Advertisements
Feeds:
Posts
Comments

Archive for the ‘Artificial Intelligence’ Category


R&D for Artificial Intelligence Tools & Applications: Google’s Research Efforts in 2018

Reporter: Aviva Lev-Ari, PhD, RN

 

Looking Back at Google’s Research Efforts in 2018

Tuesday, January 15, 2019

2018 was an exciting year for Google’s research teams, with our work advancing technology in many ways, including fundamental computer science research results and publications, the application of our research to emerging areas new to Google (such as healthcare and robotics), open source software contributions and strong collaborations with Google product teams, all aimed at providing useful tools and services. Below, we highlight just some of our efforts from 2018, and we look forward to what will come in the new year. For a more comprehensive look, please see our publications in 2018.

SOURCE & VIDEOS

Perspectives on AI

  • Ethical Principles and AI
  • AI for Social Good
  • Research Outreach
  • New Places, New Faces
  • Looking Forward to 2019

Theoretical Innovations in AI

  • Algorithms and Theory
  • Software Systems

R&D for Artificial Intelligence Application @Google in 2018 included the following Application Types:

  • Assistive Technology
  • Quantum computing
  • Natural Language Understanding
  • Perception
  • Computational Photography
  • AutoML
  • Tensor Processing Units (TPUs)
  • Open Source Software and Datasets
  • Robotics

Applications of AI to Other Fields

SOURCES

https://ai.googleblog.com/2019/01/looking-back-at-googles-research.html

Google Publication in 2018

 publications in 2018.
Advertisements

Read Full Post »


Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?

Reporter: Stephen J. Williams, Ph.D.

Boston Healthcare sponsored a Webinar recently entitled ” Role of Informatics in Precision Medicine: Implications for Innovators”.  The webinar focused on the different informatic needs along the Oncology Care value chain from drug discovery through clinicians, C-suite executives and payers. The presentation, by Joseph Ferrara and Mark Girardi, discussed the specific informatics needs and deficiencies experienced by all players in oncology care and how innovators in this space could create value. The final part of the webinar discussed artificial intelligence and the role in cancer informatics.

 

Below is the mp4 video and audio for this webinar.  Notes on each of the slides with a few representative slides are also given below:

Please click below for the mp4 of the webinar:

 

 


  • worldwide oncology related care to increase by 40% in 2020
  • big movement to participatory care: moving decision making to the patient. Need for information
  • cost components focused on clinical action
  • use informatics before clinical stage might add value to cost chain

 

 

 

 

Key unmet needs from perspectives of different players in oncology care where informatics may help in decision making

 

 

 

  1.   Needs of Clinicians

– informatic needs for clinical enrollment

– informatic needs for obtaining drug access/newer therapies

2.  Needs of C-suite/health system executives

– informatic needs to help focus of quality of care

– informatic needs to determine health outcomes/metrics

3.  Needs of Payers

– informatic needs to determine quality metrics and managing costs

– informatics needs to form guidelines

– informatics needs to determine if biomarkers are used consistently and properly

– population level data analytics

 

 

 

 

 

 

 

 

 

 

 

 

What are the kind of value innovations that tech entrepreneurs need to create in this space? Two areas/problems need to be solved.

  • innovations in data depth and breadth
  • need to aggregate information to inform intervention

Different players in value chains have different data needs

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Data Depth: Cumulative Understanding of disease

Data Depth: Cumulative number of oncology transactions

  • technology innovators rely on LEGACY businesses (those that already have technology) and these LEGACY businesses either have data breath or data depth BUT NOT BOTH; (IS THIS WHERE THE GREATEST VALUE CAN BE INNOVATED?)
  • NEED to provide ACTIONABLE as well as PHENOTYPIC/GENOTYPIC DATA
  • data depth more important in clinical setting as it drives solutions and cost effective interventions.  For example Foundation Medicine, who supplies genotypic/phenotypic data for patient samples supplies high data depth
  • technologies are moving to data support
  • evidence will need to be tied to umbrella value propositions
  • Informatic solutions will have to prove outcome benefit

 

 

 

 

 

How will Machine Learning be involved in the healthcare value chain?

  • increased emphasis on real time datasets – CONSTANT UPDATES NEED TO OCCUR. THIS IS NOT HAPPENING BUT VALUED BY MANY PLAYERS IN THIS SPACE
  • Interoperability of DATABASES Important!  Many Players in this space don’t understand the complexities integrating these datasets

Other Articles on this topic of healthcare informatics, value based oncology, and healthcare IT on this OPEN ACCESS JOURNAL include:

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Broad Institute launches Merkin Institute for Transformative Technologies in Healthcare

HealthCare focused AI Startups from the 100 Companies Leading the Way in A.I. Globally

Paradoxical Findings in HealthCare Delivery and Outcomes: Economics in MEDICINE – Original Research by Anupam “Bapu” Jena, the Ruth L. Newhouse Associate Professor of Health Care Policy at HMS

Google & Digital Healthcare Technology

Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

The Future of Precision Cancer Medicine, Inaugural Symposium, MIT Center for Precision Cancer Medicine, December 13, 2018, 8AM-6PM, 50 Memorial Drive, Cambridge, MA

Live Conference Coverage @Medcity Converge 2018 Philadelphia: Oncology Value Based Care and Patient Management

2016 BioIT World: Track 5 – April 5 – 7, 2016 Bioinformatics Computational Resources and Tools to Turn Big Data into Smart Data

The Need for an Informatics Solution in Translational Medicine

 

 

 

 

Read Full Post »


 

Koch Institute 2019 Immune Engineering Symposium

 


January 28-29, 2019, 8AM – 4PM
Kresge Auditorium, MIT

 

Biological, chemical, and materials engineers are engaged at the forefront of immunology research. At their disposal is an analytical toolkit honed to solve problems in the petrochemical and materials industries, which share the presence of complex reaction networks, and convective and diffusive molecular transport. Powerful synthetic capabilities have also been crafted: binding proteins can be engineered with effectively arbitrary specificity and affinity, and multifunctional nanoparticles and gels have been designed to interact in highly specific fashions with cells and tissues. Fearless pursuit of knowledge and solutions across disciplinary boundaries characterizes this nascent discipline of immune engineering, synergizing with immunologists and clinicians to put immunotherapy into practice.

The 2019 symposium will include two poster sessions and four abstract-selected talks. Abstract submission deadline is December 22, 2018. Registration closes December 23rd.

Featuring:

Facundo Batista (Ragon Institute)
Michael Birnbaum (MIT, Koch Institute)
Bryan Bryson (MIT, Department of Biological Engineering)
Yvonne Chen (University of California, Los Angeles)
Stephanie Dougan (Dana-Farber Cancer Institute)
Glenn Dranoff (Novartis Institute for Biomedical Research)
Michael Dustin (University of Oxford)
Phil Greenberg (Fred Hutchinson Cancer Research Center)
Darrell Irvine (MIT, Koch Institute; HHMI)
Tyler Jacks (MIT, Koch Institute; HHMI)
Max Krummel (University of California, San Francisco)
Diane Mathis (Harvard Medical School)
Mikael Pittet (Massachusetts General Hospital)
Aviv Regev (Broad Institute; Koch Institute)
Bob Schreiber (Wash University of St. Louis)
Alex Shalek (MIT, Department of Chemistry, Koch Institute)
Jamie Spangler (John Hopkins University)
Stefani Spranger (MIT, Koch Institute)
Tim Springer (Boston Children’s Hospital)
Melody Swartz (University of Chicago)
Cathy Wu (Massachusetts General Hospital)

Biological, chemical, and materials engineers are engaged at the forefront of immunology research. At their disposal is an analytical toolkit honed to solve problems in the petrochemical and materials industries, which share the presence of complex reaction networks, and convective and diffusive molecular transport. Powerful synthetic capabilities have also been crafted: binding proteins can be engineered with effectively arbitrary specificity and affinity, and multi functional nanoparticles and gels have been designed to interact in highly specific fashions with cells and tissues. Fearless pursuit of knowledge and solutions across disciplinary boundaries characterizes this nascent discipline of immune engineering, synergizing with immunologists and clinicians to put immunotherapy into practice.

 

Read Full Post »


Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

Updated 12/18/2018

In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry.  Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.

More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations.  This is where blockchain technologies may play an important role.

A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:

The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data.  They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare.  In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.

The review discusses:

  1. Recent achievements in next-generation artificial intelligence
  2. Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
  3. Open source blockchain Exonium and its application for healthcare marketplace
  4. A blockchain-based platform allowing patients to have control of their data and manage access
  5. How advances in deep learning can improve data quality, especially in an era of big data

Advances in Artificial Intelligence

  • Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
  • Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:
  1. Prediction of drug properties(2, 3) and toxicities(4)
  2. Biomarker development (5)
  3. Cancer diagnosis (6)
  4. First FDA approved system based on deep learning Arterys Cardio DL
  • Other promising systems of deep learning include:
    • Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
    • Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
    • Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require.  Biomedical imaging datasets are an example of use of transfer learning.
    • One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.

Highly Distributed Storage Systems (HDSS)

The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable.  This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.

Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data.  Each record in the block contains a timestamp and a link to the previous block in the chain.  Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone.  This allows a verifiable, secure, and consistent history of a record of events.

Data Privacy and Regulatory Issues

The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain.  The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article  Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.).  The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard.  The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:

Issues of Privacy Specific In Use of Blockchain to Distribute Health Data

  • Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
  • Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
  • Blockchain uses a consensus algorithm even though end users may have their own privacy key
  • Applied cryptography measures and routines are used to decentralize authentication (publicly available)
  • Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.

 

YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)

 

 

In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.

 

 

 

Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:

References

  1. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget 9, 5665-5690.
  2. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics 13, 2524-2530.
  3. Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research 16, 1401-1409.
  4. Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences 133, 70-78.
  5. Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging 8, 1021-1033.
  6. Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports 7, 45938.
  7. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics 14, 3098-3104.
  8. Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel) 16.

Articles from clinicalinformaticsnews.com

Healthcare Organizations Form Synaptic Health Alliance, Explore Blockchain’s Impact On Data Quality

From http://www.clinicalinformaticsnews.com/2018/12/05/healthcare-organizations-form-synaptic-health-alliance-explore-blockchains-impact-on-data-quality.aspx

By Benjamin Ross

December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.

The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.

Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.

“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”

O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.

“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”

Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.

“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”

Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.

“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”

As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.

“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”

 

US Health And Human Services Looks To Blockchain To Manage Unstructured Data

http://www.clinicalinformaticsnews.com/2018/11/29/us-health-and-human-services-looks-to-blockchain-to-manage-unstructured-data.aspx

By Benjamin Ross

November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.

Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.

HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.

“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”

The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.

“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”

Accelerate distributes the data to the workforce, making it available to them one business process at a time.

“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”

The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.

It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.

“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”

This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.

Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.

“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”

Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.

“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”

Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.

“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.

“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”

 

Shivom is creating a genomic data hub to elongate human life with AI

From VentureBeat.com
Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.

The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.

“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”

While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.

Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.

“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”

Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.

Rise in Population Genomics: Local Government in India Will Use Blockchain to Secure Genetic Data

Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.

Andhra Pradesh, DNA, and blockchain

Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.

With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.

Other Articles in this Open Access Journal on Digital Health include:

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

 

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

 

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

 

Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

 

Digital Health Breakthrough Business Models, June 5, 2018 @BIOConvention, Boston, BCEC

 

 

 

 

 

 

Read Full Post »


Heart Murmur Detection done by AI Algorithm (Eko Core and Eko Duo) Devices Outperform most Auscultatory Skills of Cardiologists

Reporter: Aviva Lev-Ari, PhD, RN

 

AI Algorithm Outperforms Most Cardiologists in Heart Murmur Detection

Eko’s heart murmur detection algorithm outperformed four out of five cardiologists in recent clinical study

“Artificial Intelligence Detects Pediatric Heart Murmurs With Cardiologist-Level Accuracy,” the study demonstrates the power of machine learning and artificial intelligence (AI) to enhance cardiac care.

The neural network AI algorithm was trained on thousands of heart sound recordings. The algorithm was then tested on an independent dataset of pediatric heart sounds and compared to gold-standard echocardiogram imagery. Five pediatric cardiologists also listened to the heart sound recordings and independently made a determination whether a recording contained a murmur. This advancement will help narrow the clinical skill gap between the 27,000 cardiologists in the U.S. — the experts at murmur detection — and the 3.8 million other clinicians who are less experienced in the identification of heart murmurs through a stethoscope.

A study published in the Journal of the American Medical Association revealed that, on average, internal medicine and family practice physician residents misdiagnose 80 percent of common cardiac events.1 Cardiologists on the other hand, can effectively diagnose 90 percent of cardiac events using a stethoscope.2

Eko’s murmur screening algorithm, when coupled with the company’s U.S. Food and Drug Administration (FDA)-cleared Eko Core and Eko Duo devices, will enable any and all clinicians to more accurately screen for heart murmurs.

Eko is currently pursuing FDA clearance for the algorithm and will be rolling it out with its existing cardiac monitoring devices upon securing regulatory clearance.

For more information: http://www.ekohealth.com

References

1. Mangione S., Nieman L.Z. Cardiac auscultatory skills of internal medicine and family practice trainees. A comparison of diagnostic proficiency. Journal of the American Medical Association, Sept. 3, 1997. doi:10.1001/jama.1997.03550090041030

2. Thompson W.R. In defence of auscultation: a glorious future? Heart Asia, Feb. 1, 2017. doi:  [10.1136/heartasia-2016-010796]

 

SOURCE

https://www.dicardiology.com/content/ai-algorithm-outperforms-most-cardiologists-heart-murmur-detection?eid=333021707&bid=2308309

Read Full Post »


HOTTEST Artificial Intelligence Hub: Israel’s High Tech Industry – Why?

Reporter: Aviva Lev-Ari, PhD, RN

 

(See here for an interactive version of the infographic above).

Why is this a Reality?

https://www.forbes.com/sites/gilpress/2018/09/24/the-thriving-ai-landscape-in-israel-and-what-it-means-for-global-ai-competition/#577a107330c5

https://hackernoon.com/israels-artificial-intelligence-landscape-2018-83cdd4f04281

 

The Voice of Gil Press on Global AI Competition in Forbes:

China is beating the US in AI and Europe’s AI experts are challenging both US and China, but a new contender for AI leadership is rapidly emerging. The startup nation of Israel is targeting today’s hottest tech sector by deploying its expertise in cutting-edge data analysis, software and hardware engineering talent, and proven entrepreneurial skills.

https://www.forbes.com/sites/gilpress/2018/09/24/the-thriving-ai-landscape-in-israel-and-what-it-means-for-global-ai-competition/#577a107330c5

Top 10 Hot Artificial Intelligence (AI) Technologies

https://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/#ceb2f7419287

Read Full Post »


Reactions to Original Tweets by @Pharma_BI and by @AVIVA1950 from #BIO2018

Curator: Aviva Lev-Ari, PhD, RN

 

Please review:

  • Re-Tweets and Likes by @Pharma_BI and by @AVIVA1950 from #BIO2018 @IAmBiotech @BIOConvention – BIO 2018, Boston, June 4-7, 2018, BCEC

https://pharmaceuticalintelligence.com/2018/06/11/re-tweets-and-likes-by-pharma_bi-aviva1950-from-bio2018-iambiotech-bioconvention-bio-2018-boston-june-4-7-2018-bcec/

 

  • Original Tweets by @Pharma_BI and by @AVIVA1950 from #BIO2018 @IAmBiotech @BIOConvention – BIO 2018, Boston, June 4-7, 2018, BCEC

https://pharmaceuticalintelligence.com/2018/06/11/original-tweets-by-pharma_bi-and-by-aviva1950-from-bio2018-iambiotech-bioconvention-bio-2018-boston-june-4-7-2018-bcec/

 

  • Thriving at the Survival Calls during Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL

https://pharmaceuticalintelligence.com/2018/06/11/thriving-at-the-survival-calls-during-careers-in-the-digital-age-an-age-like-no-other-also-known-as-digital/

 

     liked a Tweet you were mentioned in

    4h4 hours ago

  1. Gail Thornton Retweeted @pharma_BI

    From Philip K Dick’s obtuse robots to Mark O’Connell’s guide to transhumanism, novelist Julian Gough picks essential reading for a helter skelter world. Can’t wait to read some of the top 10 books. to survive the digital age!

    Gail Thornton added,

     liked your Tweet

    Jun 8

    In case you missed Gail Thornton‘s Tweet

    Jun 8

  2. Gail Thornton Retweeted Aviva Lev-Ari

    Gail Thornton added,

     Retweeted your Tweet

    Jun 8

  3.  liked your Tweets

    Jun 8

    3 other likes

     liked your Tweet

    Jun 7

     liked your Tweet

    Jun 7

     liked your Tweet

    Jun 7

     Retweeted your Tweet

    Jun 7

     liked your Tweet

    Jun 7

  4. Replying to  

    Thanks to Pete Pellerito for keeping the academic zone going . It was great.

     liked your Tweet

    Jun 7

     liked your Tweet

    Jun 7

  5. Replying to   and 2 others

    👊👏🙏

     liked your reply

    Jun 7

     liked your Retweet

    Jun 7

  6.  Retweeted your Retweets

    Jun 6

    10 other Retweets

     Retweeted a Tweet you are mentioned in

    Jun 6

  7.  liked your Tweets

    Jun 6

    2 other likes

  8.  Retweeted your Tweet

    Jun 6

    1 other Retweet

     Retweeted your Tweet

    Jun 6

  9.  liked your Retweets

    Jun 6

    2 other likes

     Retweeted your Retweet

    Jun 6

     and  liked your Tweet

    Jun 6

     and  liked your Tweet

    Jun 6

     and  liked your Tweet

    Jun 6

     liked your Tweet

    Jun 6

     Retweeted your Tweet

    Jun 6

     liked your Tweet

    Jun 6

     liked your Tweet

    Jun 5

     and  liked a Tweet you were mentioned in

    Jun 5

    Recent Tweet from Gail Thornton

    Jun 5

  10. Translate Tweet

  11.  liked Tweets you were included in

    Jun 5

    1 other like

  12.  liked your Tweets

    Jun 5

    7 other likes

     liked your Tweet

    Jun 5

     Retweeted your Tweet

    Jun 4

     Retweeted your reply

    Jun 4

     liked your reply

    Jun 4

     liked your reply

    Jun 4

     liked your reply

    Jun 4

  13.  liked your Tweets

    Jun 4

    1 other like

     liked your Tweet

    Jun 3

Read Full Post »

Older Posts »