A Federated In-Memory Database Computing Platform Enabling Real-Time Analysis...Matthieu Schapranow
1) Dr. Schapranow presents a federated in-memory database computing platform called AnalyzeGenomes.com to enable real-time analysis of big medical data.
2) The platform aims to incorporate all available patient data, reference latest lab results and medical knowledge, and support interactive analysis to help clinicians make treatment decisions.
3) It uses a distributed in-memory database across nodes to combine and link heterogeneous medical data sources while addressing challenges of data privacy, locality, and volume.
Gesundheit geht uns alle an: Smart Data ermöglicht passendere Entscheidungen...Matthieu Schapranow
The document discusses how Analyze Genomes provides real-time analysis of big medical data to enable precision medicine. It analyzes diverse data sources, from genomes to clinical trials, using an in-memory database. This allows identifying best treatment options, such as finding no-small cell lung cancer patients the most effective drug. Analyze Genomes also powers related digital health applications and research projects that integrate data from various healthcare partners.
ICT Platform to Enable Consortium Work for Systems Medicine of Heart FailureMatthieu Schapranow
The slide deck "ICT Platform to Enable Consortium Work for Systems Medicine of Heart Failure" was presented on Oct 5, 2016 at the 2016 e:Med Meeting on Systems Medicine in Kiel, Germany.
The given presentation outlines services of the cloud platform "Analyze Genomes" enabling precision medicine. It was presented on the mHealth meets Diagnostics symposium in Berlin on Jun 21, 2016.
This presentation shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
Festival of Genomics 2016 London: Analyze Genomes: A Federated In-Memory Comp...Matthieu Schapranow
This presentation covers the "Analyze Genomes: A Federated In-Memory Computing Platform for Life Sciences" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
A Federated In-Memory Database Computing Platform Enabling Real-Time Analysis...Matthieu Schapranow
1) Dr. Schapranow presents a federated in-memory database computing platform called AnalyzeGenomes.com to enable real-time analysis of big medical data.
2) The platform aims to incorporate all available patient data, reference latest lab results and medical knowledge, and support interactive analysis to help clinicians make treatment decisions.
3) It uses a distributed in-memory database across nodes to combine and link heterogeneous medical data sources while addressing challenges of data privacy, locality, and volume.
Gesundheit geht uns alle an: Smart Data ermöglicht passendere Entscheidungen...Matthieu Schapranow
The document discusses how Analyze Genomes provides real-time analysis of big medical data to enable precision medicine. It analyzes diverse data sources, from genomes to clinical trials, using an in-memory database. This allows identifying best treatment options, such as finding no-small cell lung cancer patients the most effective drug. Analyze Genomes also powers related digital health applications and research projects that integrate data from various healthcare partners.
ICT Platform to Enable Consortium Work for Systems Medicine of Heart FailureMatthieu Schapranow
The slide deck "ICT Platform to Enable Consortium Work for Systems Medicine of Heart Failure" was presented on Oct 5, 2016 at the 2016 e:Med Meeting on Systems Medicine in Kiel, Germany.
The given presentation outlines services of the cloud platform "Analyze Genomes" enabling precision medicine. It was presented on the mHealth meets Diagnostics symposium in Berlin on Jun 21, 2016.
This presentation shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
Festival of Genomics 2016 London: Analyze Genomes: A Federated In-Memory Comp...Matthieu Schapranow
This presentation covers the "Analyze Genomes: A Federated In-Memory Computing Platform for Life Sciences" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
The given slide deck was presented on the 2017 Festival of Genomics in London, UK. It depicts how latest in-memory database technology supports clinicians in finding the best treatment options incorporating genetic data.
Festival of Genomics 2016 London: Analyze Genomes: Modeling and Executing Gen...Matthieu Schapranow
This presentation covers the "Analyze Genomes: Modeling and Executing Genome Data Processing Pipelines" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
The given presentation was presented at SAPPHIRE 2017 in Orlando, FL on May 18, 2017. It highlights latest research results focusing on user-centered in-memory applications for precision medicine.
AnalyzeGenomes.com: A Federated In-Memory Database Platform for Digital HealthMatthieu Schapranow
The slide deck of the presentation "AnalyzeGenomes.com: A Federated In-Memory Database Platform for Digital Health" of the 2017 BMBF All Hands Meeting in Karlsruhe are online available now.
This presentation provides a brief overview of how in-memory database technology can be applied to support systems medicine approaches. For that, it shares real-world experiences, e.g. from the SMART project consortium funded by the German Federal Ministry of Education and Research.
Patient Journey in Oncology 2025: Molecular Tumour Boards in PracticeMatthieu Schapranow
The slide deck was presented at the Bio Data World Congress in Basel on Dec. 04, 2019. It shares first results from the work in the HiGHmed consortium on the use case oncology.
Analyze Genomes: A Federated In-Memory Database System For Life SciencesMatthieu Schapranow
1) Dr. Matthieu-P. Schapranow presented on Analyze Genomes, a federated in-memory database system for life sciences.
2) The system aims to provide real-time analysis of big medical data while maintaining sensitive data locally due to privacy and locality restrictions.
3) It incorporates local compute resources by installing worker nodes to process sensitive data locally and store results in local database instances, while being managed as part of a larger federated database system.
The Driver of the Healthcare System in the 21st Century: Real-world Applicati...Matthieu Schapranow
The document discusses the driver of the healthcare system in the 21st century. It describes how patients, clinicians, and researchers interact and how their interactions will change. It also discusses the challenges of distributed and heterogeneous healthcare data sources, and proposes approaches like in-memory databases and real-time analysis of big medical data to address these challenges. Specific examples discussed include analyzing genomes and creating a medical knowledge cockpit to link patient specifics with international healthcare knowledge.
The document proposes a federated in-memory database system for life sciences that addresses the needs of patients, clinicians, and researchers by enabling real-time analysis of big medical data while maintaining data privacy and locality. It describes key actors and a use case in cancer treatment. The proposed solution incorporates local compute resources through a federated in-memory database with a cloud service provider managing shared algorithms and master data, while sensitive patient data resides locally.
Slides of the 2015 Bio Data World Congress show how our analyzegenomes.com services are combined to support precision medicine in the context of modern oncology treatment.
The given presentation showcases examples of how artificial intelligence technology can be used to improve the patient journey in the specific medical field of oncology.
Algorithmen statt Ärzte: Algorithmen statt Ärzte: Ersetzt Big Data künftig ...Matthieu Schapranow
The document discusses whether algorithms will replace doctors in medicine. It notes that healthcare costs are rising significantly. While algorithms and health apps promise benefits like improved prevention, quality issues exist if not regulated as medical products. The document explores various use cases where algorithms already augment doctors, such as automatically segmenting tissues in scans. Citizens increasingly demand digital health services and control over their own data. The conclusion is that algorithms and doctors can work together, with algorithms handling routine tasks and doctors focusing on personal care, if challenges around regulation and data protection are addressed.
The document discusses challenges and opportunities presented by big medical data and describes an approach using in-memory technology. It proposes a medical knowledge cockpit that allows interactive exploration of distributed medical data sources. This would facilitate tasks like identifying relevant information for a patient's genes, finding suitable clinical trials, and interactively analyzing drug response data. The goal is to enable personalized medicine through real-time analysis of medical data from various sources.
This presentation shares a 10 minute pitch of big data potentials in the field of life sciences as presented at the 2015 CMS Global Life Science Forum on Nov 9, 2015 in Frankfurt
This presentation covers the final presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
This is the presentation as shown on the 2015 Future Convention in Frankfurt, Germany on Nov 23, 2015. It shows latest research results in the field of precision medicine using the Drug Response Analysis app of the http://we.analyzegenomes.com platform.
What are today's challenges of big medical data and how can we use the immense data to turn it into potentials, e.g. for precision medicine. Get insights in application examples, where big medical data are incorporated and how in-memory database technology can enable it instantaneous analysis.
Festival of Genomics 2016 London: Mining and Processing of Unstructured Medic...Matthieu Schapranow
This presentation covers the "Mining and Processing of Unstructured Medical Data" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
Festival of Genomics 2016 London: Challenges of Big Medical Data?Matthieu Schapranow
This presentation covers the "Challenges of Big Medical Data" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
UCSF Informatics Day 2014 - Wylie Burke, "Bioethical Issues in Genomics and E...CTSI at UCSF
This document discusses several bioethical issues related to genomics and electronic health records. It touches on the challenges of linking disparate health datasets while protecting patient privacy. It also examines the blurring lines between clinical care and research when using individuals' health information. Specifically, it raises questions around informed consent, transparency, justification for using data, and ensuring adequate confidentiality. The document also explores issues of trust in different contexts and public concerns about the use of newborn screening samples in research. Finally, it discusses developing policy around "information commons" and engaging stakeholders to help shape biorepository research standards.
The given slide deck was presented on the 2017 Festival of Genomics in London, UK. It depicts how latest in-memory database technology supports clinicians in finding the best treatment options incorporating genetic data.
Festival of Genomics 2016 London: Analyze Genomes: Modeling and Executing Gen...Matthieu Schapranow
This presentation covers the "Analyze Genomes: Modeling and Executing Genome Data Processing Pipelines" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
The given presentation was presented at SAPPHIRE 2017 in Orlando, FL on May 18, 2017. It highlights latest research results focusing on user-centered in-memory applications for precision medicine.
AnalyzeGenomes.com: A Federated In-Memory Database Platform for Digital HealthMatthieu Schapranow
The slide deck of the presentation "AnalyzeGenomes.com: A Federated In-Memory Database Platform for Digital Health" of the 2017 BMBF All Hands Meeting in Karlsruhe are online available now.
This presentation provides a brief overview of how in-memory database technology can be applied to support systems medicine approaches. For that, it shares real-world experiences, e.g. from the SMART project consortium funded by the German Federal Ministry of Education and Research.
Patient Journey in Oncology 2025: Molecular Tumour Boards in PracticeMatthieu Schapranow
The slide deck was presented at the Bio Data World Congress in Basel on Dec. 04, 2019. It shares first results from the work in the HiGHmed consortium on the use case oncology.
Analyze Genomes: A Federated In-Memory Database System For Life SciencesMatthieu Schapranow
1) Dr. Matthieu-P. Schapranow presented on Analyze Genomes, a federated in-memory database system for life sciences.
2) The system aims to provide real-time analysis of big medical data while maintaining sensitive data locally due to privacy and locality restrictions.
3) It incorporates local compute resources by installing worker nodes to process sensitive data locally and store results in local database instances, while being managed as part of a larger federated database system.
The Driver of the Healthcare System in the 21st Century: Real-world Applicati...Matthieu Schapranow
The document discusses the driver of the healthcare system in the 21st century. It describes how patients, clinicians, and researchers interact and how their interactions will change. It also discusses the challenges of distributed and heterogeneous healthcare data sources, and proposes approaches like in-memory databases and real-time analysis of big medical data to address these challenges. Specific examples discussed include analyzing genomes and creating a medical knowledge cockpit to link patient specifics with international healthcare knowledge.
The document proposes a federated in-memory database system for life sciences that addresses the needs of patients, clinicians, and researchers by enabling real-time analysis of big medical data while maintaining data privacy and locality. It describes key actors and a use case in cancer treatment. The proposed solution incorporates local compute resources through a federated in-memory database with a cloud service provider managing shared algorithms and master data, while sensitive patient data resides locally.
Slides of the 2015 Bio Data World Congress show how our analyzegenomes.com services are combined to support precision medicine in the context of modern oncology treatment.
The given presentation showcases examples of how artificial intelligence technology can be used to improve the patient journey in the specific medical field of oncology.
Algorithmen statt Ärzte: Algorithmen statt Ärzte: Ersetzt Big Data künftig ...Matthieu Schapranow
The document discusses whether algorithms will replace doctors in medicine. It notes that healthcare costs are rising significantly. While algorithms and health apps promise benefits like improved prevention, quality issues exist if not regulated as medical products. The document explores various use cases where algorithms already augment doctors, such as automatically segmenting tissues in scans. Citizens increasingly demand digital health services and control over their own data. The conclusion is that algorithms and doctors can work together, with algorithms handling routine tasks and doctors focusing on personal care, if challenges around regulation and data protection are addressed.
The document discusses challenges and opportunities presented by big medical data and describes an approach using in-memory technology. It proposes a medical knowledge cockpit that allows interactive exploration of distributed medical data sources. This would facilitate tasks like identifying relevant information for a patient's genes, finding suitable clinical trials, and interactively analyzing drug response data. The goal is to enable personalized medicine through real-time analysis of medical data from various sources.
This presentation shares a 10 minute pitch of big data potentials in the field of life sciences as presented at the 2015 CMS Global Life Science Forum on Nov 9, 2015 in Frankfurt
This presentation covers the final presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
This is the presentation as shown on the 2015 Future Convention in Frankfurt, Germany on Nov 23, 2015. It shows latest research results in the field of precision medicine using the Drug Response Analysis app of the http://we.analyzegenomes.com platform.
What are today's challenges of big medical data and how can we use the immense data to turn it into potentials, e.g. for precision medicine. Get insights in application examples, where big medical data are incorporated and how in-memory database technology can enable it instantaneous analysis.
Festival of Genomics 2016 London: Mining and Processing of Unstructured Medic...Matthieu Schapranow
This presentation covers the "Mining and Processing of Unstructured Medical Data" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
Festival of Genomics 2016 London: Challenges of Big Medical Data?Matthieu Schapranow
This presentation covers the "Challenges of Big Medical Data" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
UCSF Informatics Day 2014 - Wylie Burke, "Bioethical Issues in Genomics and E...CTSI at UCSF
This document discusses several bioethical issues related to genomics and electronic health records. It touches on the challenges of linking disparate health datasets while protecting patient privacy. It also examines the blurring lines between clinical care and research when using individuals' health information. Specifically, it raises questions around informed consent, transparency, justification for using data, and ensuring adequate confidentiality. The document also explores issues of trust in different contexts and public concerns about the use of newborn screening samples in research. Finally, it discusses developing policy around "information commons" and engaging stakeholders to help shape biorepository research standards.
The document discusses modern medical technologies including MRI scanners and remote surgery. MRI scanners use strong magnets and radio waves to produce detailed images of the body without exposing patients to radiation. Remote surgery allows surgeons to operate on patients from long distances using robotic arms, enabling life-saving procedures in remote areas. It can be used cooperatively with an assistant surgeon present with the patient or for teaching purposes from a distance.
The document discusses the World Health Organization's (WHO) strategy on traditional medicine. It defines traditional medicine as health practices incorporating plant, animal and mineral-based medicines, spiritual therapies, and exercises used to maintain well-being and treat illness. The WHO strategy from 2002-2005 aims to integrate traditional medicine into national healthcare systems, provide guidance on safety, efficacy and quality, ensure access, and promote rational use. It addresses developing standards for herbal medicines, monitoring safety, protecting traditional knowledge, and training guidelines.
This document discusses emerging technologies in healthcare including:
1) Earlier diagnosis, less invasive treatments, and shorter hospital stays which can lower costs.
2) Demographic changes and rising healthcare costs increasing focus on quality improvement.
3) Emerging technologies like nanotechnology, bionics, regenerative medicine, and mobile/wearable devices that could transform diagnosis, treatment and healthcare delivery.
4) Applications of these technologies including drug delivery, imaging, disease detection, prosthetics and more personalized/preventative care models.
Ayurveda is a traditional system of medicine native to India that is based on balancing the three doshas (bodily humors) of vata, pitta, and kapha. The earliest Ayurvedic texts date back to 1500 BC and are found in Hindu scriptures like the Atharvaveda and Suśruta Saṃhitā. Ayurveda views health as a balance of physical, mental and emotional well-being. Diagnosis evaluates the doshas, and treatments emphasize herbal medicines, yoga, and lifestyle. The goal is to ensure proper functioning of the body's channels to prevent disease.
10 tech trends in healthcare are discussed including:
1. Smartphones have been widely adopted in clinical care and applications leverage smartphone hardware.
2. Wi-Fi adoption has increased with more connected devices on healthcare networks than wired ones.
3. Bring your own device (BYOD) policies are required to manage personal devices on hospital networks.
4. Government mandates have forced investment in IT and applications and have potential for big data analysis.
People are sometimes intimidated by big data because it seems overwhelming and they’re much more familiar with using statistics on survey data or analyzing opinions from focus group data. But here are nine examples from companies like Netflix, Ceasars Entertainment, Walmart, eBay, and UPS, that could have conducted survey or focus group research have instead used big data to accomplish big things.
Processing of Big Medical Data in Personalized Medicine: Challenge or PotentialMatthieu Schapranow
Experience our AnalyzeGenomes.com services at the example of the Medical Knowledge Cockpit and how it can improve the daily work for researchers and physicians.
Introduction to High-performance In-memory Genome Project at HPI Matthieu Schapranow
The document discusses challenges of big data processing for personalized medicine. It describes the vision of using large amounts of diverse medical data like genomes, medical records, clinical trials, and research papers to enable personalized preventative care and more effective therapies for patients. The speaker then outlines their approach using in-memory databases and analytics to enable interactive analysis of this data. Examples discussed include tools for researchers to analyze genomes, clinicians to find comparable patient cases, and patients to identify relevant clinical trials.
Enabling Real-time Genome Data Research with In-memory Database Technology (S...Matthieu Schapranow
Dr. Schapranow presented on using in-memory database technology to enable real-time genome data research. Key points include:
- Current genome analysis takes 4-6 weeks but in-memory technology can accelerate it to minutes or less.
- The High-Performance In-Memory Genome project aims to analyze a patient's full genomic and medical record data during a doctor's visit.
- Tests on SAP HANA showed genome analyses like variant calling and allele counting were 82-600x faster than traditional methods.
- The in-memory approach enables interactive exploration of patient cohorts and pathways to better understand treatment effectiveness.
Enabling Real-Time Genome Data Research with In-Memory Database Technology (I...Matthieu Schapranow
This document discusses enabling real-time genome data research using in-memory database technology. It describes how in-memory databases can perform genomic analyses like variant calling and clustering of patient cohorts much faster than traditional disk-based approaches. The document outlines several research topics like improving data preparation pipelines and integrating genetic pathways. It also presents results showing how an in-memory database loaded a large genome dataset and was able to perform queries 82-600 times faster than conventional tools. The future potential for combining genomic and clinical data in real-time to help researchers, clinicians and patients is also discussed.
Hasso Plattner gave this presentation about how in-memory technology can support analysis of big medical data at the 2013 World Health Summit in Berlin. It consists real-world examples showing latest results of partners, such as the Hasso Plattner Institute, Stanford, Charité, and SAP. For background details, please refer to http://we.analyzegenomes.com
Gaining Time – Real-time Analysis of Big Medical Data SAP Technology
Growing volumes of diverse medical data from sources like genomes, proteomes, clinical records, medical sensors and clinical trials are creating new opportunities for innovation in medicine. SAP HANA is enabling real-time analysis of this big medical data through its ability to process large volumes of data in memory at rapid speeds. This allows for new scenarios like genome variant analysis across large populations in parallel, building proteomics-based cancer diagnostic pipelines interactively, and providing unified access to clinical data from different sources. Multidisciplinary teams combining clinical, research, technical and business expertise are needed to develop new collaborative approaches that are viable and can help drive improvements in areas like personalized healthcare and clinical decision making.
Biothings APIs: high-performance bioentity-centric web servicesChunlei Wu
High performance web service API for gene and genetic variant annotations: MyGene.info and MyVariant.info, And a SDK for building same high-performance API for other biomedical data types ("biothings")
1) In-memory applications are revolutionizing oncology research by enabling fast access and analysis of large amounts of individual patient data, clinical trials data, research findings, and more.
2) Researchers are developing tools that incorporate all available individual patient information, reference latest lab results and medical knowledge, and allow interactive analysis to help clinicians personalize cancer treatment in real time.
3) Key challenges include analyzing and combining distributed heterogeneous medical data sources rapidly. Technologies using in-memory databases aim to address this by enabling analysis of large datasets in seconds.
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...Bonnie Hurwitz
The document discusses extending the iPlant cyberinfrastructure to support microbes in addition to plants. It provides an overview of iPlant, including its funding from NSF, collaborations, resources like data storage and computing platforms, and applications for analysis. Future plans are outlined to build tools and streamline workflows for metagenomics and enable high-throughput computing for microbial data.
IC-SDV 2018: Stefan Geißler (Expert System) Navigating to new shores: the Bio...Dr. Haxel Consult
We present the latest developments around the Biopharma Navigator, a consolidated large search, analysis and reporting application for tens of millions of biomedical documents. In its latest version the application has expanded to include yet more document sources, is offering real-time data-driven dashboards, an enhanced API that facilitates integration into third-party environments, advances in expert identification, the extension of the pharmacovigilance approach to new sources from news and social media as well as live extension of drug name repositories and clinical trial monitoring.
The Biopharma Navigator is used by a growing number of experts in the industry for their daily analyses and can be employed either on a simple subscription basis or with an on-premise installation. The Biopharma Navigator is our answer for the question how big data, cognitive computing analysis and intuitive webfrontends can be combined to provide broad and up-to-date information access to Life Science professionals.
Open PHACTS provides a single access point for integrating multiple biomedical data resources. It has transitioned from an EU project to the Open PHACTS Foundation to sustain the platform long-term. Challenges included addressing licensing issues across different data sources and enabling maximum dissemination. Usage has grown to over 500 million queries. The Foundation is pursuing collaboration, grants, and industry partnerships to support ongoing development and new projects. It welcomes contributions to improve services and develop new data and workflows.
Data science technology is important for better marketing. Many companies uses data to analyze their marketing strategies and create new advertisement.
The document introduces Tag.bio as a low-code analytics application platform built from interconnected data products in a data mesh architecture. It consists of data, algorithms, and analysis apps contributed by different groups - data engineers, data scientists, and domain experts. The platform can integrate various data sources and enable collaboration between groups. It then provides demos of the Tag.bio developer studio and data portal. Key capabilities discussed include integration with AWS services like AI/ML and HealthLake, as well as security features like confidential computing. Example use cases presented are for clinical trials, healthcare, life sciences, and universities.
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
The document summarizes Dr. Matthieu-P. Schapranow's presentation at the Festival of Genomics in Boston on turning big medical data into precision medicine. It describes an in-memory database approach that enables real-time analysis of heterogeneous medical data sources. This allows clinicians and researchers to interactively explore patient data, clinical trials, pathways, and literature to obtain personalized treatment recommendations. The system was designed using a human-centered methodology to ensure usability, effectiveness, and feasibility for precision medicine applications.
Presented by Radu Craioveanu, Director of Software Development, Clinical Systems, Fresenius Medical Care at MongoDB Evenings New England 2017.
resenius is a large healthcare enterprise, specializing in dialysis care. Fresenius' 40,000 clinicians and physicians deliver over 100,000 dialysis treatments per day, across 8 time zones. There is significant pressure to improve treatment outcomes, lower costs, expand the patient coverage, and overall become a Value Based Services provider, sharing the risk with the payers, the insurance companies. This pressures requires Fresenius to adapt, change, and leverage technologies and processes that can enable a rapid transformation to Value Based Care. </br></br>Using technologies and partnerships with players such as MongoDB, Red Hat, and others with a similar, open source innovative approach to progress, Fresenius has been able to implement a healthcare platform that is the foundation onto which the business can transform itself.
MongoDB has enabled Fresenius to achieve high availability of systems across multiple data centers, a data lake concept used for predictive analytics and reporting, enhanced messaging capabilities, fast, effective and distributed archiving, rapid application development via MEAN stacks, and Red Hat Open Shift Docker Container ready-to-use persistence.
The State of the Data Warehouse in 2017 and BeyondSingleStore
The document provides an overview of the changing analytic environment and the evolution of the data warehouse. It discusses how new requirements like performance, usability, optimization, and ecosystem integration are driving the adoption of a real-time data warehouse approach. A real-time data warehouse is described as having low latency ingestion, in-memory and disk-optimized storage, and the ability to power both operational and machine learning applications. Examples are given of companies using a real-time data warehouse to enable real-time analytics and improve business processes.
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A Fit-to-Fly PCR Test is a crucial service for travelers needing to meet the entry requirements of various countries or airlines. This test involves a polymerase chain reaction (PCR) test for COVID-19, which is considered the gold standard for detecting active infections. At our travel clinic in Leeds, we offer fast and reliable Fit to Fly PCR testing, providing you with an official certificate verifying your negative COVID-19 status. Our process is designed for convenience and accuracy, with quick turnaround times to ensure you receive your results and certificate in time for your departure. Trust our professional and experienced medical team to help you travel safely and compliantly, giving you peace of mind for your journey.www.nxhealthcare.co.uk
The facial nerve, also known as cranial nerve VII, is one of the 12 cranial nerves originating from the brain. It's a mixed nerve, meaning it contains both sensory and motor fibres, and it plays a crucial role in controlling various facial muscles, as well as conveying sensory information from the taste buds on the anterior two-thirds of the tongue.
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Here are some key objectives of communication with children:
Build Trust and Security:
Establish a safe and supportive environment where children feel comfortable expressing themselves.
Encourage Expression:
Enable children to articulate their thoughts, feelings, and experiences.
Promote Emotional Understanding:
Help children identify and understand their own emotions and the emotions of others.
Enhance Listening Skills:
Develop children’s ability to listen attentively and respond appropriately.
Foster Positive Relationships:
Strengthen the bond between children and caregivers, peers, and other adults.
Support Learning and Development:
Aid cognitive and language development through engaging and meaningful conversations.
Teach Social Skills:
Encourage polite, respectful, and empathetic interactions with others.
Resolve Conflicts:
Provide tools and guidance for children to handle disagreements constructively.
Encourage Independence:
Support children in making decisions and solving problems on their own.
Provide Reassurance and Comfort:
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Reinforce Positive Behavior:
Acknowledge and encourage positive actions and behaviors.
Guide and Educate:
Offer clear instructions and explanations to help children understand expectations and learn new concepts.
By focusing on these objectives, communication with children can be both effective and nurturing, supporting their overall growth and well-being.
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Digital India will need a big trained army of Health Informatics educated & trained manpower in India.
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We look into the evolution of health informatics and its applications in the healthcare industry.
HIMMS TIGER resources are available to assist Health Informatics education.
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Analyze Genomes: A Federated In-memory Database Computing Platform enabling real-time Analysis of Big Medical Data
1. Analyze Genomes: A Federated In-Memory Database Computing
Platform Enabling Real-time Analysis of Big Medical Data
Dr. Matthieu-P. Schapranow
SAPPHIRE, Orlando, USA
May 17, 2016
2. ■ Online: Visit we.analyzegenomes.com for latest research
results, slides, videos, tools, and publications
■ Offline: High-Performance In-Memory Genome Data Analysis:
In-Memory Data Management Research, Springer,
ISBN: 978-3-319-03034-0, 2014
■ In Person: Join us for Intel Tech Talks at SAPPHIRE booth 625 daily!
□ May 17 12.30pm: A Federated In-Memory Database Computing Platform Enabling
Real-time Analysis of Big Medical Data
□ May 18 12.30pm: In-Memory Apps for Next Generation Life Sciences Research
□ May 19 11.30am: In-Memory Apps Supporting Precision Medicine
Where to find additional information?
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
2
3. Indirect Interaction
Direct Interaction
C linician PatientResearcher
Pharm aceutical
Com pany
H ealthcare
Providers
H ospital
Research
Center
Laboratory
Patient
Advocacy
G roup
Intelligent Healthcare Networks in the 21st Century?
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
3
4. Indirect Interaction
Direct Interaction
C linician PatientResearcher
Pharm aceutical
Com pany
H ealthcare
Providers
H ospital
Research
Center
Laboratory
Patient
Advocacy
G roup
Intelligent Healthcare Networks in the 21st Century?
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
4
5. Indirect Interaction
Direct Interaction
C linician PatientResearcher
Pharm aceutical
Com pany
H ealthcare
Providers
H ospital
Research
Center
Laboratory
Patient
Advocacy
G roup
Intelligent Healthcare Networks in the 21st Century!
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
5
6. ■ Patients
□ Individual anamnesis, family history, and background
□ Require fast access to individualized therapy
■ Clinicians
□ Identify root and extent of disease using laboratory tests
□ Evaluate therapy alternatives, adapt existing therapy
■ Researchers
□ Conduct laboratory work, e.g. analyze patient samples
□ Create new research findings and come-up with treatment alternatives
The Setting
Actors in Oncology
Schapranow, SAPPHIRE,
May 17, 2016
6
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
7. IT Challenges
Distributed Heterogeneous Data Sources
7
Human genome/biological data
600GB per full genome
15PB+ in databases of leading institutes
Prescription data
1.5B records from 10,000 doctors and
10M Patients (100 GB)
Clinical trials
Currently more than 30k
recruiting on ClinicalTrials.gov
Human proteome
160M data points (2.4GB) per sample
>3TB raw proteome data in ProteomicsDB
PubMed database
>23M articles
Hospital information systems
Often more than 50GB
Medical sensor data
Scan of a single organ in 1s
creates 10GB of raw dataCancer patient records
>160k records at NCT A Federated In-
Memory Database
Computing Platform
for Big Medical Data
Schapranow, SAPPHIRE,
May 17, 2016
8. Schapranow, SAPPHIRE,
May 17, 2016
Our Approach
Analyze Genomes: Real-time Analysis of Big Medical Data
8
In-Memory Database
Extensions for Life Sciences
Data Exchange,
App Store
Access Control,
Data Protection
Fair Use
Statistical
Tools
Real-time
Analysis
App-spanning
User Profiles
Combined and Linked Data
Genome
Data
Cellular
Pathways
Genome
Metadata
Research
Publications
Pipeline and
Analysis Models
Drugs and
Interactions
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources
9. Combined column
and row store
Map/Reduce Single and
multi-tenancy
Lightweight
compression
Insert only
for time travel
Real-time
replication
Working on
integers
SQL interface on
columns and rows
Active/passive
data store
Minimal
projections
Group key Reduction of
software layers
Dynamic multi-
threading
Bulk load
of data
Object-
relational
mapping
Text retrieval
and extraction engine
No aggregate
tables
Data partitioning Any attribute
as index
No disk
On-the-fly
extensibility
Analytics on
historical data
Multi-core/
parallelization
Our Technology
In-Memory Database Technology
+
++
+
+
P
v
+++
t
SQL
x
x
T
disk
9
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
10. Where are all those Clouds go to?
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
10
Gartner's 2014 Hype Cycle for Emerging Technologies
11. ■ Requirements
□ Real-time data analysis
□ Maintained software
■ Restrictions
□ Data privacy
□ Data locality
□ Volume of “big medical data”
■ Solution?
□ Federated In-Memory Database System vs. Cloud Computing
Software Requirements in Life Sciences
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
11
12. Approach I:
Multiple Cloud Service Providers
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
12
Local System
C loud
Synchronization
Service
R
Local Storage
Local
Synchronization
Service
R
Shared
C loud
Storage
Site A
Local System
R
Local Storage
Local
Synchronization
Service
Site B
C loud
Synchronization
Service
Shared
C loud
Storage
R
Cloud Provider
Site A
C loud Provider
Site B
13. Approach II:
A Single Service Provider
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
13
Cloud
Synchronization
Service
Shared
Cloud
Storage
Site A Site BCloud Provider
Cloud System
R R
14. Multiple Sites Forming the
Federated In-Memory Database System (FIMDB)
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
14
Federated In-M em ory D atabase System
M aster Data and
Shared Algorithm s
Site A Site BCloud Provider
Cloud IM D B
Instance
Local IM DB
Instance
Sensitive D ata,
e.g. Patient Data
R
Local IM DB
Instance
Sensitive Data,
e.g. Patient D ata
R
15. FIMDB: Cloud Service Provider
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
15
Site B
Federated In-M em ory
D atabase Instance,
Algorithm s, and
Applications M anaged
by Service Provider
CloudService
Provider
Site A
FIMDB
A.1
FIMDB
A.2
FIMDB
A.3
FIMDB
A.4
FIMDB
A.5
FIMDB
B.1
FIMDB
B.2
FIMDB
B.3
FIMDB
C.1
Federated In-M em ory
Database Instances
M aster Data
M anaged by
Service Provider
Sensitive D ata
reside at Site
■ Change of cloud computing paradigm:
Transfer (small) algorithms to (big) data
■ In-Memory Database (IMDB)
□ Landscape of IMDB nodes
□ Stored IMDB procedures and algorithms
□ Master data for applications
■ In-Memory File System (IMDBfs)
□ Integration of file-based tools
□ Managed services directory
□ OS binaries compiled and statically linked for
individual platforms
16. 1. Establish site-to-site VPN connection b/w site and cloud service
provider
2. Mount remote services directory
3. Install and configure local IMDB instance from services directory
4. Subscribe to and configure selected managed services
FIMDB: Setup of a New Client
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
16
17. ■ Data partitioning protects sensitive data by
storing it on local hardware resources only
■ Supports parallel query execution, i.e. reduced
processing time
■ Efficient use of existing hardware resources
FIMDB: Incorporating Local Compute Resources
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
17
18. ■ Brings algorithms to data
■ Forms a single database across individual sites and locations
■ Master data managed by service provider whilst sensitive data resides locally
What to Take Home?
Test it Yourself: AnalyzeGenomes.com
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
18
Pros Cons
Single database license Complex operation
Easy to consume services Time-consuming infrastructure setup
Query propagation by IMDB
Only a single source of truth
19. ■ Online: Visit we.analyzegenomes.com for latest research
results, slides, videos, tools, and publications
■ Offline: High-Performance In-Memory Genome Data Analysis:
In-Memory Data Management Research, Springer,
ISBN: 978-3-319-03034-0, 2014
■ In Person: Join us for Intel Tech Talks at SAPPHIRE booth 625 daily!
□ May 17 12.30pm: A Federated In-Memory Database Computing Platform Enabling
Real-time Analysis of Big Medical Data
□ May 18 12.30pm: In-Memory Apps for Next Generation Life Sciences Research
□ May 19 11.30am: In-Memory Apps Supporting Precision Medicine
Where to find additional information?
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
19
20. Keep in contact with us!
Dr. Matthieu-P. Schapranow
Program Manager E-Health & Life Sciences
Hasso Plattner Institute
August-Bebel-Str. 88
14482 Potsdam, Germany
schapranow@hpi.de
http://we.analyzegenomes.com/
Schapranow, SAPPHIRE,
May 17, 2016
A Federated In-
Memory Database
Computing Platform
for Big Medical Data
20