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.
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.
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.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
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.
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.
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.
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.
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.
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.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
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.
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.
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.
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 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.
This presentation shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
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.
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 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 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.
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.
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.
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.
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.
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.
This presentation covers the agenda 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.
Festival of Genomics 2016 London: Real-time Exploration of the Cancer Genome,...Matthieu Schapranow
This presentation covers the NCT presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
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.
Festival of Genomics 2016 London: Analyze Genomes: Real-world ExamplesMatthieu Schapranow
This presentation covers the "Analyze Genomes: Real-world Examples" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
- The document discusses building a digital health ecosystem in Africa using mobile technology to transform healthcare delivery. It describes how patient monitoring solutions using digital devices can generate savings for hospitals by reducing readmissions for chronic diseases.
- The medopad platform is presented as an integrated digital health solution that can enable real-time patient monitoring, care coordination between patients and providers, and clinical research across different diseases like cardiology, oncology and diabetes.
- Examples of pilot programs using medopad in cancer and cardiology care demonstrate improved outcomes and cost savings. The platform aims to connect the global healthcare community to enhance care in developing countries.
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 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.
This presentation shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
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.
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 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 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.
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.
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.
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.
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.
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.
This presentation covers the agenda 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.
Festival of Genomics 2016 London: Real-time Exploration of the Cancer Genome,...Matthieu Schapranow
This presentation covers the NCT presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
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.
Festival of Genomics 2016 London: Analyze Genomes: Real-world ExamplesMatthieu Schapranow
This presentation covers the "Analyze Genomes: Real-world Examples" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
- The document discusses building a digital health ecosystem in Africa using mobile technology to transform healthcare delivery. It describes how patient monitoring solutions using digital devices can generate savings for hospitals by reducing readmissions for chronic diseases.
- The medopad platform is presented as an integrated digital health solution that can enable real-time patient monitoring, care coordination between patients and providers, and clinical research across different diseases like cardiology, oncology and diabetes.
- Examples of pilot programs using medopad in cancer and cardiology care demonstrate improved outcomes and cost savings. The platform aims to connect the global healthcare community to enhance care in developing countries.
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.
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.
Patient-generated data is health-related data created by patients to help manage their condition, including symptoms, medication adherence, and biometric data from wearable devices. This data is distinct from clinical data as it is recorded by patients outside of healthcare settings. Technologies allow widespread collection of patient data to improve monitoring and research. However, ensuring high quality, standardized data sharing while protecting patient privacy and engaging patients requires governance plans and may require significant resources from patient organizations.
Cancer is a dangerous ailment that influences any part of the body and could produce malignant tumors. One feature of cancer is that abnormal cells create quickly and expand beyond their regular bounds. This could attack various parts of the human body and spread to other organs, which is the primary cause of cancer death. Cancer is becoming a more serious worldwide health concern. In the face of these threats, advanced technologies such as Artificial Intelligence (AI), cognitive systems, and the Internet of Things (IoT) may be insufficient to prevent, predict, diagnose, and treat cancer. Digital Twins (DT) with a combination of IoT, AI, cloud computing, and communications technologies such as 5G and 6G have the potential to significant reduce serious cancer threats. Observing data from DT populations may aid in the improvement of some cancer screening, prediction, prevention, detection, treatment, and research investment strategies. Applications of DT medicine specifically cancer, have been studied and analyzed in this paper using both conceptual and statistical analyses. This paper also shows a tree of some ailments where DT is applicable in their study. To the best of our knowledge, there is no literature research on various illnesses and DT specifically cancer disorders. To show the potential of DT, development hurdles of utilizing DT in cancer diseases are discussed, and then, several open research directions will be explained.
Sucessful Healthcare Organizations will be Data DrivenMichelle Blackmer
The document discusses how healthcare organizations are becoming increasingly data-driven. It notes that there is an estimated 50 petabytes of healthcare data, much of which is unstructured, and stored across hundreds of different sources like medical images and lab results. Integrating medical devices with electronic health records could save over $30 billion per year while improving patient care. However, only a third of hospitals currently integrate devices with EHRs. The large amount of data from various sources presents challenges around data quality, fragmentation, accuracy, and security. Healthcare organizations are increasingly relying on data and analytics to support population health, deliver best practices, increase patient engagement, and move from volume-based to value-based care. Clean, connected, and secure data
I have framed this talk to encourage Pharmacy students to embrace computing in general, and data science and artificial intelligence techniques in particular. The reason is that data-driven science has overtaken traditional lab science; chemistry and biology that underlie pharmacy have become data-driven sciences, and a significant majority of the new jobs in pharma industries demand data analysis skills. Increasingly, traditional bioinformatics approaches are being complemented or replaced by machine learning or deep learning algorithms, especially for cases that have large data sets. I will provide a few examples (e.g., drug discovery, finding adverse drug reactions and broadly pharmacovigilance, and selecting patients for clinical trials) to demonstrate how big data and/or AI are indispensable to pharma research and industry today.
Harnessing Big Data and Artificial Intelligence for Pharmacovigilance in Prec...ClinosolIndia
Precision medicine, an innovative approach tailoring medical treatment to individual characteristics, holds great promise for improved patient outcomes. In this paradigm, pharmacovigilance plays a crucial role in monitoring and ensuring the safety of personalized treatments. The integration of big data and artificial intelligence (AI) into pharmacovigilance practices becomes paramount for handling the complexities of individualized therapies and identifying potential safety concerns. This article explores the synergies between big data, AI, and pharmacovigilance in the context of precision medicine.
This document discusses how machine learning can be applied to help plastic surgeons better analyze and interpret the large amounts of patient data that are now routinely collected. It begins by explaining that traditional data analysis techniques struggle with "big data," which contains complex patterns. Machine learning, a subfield of artificial intelligence, can generate algorithms capable of acquiring knowledge from historical examples to help address this challenge. The document then provides examples of how machine learning has already been successfully applied in other fields and in cancer treatment. It proposes that plastic surgeons should also look to machine learning approaches to more efficiently deliver healthcare and improve surgical outcomes by extracting meaningful insights from their extensive patient data collections. Specific potential applications discussed include burn surgery, microsurgery, and various types of reconstruct
Presentation carried out during the EMBC'16 conference in Orlando the 17th of August by Paulo Carvalho and Vicente Traver introducing the LINK project and the results of the first iteration with experts about the future opportunities and challenges for research on personalised health care for cardiovascular disease management.
H2O World - Machine Learning to Save Lives - Taposh Dutta RoySri Ambati
The document discusses how Kaiser Permanente is using machine learning to develop an early warning system (EWS) to predict unplanned transfers from medical/surgical wards to the intensive care unit (ICU). The EWS, called Advanced Alert Monitoring (AAM), analyzes patient data like vitals, labs, demographics and comorbidities to identify patients at risk of deterioration in the next 12 hours. When AAM exceeds a threshold, clinicians receive a pop-up alert to intervene early and potentially prevent ICU transfers. Kaiser is continuously improving AAM by refining the model and validating predictions to help save lives through integrated, technology-enabled care delivery.
Artificial intelligence has great potential in healthcare, especially for analyzing medical images and aiding clinical decision-making. However, there are also risks like inaccurate data from devices, privacy and security issues, and lack of transparency in AI systems. To address this, the document recommends (1) standards for data collection, testing, and use of AI technologies, (2) collaboration between industry, academia and other stakeholders, and (3) evolving medical education and regulations to foster safe, ethical and responsible development and adoption of artificial intelligence in medicine.
Using Healthcare Data for Research @ The Hyve - Campus Party 2016Kees van Bochove
In this presentation, Kees van Bochove, founder & CEO of The Hyve, a services company in biomedical open source software, presents a number of different types of healthcare data. As an example, he also provides details of a project in which The Hyve participates and which uses that kind of data. Covered are: translational medicine data using tranSMART and cBioPortal, population health data using OMOP and OHDSI, and personal health data processing using open mHealth Shimmer and Apache Kafka.
The 4th paradigm of research is manifest in the rising popularity of data science. Data science developments relevant to human genetics are discussed with particular reference to cloud computing and data accessibility.
American Society for Human Genetics, October 16, 2018, San Diego
AI-based Business Models in Healthcare: An Empirical Study of Clinical Decisi...ICDEcCnferenece
Marija Radic, Claudia Vienken, Laurin Nikschat, Thore Dietrich, Holger König, Lorenz Laderick and Dubravko Radic. AI-based Business Models in Healthcare: An Empirical Study of Clinical Decision Support Systems. (ICDEc 2022)
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
How will AI affect the patient journey of the future?
1. How will AI affect the patient journey of the future?
Dr.-Ing. Matthieu-P. Schapranow
Clinical Trials Europe Conf., Basel, Switzerland
Oct 16, 2019
2. ■ 1998: Founded as public-private partnership in Potsdam, Germany
■ 15+ professors, 600+ B.Sc. & M.Sc. students, 170+ PhD students
■ Since 2017 Digital Engineering Faculty of Potsdam University
■ Digital Health Center: Connected Health, Digital Health & Machine Learning,
Personalized Medicine, In-Memory Computing for Digital Health
Hasso Plattner Institute
Key Facts
How will AI affect the
patient journey of the
future?
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Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
3. ■ Long-lasting digital health expertise and access
to global network of subject-matter experts
■ Database: In-memory database technology
for real-time data analysis
■ Methods: Latest artificial intelligence
and machine learning algorithms optimized
for scalable high-throughput processing
■ High-throughput processing of *omics data and
big medical data modalities
Working Group In-Memory Computing for Digital Health
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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5. How will AI affect the
patient journey of the
future?
Newborn pics
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
5
Personal Risk Score
Chord Blood /
Stem Cells
DNA Repair
CRISPR/Cas 9
Bred or Donated
Organ
Glucose Sensor
Nutrition Sensor
Heart Monitoring /
Implanted Defy
Hearing Aid
PhotobyJensJohnsson
6. ■ Case vignette: 65-year old, male, former smoker, COPD patient
■ How his patient journey looks like?
Patient Journey: Lung Cancer in 2024
Screening, Diagnosis, Treatment, and Prevention
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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8. Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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In-Memory Computing Platform
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
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources
9. ■ Personal risk score based on patient anamnesis, e.g. COPD or
former smoker, regularly calculated by algorithms
■ à Regular check-ups supported through direct notifications, e.g.
annual respiratory screening recommended
Patient Journey: Lung Cancer in 2024
Screening
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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10. ■ 150 MEUR national initiative to support adaption of digital health
■ HiGHmed consists of international partners from, e.g. hospitals,
academia, and industry.
■ Clinical Use Cases
□ Oncology
□ Cardiology
□ Infection control
■ Education: Curriculum for experts, e.g. data scientists, data stewards
Our Cooperation Partners:
HiGHmed Consortium
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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11. ■ Lung function test + low-dose CT scan of the lung
■ AI system supports radiologists in detecting lung tissue changes
■ Minimal invasive CTC test reveals cells carrying relevant genetic
changes, e.g. EGFR+ and ERBB2+
■ à Biopsy from lung validated hypothesis
Patient Journey: Lung Cancer in 2024
Diagnosis
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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https://www.plattform-lernende-systeme.de/anwendungsszenario-2.html
12. Use Case: Precision Oncology
Identification of Best Treatment Option for Cancer Patient
■ Diagnosis: Non-Small Cell Lung Cancer (NSCLC), stage IV
■ Markers: EGFR, ERBB2
1. Send tumor sample to laboratory for DNA extraction
2. Sequencing of tumor DNA is possible in <24hrs
3. Analysis involves 1+ TB of raw genome data per sample and takes days
4. Process raw genome data, e.g. alignment, variant calling, and annotate
5. Identify relevant variants using international medical knowledge
6. Decision making requires global medical knowledge, e.g. similar cases
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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13. ■ AI-based therapy support based on, e.g.
□ Clinical guidelines
□ Historic patient cases
□ Latest international medical
knowledge and publications
■ à Fully compliant with latest clinical
guidelines surgery can be performed
Patient Journey: Lung Cancer in 2024
Treatment
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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https://we.analyzegenomes.com/mkc/digiGipfel2018/
14. Use Case: Molecular Tumor Boards
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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■ Multidisciplinary exchange format
for oncologists
■ Incorporates genetic dispositions
■ Focus on data management, e.g.
data retrieval, variant annotations,
case presentations, documentation,
and follow-up
15. ■ Comparison of outcome of similar patients
■ Quantitative real-time analysis of therapy efficiency
■ Assessment of alternative therapy options
■ Break-through: bringing clinical trials to participants
■ à Adjuvant therapy based on specific
combination of chemotherapy is selected
Patient Journey: Lung Cancer in 2024
Treatment
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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https://we.analyzegenomes.com/mkc/digiGipfel2018/
16. Use Case: Medical Process and Knowledge Support
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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■ Identification of cluster of similar patients
■ Real-time analysis of clinical guidelines incorporating patient specifics
17. Use Case: Real-time Assessment of
Clinical Trial Candidates
■ Supports trial design and recruitment process through
statistical data analysis
■ Real-time matching and clustering of patients and
clinical trial inclusion/exclusion criteria
■ Reassessment of already screened or participating
citizens to reduce recruitment costs
How will AI affect the
patient journey of the
future?
Real-time assessment of
clinical trial candidates
17
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
18. ■ Federal Institute of Digital Health Data envisioned:
□ Maintains population data for a healthier society
□ Provides access to subject-matter experts
□ Supports development of innovative DH solutions
■ Data Donation Pass as citizen-facing tool:
□ Enable sovereign use of healthcare data
□ Control access to personal healthcare data
□ Donate de-identified data for research projects
Patient Journey: Lung Cancer in 2024
Prevention
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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https://we.analyzegenomes.com/apps/data-donation-pass/
19. Citizen Participation in Healthcare
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
How will AI affect the
patient journey of the
future?
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PhotobyBenDuchac
Citizen Participation is Key
■ Today, acquisition and processing of fine-grained healthcare data is no longer a limitation
■ Every individual is a valuable source of data, e.g. to better understand population health
■ Digital solutions are not for free, but they facilitate lean processes in healthcare
■ Healthy citizens can help to design innovative and helpful solutions
20. Stay in Contact
Dr.-Ing. Matthieu-P. Schapranow
Group Leader & Scientific Manager Digital Health Innovations
Hasso Plattner Institute
Digital Health Center
Rudolf-Breitscheid-Str. 187
14482 Potsdam, Germany
we.analyzegenomes.com
@AnalyzeGenomes
Schapranow, Clinical
Trials Europe Conference,
Oct 16, 2019
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patient journey of the
future?
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