Mediaboss Marketing

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:

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

Select your countryUnited StatesCanadaAfghanistanAlbaniaAlgeriaAmerican SamoaAndorraAngolaAntigua and BarbudaArgentinaArmeniaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBosnia and HerzegovinaBotswanaBrazilBruneiBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaColombiaComorosCongo, Democratic Republic of theCongo, Republic of theCosta RicaCte d'IvoireCroatiaCubaCyprusCzech RepublicDenmarkDjiboutiDominicaDominican RepublicEast TimorEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFaroe IslandsFijiFinlandFranceFrench PolynesiaGabonGambiaGeorgiaGermanyGhanaGreeceGreenlandGrenadaGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKiribatiNorth KoreaSouth KoreaKosovoKuwaitKyrgyzstanLaosLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacedoniaMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMauritaniaMauritiusMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew ZealandNicaraguaNigerNigeriaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestine, State ofPanamaPapua New GuineaParaguayPeruPhilippinesPolandPortugalPuerto RicoQatarRomaniaRussiaRwandaSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSpainSri LankaSudanSudan, SouthSurinameSwazilandSwedenSwitzerlandSyriaTaiwanTajikistanTanzaniaThailandTogoTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVatican CityVenezuelaVietnamVirgin Islands, BritishVirgin Islands, U.S.YemenZambiaZimbabwe

Select your industryAd AgenciesAgricultureApparelAutomotiveBiotechnologyChemicalsConstructionConsultingConsumer GoodsEducationEnergyEngineeringEntertainmentEnvironmentalFinance & BankingFood & BeverageGovernmentHealth CareHospitalityInsuranceManufacturingMediaNot For ProfitRecreationRetailSecurityServicesTechnologyTelecommunicationsTransportationTravel and LeisureUtilitiesWholesaleOther (please specify)

Privacy Policy

By submitting this form to MIT SMR, you acknowledge that your name and contact information will be shared with SAS Institute Inc., which may contact you regarding the content.

By submitting this form to MIT SMR, you acknowledge that your name and contact information will be shared with SAS Institute Inc., which may contact you regarding the content.

This field is for validation purposes and should be left unchanged.

See the original post here:
Free Webinar | Machine Learning and Data Analytics in the Pandemic Era - MIT Sloan

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.

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

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.

Share article on social media or email:

See the original post here:
PathAI and Gilead Report Data from Machine Learning Model Predictions of Liver Disease Progression and Treatment Response at AASLD's The Liver Meeting...

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.

To continue reading this article, click here.

Read the rest here:
AI Recognizes COVID-19 in the Sound of a Cough Machine Learning Times - The Predictive Analytics Times