 Inventory Management6.2.7.3 Manufacturing Analysis6.2.7.4 Others6.2.8 Network Analytics6.2.8.1 Intelligent Network Optimization6.2.8.2 Traffic Management6.2.8.3 Others6.2.9 Others6.3 Services6.3.1 Managed Services6.3.2 Professional Services6.3.2.1 Consulting6.3.2.2 Deployment and Integration

7 Predictive Analytics Market, by Deployment Mode7.1 Introduction7.1.1 Deployment Mode: Market Drivers7.1.2 Deployment Mode: COVID-19 Impact7.2 Cloud7.3 On-Premises

8 Predictive Analytics Market, by Organization Size8.1 Introduction8.1.1 Organization Size: Market Drivers8.1.2 Organization Size: COVID-19 Impact8.2 Large Enterprises8.3 Small and Medium-Sized Enterprises

9 Predictive Analytics Market, by Vertical9.1 Introduction9.1.1 Vertical: Market Drivers9.1.2 Vertical: COVID-19 Impact9.2 Predictive Analytics: Enterprise Use Cases9.3 Banking, Financial Services, and Insurance9.4 Telecommunications and It9.5 Retail and Ecommerce9.6 Healthcare and Life Sciences9.7 Manufacturing9.8 Government and Defense9.9 Energy and Utilities9.10 Transportation and Logistics9.11 Others

10 Predictive Analytics Market, by Region10.1 Introduction10.2 North America10.3 Europe10.4 Asia-Pacific10.5 Middle East and Africa10.6 Latin America

11 Competitive Landscape11.1 Overview11.2 Market Evaluation Framework11.3 Market Share, 202011.4 Historic Revenue Analysis of Key Market Players11.5 Key Market Developments11.5.1 New Product Launches and Product Enhancements11.5.2 Business Expansions11.5.3 Mergers and Acquisitions11.5.4 Partnerships, Agreements, Contracts, and Collaborations

12 Company Evaluation Matrix and Company Profiles12.1 Overview12.2 Company Evaluation Matrix Definitions and Methodology12.2.1 Market Ranking Analysis, by Company12.3 Company Evaluation Matrix, 202012.3.1 Star12.3.2 Emerging Leaders12.3.3 Pervasive12.3.4 Participant12.4 Company Profiles12.4.1 Introduction12.4.2 Microsoft12.4.3 IBM12.4.4 Oracle12.4.5 SAP12.4.6 SAS Institute12.4.7 Google12.4.8 Salesforce12.4.9 Aws12.4.10 Hpe12.4.11 Teradata12.4.12 Alteryx12.4.13 Fair Issac Corporation12.4.14 Altair12.4.15 Domo12.4.16 Cloudera12.4.17 Board International12.4.18 Tibco Software12.4.19 Hitachi Vantara12.4.20 Happiest Minds12.4.21 Dataiku12.4.22 Rapidminer12.4.23 Qlik12.4.24 IBI12.4.25 Infor12.5 Startup/Sme Evaluation Matrix, 202012.5.1 Progressive Companies12.5.2 Responsive Companies12.5.3 Dynamic Companies12.5.4 Starting Blocks12.6 Startup/Sme Profiles

13 Appendix

For more information about this report visit https://www.researchandmarkets.com/r/o1r23s

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Global Predictive Analytics Market (2020 to 2025) - Advent of Machine Learning and Artificial Intelligence is Driving Growth - PRNewswire

Bitcoin price nears $16K, but it’s Ethereum that may shine in November – Cointelegraph

November 6, 2020 | Author: admin

The price of Bitcoin (BTC) is nearing $16,000 after achieving $15,960 on Binance. Following the dominant cryptocurrency's rally, analysts are now looking toward Ether (ETH). The Ethereum blockchain's native token has seen heightened momentum in the past week. After underperforming against BTC in October, the probability of a new ETH rally is beginning to increase.

There are two key reasons why analysts expect Ether to perform strongly in the near term. First, the capital in the Bitcoin market could move into ETH following the announcement of Ethereum 2.0. Second, ETH recently tested a critical resistance level, raising the chances of a broader rally. Given that the altcoin market has historically rallied after an initial Bitcoin upsurge, the timing of an ETH uptrend is ideal.

Since Oct. 21, the price of Bitcoin has increased by around 33%. It broke out of important resistance areas, one after another, starting with $13,000. When Bitcoin initially surpassed $13,000, large whale clusters formed at that level. It showed that whales began to actively accumulate BTC, causing $13,000 to evolve into a support zone.

After BTC reclaimed $13,000 as a support level for the first time since July 2019, it continued to surge upward. Over time, it confirmed $13,500 as the next support level, followed by $14,000 and, most recently, $15,000. When Bitcoin started climbing upward, analysts said it was negative for altcoins, as it began to suck most of the volume from the crypto market. Consequently, as Bitcoin rallied, many altcoins declined in value against both Bitcoin and the U.S. dollar.

The overwhelming strength of Bitcoin from October to early November took a hard toll on the altcoin market, but Bitcoin's price action has shown that the bullish market sentiment around crypto has returned. As such, a clean breakout above $15,000 could trigger more capital to diverge into higher-risk plays, which include Ether.

Denis Vinokourov, head of research at crypto exchange and broker Bequant, told Cointelegraph that capital from Bitcoin could cycle into Ether and theEthereum ecosystem. In the last 48 hours, the decentralized finance market has performed particularly strong after stagnating since early September.

DeFi tokens, such as Yearn.finance's YFI and Uniswap's UNI surged by almost 30% after Ethers abrupt recovery. Hence, Vinokourov emphasized that the broader Ethereum ecosystem could soon benefit from Bitcoin's rally:

Atop the historical tendency of Ether to soar following a Bitcoin rally, crypto traders have said that ETH could soon rise against Bitcoin. Michal van de Poppe, a full-time trader at the Amsterdam Stock Exchange, said the ETH/BTC trading pair has hit a major support area. Van de Poppe stated, It took ages, but $ETH reached the 0.026 area we've been discussing a lot, referring to it as a big support zone for ETH.

The release of Ethereum 2.0 in the imminent future is critical for the momentum of Ether, as the network upgrade would significantly increase the transaction capacity of ETH. This would allow the new DeFi cycle, if it emerges, to last for a long period because it would reduce the risk of network clogs and high transaction fees. Since Ethereum 2.0 supports staking, allowing users to allocate 32 ETH to the network in return for incentives, it could decrease the circulating supply of ETH across exchanges.

According to Ethereum co-founder Vitalik Buterin's blog post titled "Why Proof of Stake," staking on Ethereum will reward users with a 15% return. Because the rate of return is based on ETH holdings and not the U.S. dollar, if the price of ETH continues to increase, then the staking incentives increase with it. As such, analysts expect more investors to accumulate ETH to stake it, which would decrease the sell-side pressure on it.

The market and the community have anticipated Ethereum 2.0 for several years, but challenges have delayed its release. Ethereum 2.0 has required several testnets with an immense amount of testing due to the complexity of the upgrade. Developers behind Ethereum 2.0 wrote on the Medalla testnet's Github page:

The sentiment around Ether has become increasingly bullish because the launch of Ethereum 2.0 coincides with various favorable catalysts for ETH. A pseudonymous cryptocurrency trader known as Loma pinpointed the fact that Ethereum 2.0 will remove about $1 billion from the market. While supply drops, the rally of Bitcoin is bringing significant capital back into the cryptocurrency as the ETH/BTC trading pair is forming a bottom formation.

The excitement around Ethereum 2.0 has intensified after Buterin's personal wallet sent 3,200 ETH to an Ethereum 2.0 deposit address. According to the official Ethereum 2.0 release notes by coordinator Danny Ryan, if there are 16,384 deposits of 32 ETH seven days prior to Dec. 1, the Ethereum 2.0 upgrade can commence. After years of research, testing and implementation, there is finally a hard date for the release.

The confluence of Ethereum 2.0 nearing, which would benefit the entire Ethereum and DeFi ecosystem in terms of scaling, and the strength of the ETH/BTC trading pair makes a rally in November and December more likely. There is also the narrative that ETH surged significantly in January 2018 to its all-time high of $1,419, almost a month after BTC reached its record-high at $20,000.

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Bitcoin price nears $16K, but it's Ethereum that may shine in November - Cointelegraph

Crypto Analytics Firm Warns Bitcoin Overbought, With One Altcoin Showing Biggest Bullish Divergence Since September – The Daily Hodl

November 6, 2020 | Author: admin

The crypto intelligence firm Santiment says Bitcoin is starting to show signals that a sell-off event is on the horizon.

Santiment says BTC is overbought based on three metrics. The first, Daily Active Addresses (DAA) vs. Price Divergence,compares an assets price movement to the number of unique crypto addresses interacting with that particular coin on a daily timeframe. The metric views price action that outperforms DAA as a bearish signal, and vice versa.

The second, MVRV Opportunity/Danger Zones, relies on average trader returns from various timeframes to determine danger zones or sell points and opportunity zones or buy points. When average trader returns surge too fast, the metric views it as a bearish signal and vice versa.

The third is Weighted Social Sentiment, which tracks the sentiment of market participants on Twitter.

According to the Daily Active Addresses (DAA) vs. Price Divergence metric, Santiment believes that BTC could be ripe for a significant correction.

The nine consecutive days of neon red territory on the DAA Divergence model indicates that prices are WAY inflated compared to the growth that unique addresses have seen as the price rose to above $14,000 to end October. Now, with a decline quickly back to $13,400, this could be just the beginning of the bleed. November 2nd marked the single most bearish divergence in over a year.

Its the same case for MVRV Opportunity/Danger Zones as Santiment says those who bought BTC recently may succumb to profit-taking.

Right now, at -79.6%, Bitcoin is looking a lot like an asset that is about to make a lot of BTC FOMOers have a bad time.

As for BTCs Weighted Social Sentiment, the crypto analytics platform says that the metric is postive which is a bearish signal.

While BTC may be showing signs of bullish exhaustion, Santiment is highlighting one altcoin that looks primed to ignite a bounce.

The crypto insights company says Synthetix Token (SNX) has the potential to rally as the coins address activity is showing signs of life.

Yesterday marked the biggest bullish divergence for SNX since September 25th, and if and when altcoins are finally able to have their moment in the sun again, expect this project to see some isolated pumps.

Santiment also gave SNX a bullish rating on the MVRV Opportunity/Danger Zones metric even after the coin lost 30% of its value over the past week.

And with so many SNX traders bleeding big right now, its +68% bullish divergence on this model indicates it could be a great time to take a chance if Bitcoin can stay propped up for a while.

Featured Image: Shutterstock/Art Furnace

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Crypto Analytics Firm Warns Bitcoin Overbought, With One Altcoin Showing Biggest Bullish Divergence Since September - The Daily Hodl

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Crypto Analytics Firm Warns Bitcoin Overbought, With One Altcoin Showing Biggest Bullish Divergence Since September – The Daily Hodl

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