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.
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.
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 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.
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.
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 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.
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.
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 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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
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.
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.
Research & Reviews: Journal of Oncology and Hematology vol 5 issue 3STM Journals
Research & Reviews: Journal of Oncology and Hematology (RRJoOH) is focused towards the publication of current research work carried out at all the major research centers in the fields of Oncology and Haematology.
Focus and Scope Covers
Oncology
Radiation Oncology.
Surgical Oncology.
Medical Oncology.
Interventional Oncology.
Gynecologic Oncology.
Pediatric Oncology.
Allergooncology.
Clinical Oncology.
Ocular Oncology.
Hematology
Erythrocytes.
Leukocytes and Hematopoiesis.
Hemostasis.
Thrombosis and Vascular Biology.
Hematological Malignancies,
Transplantation.
Cell Therapy.
The document discusses changing market dynamics in the oncology industry. Specifically, it notes that:
1) The oncology market has historically seen significant growth but signs of change are emerging, as new drugs and indications are crowding the market and payors are taking a more aggressive role in managing drug utilization.
2) Evidence suggests payors are using traditional utilization management tools like tiered co-payments, prior authorization, and step therapy more aggressively. Industry is also responding with price caps and pay-for-performance arrangements.
3) With the cost of newer targeted cancer therapies higher than traditional chemotherapy drugs, industry participants need to prepare for a new market dynamic with greater influence from payors.
The document provides an overview of oncologic emergencies including spinal cord compression, brain metastases, superior vena cava obstruction, febrile neutropenia, and hypercalcemia. For spinal cord compression, it discusses signs/symptoms, workup with MRI, treatment with corticosteroids and radiotherapy, and prognosis. It notes surgery plus radiotherapy is better than radiotherapy alone based on the Patchell 2005 study. For brain metastases, it outlines presentation, workup with imaging, treatment with steroids, radiotherapy, and stereotactic radiosurgery based on studies showing better outcomes than whole brain radiotherapy alone.
Principles of Oncology discusses the study, diagnosis, and treatment of tumors (neoplasms). It defines key terms like benign and malignant, carcinomas and sarcomas, and describes methods of examining and categorizing tumors microscopically and visually. Imaging, biopsies, and tumor markers are used to diagnose cancers before discussing common treatment techniques like surgery, chemotherapy, radiation therapy, and immunotherapy.
Tracxn Research — Immuno-Oncology Landscape, September 2016Tracxn
In 2015/16, five startups in this space — Stemcentrx, Gritstone Oncology, Hengrui Therapeutics, and Zai Lab secured big ticket funding rounds of $100 million and above.
The document provides an overview of cancer biology, including key terminology, epidemiology, etiology, prevention, screening, diagnosis, staging, treatment, and biomarkers. It defines various types of cancers and neoplasms, describes the cellular and genetic events leading to cancer development, and outlines the general principles and goals of cancer treatment, which may include surgery, chemotherapy, radiation therapy, and palliative care.
Austin Oncology is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Oncology.
The journal aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all the areas of Oncology. Austin Oncology accepts original research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of oncology.
Austin Oncology strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing, mutual promotion of multidisciplinary science.
The document discusses in-memory applications for informed patients using precision medicine. It describes the Hasso Plattner Institute which develops these applications using in-memory technology. The applications include real-time processing of medical sensor data, statistical analysis of drug side effects, and medical knowledge cockpits for patients and clinicians to access up-to-date research and clinical trials. The goal is to empower patients to better understand their health conditions and treatments.
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.
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.
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.
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
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.
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.
Temporal relations in queries of ehr data for researchWolfgang Kuchinke
Temporal Relations in Queries of Electronic Patient Records. Our main scenario covers the patient identification and recruitment process for clinical trials. For this purpose an extension of the EHR4CR workbench to support patient recruitment was created. This workbench covers following requirements:
Need for built-in privacy protection.
Patient identification and recruitment tracking.
Availability at clinical sites in the form of a workbench with an user-friendly interface.
Each participating clinical site has its own installation only used locally.
Ability to generate queries with temporal relations and constraints for eligibility criteria to find candidate patients.
Our development is based on the fact that queries in EHRs often have a temporal component. But available user interfaces allow only the generation of simple queries with basic temporal relations. Time points and time intervals are therefore the main concepts that must be considered. Time points are related to instantaneous events (e.g. a single myocardial infarction), or to situations lasting for a span of time (e.g. a drug therapy for 2 weeks). Intervals can be represented using time points by their upper and lower temporal boundaries: the start and end. Temporal relations (e.g before, after) can be expressed via additional anchors. The dates of these anchor events can be retrieved and event dates relative to an anchor event can be calculated. EHR4CR decided to build its workbench upon a simple, time-stamp database concept. To each patient’s attribute a time-stamp, which corresponds to the time of the attribute’s occurrence was assigned. The processing of temporal intervals is necessary for EHR4CR since many questions dealing with inclusion / exclusion criteria often involve complex temporal periodes. A graphical interface to use boxes for querying with temporal relations was therefore created. The idea is that the easiest way to specify temporal operators is with an user interface based on the combination of boxes. Temporal operators based on Allen’s algebra were included. Expressions are displayed as graphic boxes and combined by
operators. Events are specified and a temporal operator selected from a predefined list.
Temporal relations in queries of ehr data for researchWolfgang Kuchinke
Temporal Relations in Queries of Electronic Patient Records. The main usage scenario for queries covers patient identification and recruitment process for clinical trials. For this purpose an extension of the EHR4CR workbench to support patient recruitment was created.
This workbench covers following requirements: Need for built-in privacy protection. Patient identification and recruitment tracking tools have been made available to the clinical sites in the form of the workbench. Each participating clinical site has its own installation only used locally (patient data don't leave the hospital).
One important requirement for the workbench is the ability to generate queries with temporal relations and constraints for eligibility criteria.
Queries in EHRs often have a temporal component. But available user interfaces allow only simple queries with simple temporal expressions. Time points and time intervals are the main concepts that must be considered, being related to instantaneous events (e.g. a single myocardial infarction), or to situations lasting for a time span (e.g. a drug therapy for 2 weeks). Intervals can be represented using time points by their upper and lower temporal boundaries: the start and end time points. Temporal relations (e.g before, after) can be expressed via anchors. The dates of these anchor events can be retrieved and event dates relative to this anchor events can be calculated. EHR4CR decided to build the workbench upon a simple, time-stamp database. To each patient’s attribute a time-stamp, which corresponds to the time of the attribute’s
occurrence was assigned. The processing of temporal intervals was necessary since many questions dealing with inclusion / exclusion criteria often involve complex temporal periods. A graphical interface to use boxes for querying with temporal relations was created to simplify query generation. We think that the easiest way to specify temporal operators is with an user interface based on the combination of boxes. Temporal operators based on Allen’s algebra were included. Expressions are displayed as graphic boxes and combined by operators. Events are specified and a temporal operator selected from a
predefined list.
Big Data Analytics for Treatment Pathways John CaiJohn Cai
This document discusses using real-world big data analytics to understand treatment pathways. It begins by explaining the need for real-world evidence from real-world data to assess effectiveness and outcomes beyond randomized clinical trials. It then describes the volume, variety, and velocity characteristics of real-world big data from sources like claims, EMRs, surveys, and devices. Technical challenges of reconstructing complex patient journeys are discussed. Hadoop and MapReduce are presented as a potential solution by breaking the work into mappers that extract patient data and reducers that organize it into timelines. Examples are given of how this could enable cost, pathway, and outcomes analyses to better inform decision making.
1. Big data is changing lives in healthcare by providing large volumes of diverse data from a variety of sources that can be analyzed to gain new insights.
2. This data includes information from patients, citizens, doctors, ICT systems, clinical trials, and more that can be used for predictive modeling and personalized medicine when combined across domains.
3. Challenges include privacy concerns, developing interoperable systems to integrate diverse data sources, and overcoming the traditional divide between research and clinical practice to apply new insights.
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.
During the last two decades Clinical Decision Support (CDS) standards and technologies have progressed significantly to develop them as more robust and scalable systems. However, the current context of medicine sets high demands in aspects such as interoperability to enable the use of EHR data in CDS systems, the need to establish communication challenges to include the patient as an active component in decision making, collaborative learning and sharing CDS systems across institutional borders, to name a few.
In this thesis I tackle some of these challenges. In particular, I evolve previous conceptual computerized decision support frameworks and I postulate a CDS systems environment where different models interact to enable:
• Secondary use of data for CDS systems: The dissertation presents a model to leverage different developments in data access and standardization of medical information. The result is an openEHR-based Data Warehouse architecture that enables access, standardization and abstraction of clinical data for CDS systems. The architecture allows: a) to access heterogeneous data sources; b) to standardize data into openEHR to grant interoperability of data; and c) to exploit an openEHR repository as a Data Warehouse that allows querying data in a technology-independent format (the Archetype Query Language).
• CDS systems semantic specification: The semantic model proposed exploits the paradigm of Linked Services to unambiguously describe CDS systems in a machine- understandable fashion. This grants ontological descriptions of functional, non- functional and data semantics. These descriptions facilitate to overcome some of the barriers in CDS functionality sharing. In particular, the semantic model proposed allows using expressive queries to discover CDS services in health
III
networks, and analyzing CDS systems interfaces to understand how to interoperate with
them.
• Effective patient-CDS systems interaction: the dissertation proposes a method to
evaluate the communication process between patients and consumer-oriented CDS systems. The method aims for detecting if important human-computer interaction barriers that could lead to negative outcomes are present in CDS systems user interfaces.
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.
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.
Personalized medicine tools for clinical trials - kuchinkeWolfgang Kuchinke
Tools for personalised medicine in clinical trials. ---------
The implementation of clinical trials in personalized medicine is a different way of doing clinical research compared to the standard way of large clinical trials aiming for statistical significance. Personalized medicine uses a medical model that separates people into different groups with medical decisions, practices, drugs, interventions being tailored to the individual patient based on their predicted response. Basis for this approach is the progress of the study of the human genome and its variation over the last two decades. Especially advancement in automated DNA sequencing and PCR and the use of expressed sequence tags (ESTs), cDNAs, antisense molecules, small nterfering RNAs (siRNAs), full-length genes and their expression products and haplotypes.
But adoption of personalized medicine requires an active and flexible and highly integrated infrastructure, which allows joining of many different competences and technologies. We asked the question: can the tools developed for personalized medicine in the p-pedicine project be employed effectively in a clinical trials network to support personalised clinical trials. We conducted an analysis of tool integration and the evaluation tool usage requirements. Based on the survey results, the tendency for clinical trial network ECRIN is to use software as a service in the form as SaaS or ASP. ECRIN data centres will (probably) not install and employ p-medicine tools in one of their data centres. A robust business model for the provision of services and the implementation and employment of tools does not yet exist.
How can the personalized medicine infrastructure p-medicine and the clinical trials network ECRIN gain from each other to allow the conduct of personalized clinical trials?
We suggest a business model, in which personal medicine infrastructures and clinical trials networks exchange their services to gain jointly from each other. Therefore: an integration by reciprocal exchange of services may be the solution. Not only software as a service will be exchanged, but also knowledge, personnel and joint staff trainings.
Personalized medicine tools for clinical trials - KuchinkeWolfgang Kuchinke
Tools for personalised medicine in clinical trials.
The implementation of clinical trials in personalized medicine is a different way of doing clinical research, compared to the standard way of large clinical trials aiming for statistical significance. Personalized medicine uses a medical model that separates people into different groups with medical decisions, practices, drugs, interventions being tailored to the individual patient based on their predicted response. Basis for this approach is the progress of the study of the human genome and its variation over the last two decades. Especially advancements in automated DNA sequencing, PCR technologies and the use of expressed sequence tags (ESTs), cDNAs, antisense molecules, small interfering RNAs (siRNAs).
But the adoption of personalized medicine requires an active and flexible and highly integrated infrastructure, which must allow the joining of many different competences and technologies. We asked the question: can the tools developed for personalized medicine in the p-pedicine project be employed effectively in a clinical trials network to support personalised clinical trials? We conducted an analysis of tool integration and the evaluation of tool usage requirements. Based on the survey results, the tendency for the clinical trial network ECRIN is to use software as a service in the form of SaaS or ASP. ECRIN data centres will (probably) not install and employ p-medicine tools in one of their data centres. A robust business model for the provision of services and the implementation and employment of tools does not yet exist.
How can the personalized medicine infrastructure p-medicine and the clinical trials network ECRIN gain from each other to allow the conduct of personalized clinical trials? We suggest a business model, in which personalized medicine infrastructures and clinical trials networks exchange their services to gain jointly from each other. An integration of networks by reciprocal exchange of services may be the solution. Not only software as a service will be exchanged, but also knowledge, personnel and staff trainings.
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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
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
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.
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
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!
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
2. ■ 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
Dr. Schapranow, HPI,
Aug 12, 2014
2
In-Memory
Applications
Revolutionizing
Oncology Research
3. ■ Motivation: Can we enable clinicians to take their therapy decisions:
□ Incorporating all available specifics about each individual patient,
□ Referencing latest lab results and worldwide medical knowledge, and
□ Interactively during their ward round?
Our Motivation
Make Precision Medicine Come Routine in Real Life
In-Memory
Applications
Revolutionizing
Oncology Research
3
Dr. Schapranow, HPI,
Aug 12, 2014
4. IT Challenges
Distributed Heterogeneous Data Sources
4
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 In-Memory
Applications
Revolutionizing
Oncology Research
Dr. Schapranow, HPI,
Aug 12, 2014
5. Our Methodology
Design Thinking Methodology
Dr. Schapranow, HPI,
Aug 12, 2014
In-Memory
Applications
Revolutionizing
Oncology Research
5
6. Our Methodology
Design Thinking Methodology
Dr. Schapranow, HPI,
Aug 12, 2014
In-Memory
Applications
Revolutionizing
Oncology Research
6
Desirability
■ Leveraging directed customer services
■ Portfolio of integrated services for clinicians, researchers, and patients
■ Include latest research results, e.g. most effective therapies
Viability
■ Enable personalized medicine also in far-off
regions and developing countries
■ Share data via the Internet to get feedback
from word-wide experts (cost-saving)
■ Combine research data (publications,
annotations, genome data) from international
databases in a single knowledge base
Feasibility
■ HiSeq 2500 enables high-coverage
whole genome sequencing in 20h
■ IMDB enables allele frequency
determination of 12B records within <1s
■ Detection of 1 relevant annotation out
of 80M <1s
■ Cloud-based data processing services
reduce TCO
7. 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
7
Dr. Schapranow, HPI,
Aug 12, 2014
In-Memory
Applications
Revolutionizing
Oncology Research
8. Dr. Schapranow, HPI,
Aug 12, 2014
Our Approach
Analyze Genomes: Real-time Analysis of Big Medical Data
8
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge Cockpit
Oncolyzer Clinical Trial
Assessment
Cohort
Analysis
In-Memory Database
Extensions for Life Sciences
Data Exchange,
App Store
Access Control,
Data Protection
Fair Use
Statistical
Tools
Combined and Linked Data
Genome
Data
Cellular
Pathways
Genome
Metadata
Resarch
Publications
Pipeline and
Analysis Models
Real-time
Analysis
App-spanning
User Profiles
Drugs and
Interactions
...
In-Memory
Applications
Revolutionizing
Oncology Research
9. Cloud-based Services for Processing of DNA Data
■ Control center for processing of raw DNA data, such as
FASTQ, SAM, and VCF
■ Personal user profile guarantees privacy of uploaded
and processed data
■ Supports reproducible research process by storing all
relevant process parameters
■ Implements prioritized data processing and fair use, e.g.
per department or per institute
■ Supports additional service, such as data annotations,
billing, and sharing for all Analyze Genomes services
■ Honored by the 2014 European Life Science Award
In-Memory
Applications
Revolutionizing
Oncology Research
Standardized Modeling and
runtime environment for
analysis pipelines
9
Dr. Schapranow, HPI,
Aug 12, 2014
10. Individual Analysis Pipelines
Standardized Modeling and Runtime Environment
■ Easy-to-use graphical modeling of analysis pipelines,
e.g. BPMN-based pipelines for genome data processing
■ Runtime environment for analysis models integrating
IMDB tools as well as any operating processes
■ Optimized for high-throughput processing, i.e.
parallelization across CPU cores as well as distributed
computing across computer systems
■ Implements fail-safe and recoverability using IMDB
In-Memory
Applications
Revolutionizing
Oncology Research
Standardized Modeling and
runtime environment for
analysis pipelines
10
Dr. Schapranow, HPI,
Aug 12, 2014
11. Interactive Genome Browser
■ Genome Browser enables interactive comparison of
multiple genomes
■ Combined knowledge by integrating latest
international annotations and literature, e.g. from NCBI,
dbSNP, and UCSC
■ Detailed exploration of genome locations and existing
associations
■ Ranked variants, e.g. accordingly to known diseases
■ Links always back to primary data sources to guarantee
validity of discovered findings
In-Memory
Applications
Revolutionizing
Oncology Research
Matching of genetic variants
and relevant annotations
Unified access to multiple
formerly disjoint data sources
11
Dr. Schapranow, HPI,
Aug 12, 2014
12. Analysis of Patient Cohorts
■ In a patient cohort, a subset does not respond to
therapy – why?
■ Clustering using various statistical algorithms, such as
k-means or hierarchical clustering
■ Calculation of all locus combinations in which at least
5% of all TCGA participants have mutations: 200ms for
top 20 combinations
■ Individual clusters are calculated in parallel directly
within the database
■ K-means algorithm: 50ms (PAL) vs. 500ms (R)
In-Memory
Applications
Revolutionizing
Oncology Research
Fast clustering directly
performed within the in-
memory database
12
Dr. Schapranow, HPI,
Aug 12, 2014
13. Dr. Schapranow, HPI,
Aug 12, 2014
Oncolyzer
■ Research initiative for exchanging relevant
tumor data to improve personalized treatment
■ Real-time analysis of tumor data in seconds
instead of hours
■ Information available at your fingertips: In-
memory technology on mobile devices, e.g. iPad
■ Interdisciplinary cooperation between clinicians,
clinical researchers, and software engineers
■ Honored with the 2012 Innovation Award of the
German Capitol Region
In-Memory
Applications
Revolutionizing
Oncology Research
Unified access to formerly disjoint
oncological data sources
Flexible analysis on patient’s
longitudinal data
13
t
14. ■ Combines patient’s
longitudinal time series data
with individual analysis
results
■ Real-time analysis across
hospital-wide data using
always latest data when
details screen is accessed
■ http://epic.hpi.uni-
potsdam.de/Home/
HanaOncolyzer
Oncolyzer
Patient Details Screen
Dr. Schapranow, HPI,
Aug 12, 2014
In-Memory
Applications
Revolutionizing
Oncology Research
14
15. ■ Allows real-time analysis on
complete patient cohort
■ Supports identification of
clinical trial participants
based on their individual
anamnesis
■ Flexible filters and various
chart types allow graphical
exploration of data on
mobile devices
Oncolyzer
Patient Analysis Screen
Dr. Schapranow, HPI,
Aug 12, 2014
In-Memory
Applications
Revolutionizing
Oncology Research
15
16. ■ Shows all patients the logged-
in clinician is assigned for
■ Provides overview about most
recent results and treatments
for each patient
■ http://global.sap.com/
germany/solutions/
technology/enterprise-
mobility/healthcare-apps/
mobile-patient-record-app.epx
SAP EMR
Patient Overview Screen
Dr. Schapranow, HPI,
Aug 12, 2014
In-Memory
Applications
Revolutionizing
Oncology Research
16
17. ■ Displays time series data, e.g.
temperature or BMI
■ Allows graphical exploration of
time series data
SAP EMR
Patient Detail Screen
Dr. Schapranow, HPI,
Aug 12, 2014
In-Memory
Applications
Revolutionizing
Oncology Research
17
18. Dr. Schapranow, HPI,
Aug 12, 2014
SAP Medical Research Insights
■ Clinical data from different sources and
departments are combined within a single in-
memory database system to form a unified biobank
■ Combine complex filter criteria to identify adequate
patient samples, e.g. for clinical research or trials
■ Breakthrough for managing and analysis of biobank
data in a systematic way
In-Memory
Applications
Revolutionizing
Oncology Research
Unified access to formerly disjoint
medical and biological data sources
Flexible Analysis
on historical data
18
t
19. Perform Manual
Data Exploration
And Analysis
Drug Response Analysis
Data Sources and Matching
In-Memory
Applications
Revolutionizing
Oncology Research
Collect Patient
Data
Sequence Tumor
Conduct Xenograft
Experiments
Metadata
e.g. smoking status,
tumor classification and age
Genome Data
e.g. raw DNA data
and genetic variants
Experiment Results
e.g. medication effectivity
obtained from wet laboratory
19
Dr. Schapranow, HPI,
Aug 12, 2014
20. Drug Response Analysis
Interactive Data Exploration
■ Drug response depends on individual genetic
variants of tumors
■ Challenge: Identification of relevant genetic
variants and their impact on drug response is a
ongoing research activity, e.g. Xenograft models
■ Exploration of experiment results is time-
consuming and Excel-driven
■ In-memory technology enables interactive
exploration of experiment data to leverage new
scientific insights
In-Memory
Applications
Revolutionizing
Oncology Research
Interactive analysis of
correlations between drugs
and genetic variants
20
Dr. Schapranow, HPI,
Aug 12, 2014
21. Interactive Clinical Trial Recruitment
■ Switch from trial-centric to patient-centric clinical trials
■ Real-time matching and clustering of patients and
clinical trial inclusion/exclusion criteria
■ No manual pre-screening of patients for months:
In-memory technology enables interactive pre-
screening process
■ Reassessment of already screened or already
participating patient reduces recruitment costs
In-Memory
Applications
Revolutionizing
Oncology Research
Assessment of patients
preconditions for clinical trials
21
Dr. Schapranow, HPI,
Aug 12, 2014
22. ■ For patients
□ Identify relevant clinical trials and medical experts
□ Start most appropriate therapy as early as possible
■ For clinicians
□ Preventive diagnostics to identify risk patients early
□ Indicate pharmacokinetic correlations
□ Scan for similar patient cases, e.g. to evaluate therapy
■ For researchers
□ Enable real-time analysis of medical data and its assessment, e.g.
assess pathways to identify impact of detected variants
□ Combined free-text search in publications, diagnosis, and
EMR data, i.e. structured and unstructured data
What to take home?
Test-drive it yourself: http://we.AnalyzeGenomes.com
Dr. Schapranow, HPI,
Aug 12, 2014
22
In-Memory
Applications
Revolutionizing
Oncology Research
23. Keep in contact with us!
Hasso Plattner Institute
Enterprise Platform & Integration Concepts (EPIC)
Program Manager E-Health
Dr. Matthieu-P. Schapranow
August-Bebel-Str. 88
14482 Potsdam, Germany
Dr. Matthieu-P. Schapranow
schapranow@hpi.de
http://we.analyzegenomes.com/
Dr. Schapranow, HPI,
Aug 12, 2014
In-Memory
Applications
Revolutionizing
Oncology Research
23