gesundheit geht uns alle an: smart data ermöglicht passendere entscheidungen – in echtzeit!

Gesundheit geht uns alle an: Smart Data ermöglicht passendere Entscheidungen – in Echtzeit!

Dr. Matthieu-P. Schapranow Bitkom/IHK Big Data Summit Berlin

Sep 27, 2016

Patient Doctor Interaction

Schapranow, Bitkom/IHK Big Data Summit, Sep 27, 2016

Analyze Genomes Services for Precision Medicine

2

What about the relevant data?

IT Challenges of Healthcare Distributed Heterogeneous Data Sources

3

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 data Cancer patient records

>160k records at NCT Analyze Genomes Services for Precision Medicine


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

xx

T

disk

4




Digital Health powered by AnalyzeGenomes.com: Real-time Analysis of Big Medical Data

5

In-Memory Database

Extensions for Life Sciences

Data Exchange, App Store

Access Control, Data Protection

Fair Use

Statistical Tools

Real-time Analysis

App-spanning User Profiles

Combined and Linked Data

Genome Data

Cellular Pathways

Genome Metadata

Research Publications

Pipeline and Analysis Models

Drugs and Interactions


Drug Response Analysis

Pathway Topology Analysis

Medical Knowledge Cockpit Oncolyzer

Clinical Trial Recruitment

Cohort Analysis

...

Indexed Sources

Use Case: Precision Medicine in Oncology Identification of Best Treatment Option for Cancer Patient

■  Patient: 48 years, female, non-smoker, smoke-free environment

■  Diagnosis: Non-Small Cell Lung Cancer (NSCLC), stage IV

■  Markers: KRAS, EGFR, BRAF, NRAS, (ERBB2)

1.  Surgery to remove tumor

2.  Tumor sample is sent to laboratory to extract DNA

3.  DNA is sequenced resulting in 750 GB of raw data per sample

4.  Processing of raw data to perform analysis

5.  Identification of relevant driver mutations using international medical knowledge

6.  Informed decision making Schapranow, Bitkom/IHK Big Data Summit, Sep 27, 2016


6



7



8

App Example: Integrating Processing and Real-time Analysis of Genome Data in the Clinical Routine

■  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


Standardized Modeling and runtime environment for

analysis pipelines

9


■  Query-oriented search interface

■  Seamless integration of patient specifics, e.g. from EMR

■  Parallel search in international knowledge bases, e.g. for biomarkers, literature, cellular pathway, and clinical trials

App Example: Medical Knowledge Cockpit for Patients and Clinicians


10


Real-time Data Analysis and Interactive Exploration

App Example: Identifying Best Chemotherapy using Drug Response Analysis



Smoking status, tumor classification

and age (1MB - 100MB)

Raw DNA data and genetic variants

(100MB - 1TB)

Medication efficiency and wet lab results

(10MB - 1GB)

11

Patient-specific Data

Tumor-specific Data

Compound Interaction Data

Heart Failure

Sleeping disorder

Fibrosis

Blood pressure

Blood volume

Gene ex-pression

Hyper-trophy Calcium

meta-bolism

Energy meta-bolism

Iron deficiency

Vitamin-D deficiency

Gender

Epi-genetics

■  Integrated systems medicine based on real-time analysis of healthcare data

■  Initial funding period: Mar ‘15 – Feb ‘18

■  Funded consortium partners:

Systems Medicine Model of Heart Failure (SMART)



12

A R T +

T R A M

S + S

M

■  Process definition through user interviews

■  Identification of time-consuming and manual process steps

■  Requirements for a computer-aided research process:

1.  Sharing of data

2.  Improved communication

3.  Reproducible data processing

Requirements Engineering for System Medicine Computer-aided Systems Medicine Process



13

■  In-Memory Database (IMDB) as data integration platform

■  Automatic synchronization of partner data

■  Event-driven notifications

■  Specific real-time analysis workflows, e.g. for RNAseq

SMART Data Integration and Analysis Platform



14

■  Interdisciplinary partners collaborate on enabling real-time healthcare research

■  Initial funding period: Aug 2015 – July 2018

■  Funded consortium partners:

□  AOK German healthcare insurance company

□  data experts group Technology operations

□  Hasso Plattner Institute Real-time data analysis, in-memory database technology

□  Technology, Methods, and Infrastructure for Networked Medical Research

Legal and data protection

Smart Analysis Health Research Access (SAHRA)



15

1.  AP4.12: Data exploration, e.g. graphical exploration and linkage of data

2.  AP4.17: Matching, e.g. to identify sets of patients with similar features

3.  AP4.22: Prediction, e.g. of future patient developments using training data from similar patients

SAHRA Pilot Applications



16

■  Deep learning supporting radiologists at Charité, e.g. MRI segmentation

■  Natural language processing with DKG,

e.g. question answering, clinical trials, publications

■  Real-time Exploration of the Human Immune System, NCT Heidelberg

Further Digital Health Activities



17

Control your Personal Data



18

Data Donation Pass

Personal Interests ■  Asthma ■  Diabetes II

Notifications ■  9/27: Granted Dr. House

access to resp. lab data

■  9/25: Respiratory laboratory data available

■  9/21: Asthma self management study publ.

■  For patients

□  Identify relevant clinical trials and medical experts

□  Become an informed patient

■  For clinicians

□  Identify pharmacokinetic correlations

□  Scan for similar patient cases, e.g. to evaluate therapy efficiency

■  For researchers

□  Enable real-time analysis of medical data, e.g. assess pathways to identify impact of detected variants

□  Combined mining in structured and unstructured data, e.g. publications,

diagnosis, and EMR data

What to Take Home? Test it Yourself: AnalyzeGenomes.com

Schapranow, Bitkom/IHK Big Data Summit, Sep 27, 2016 19


Keep in contact with us!



20

Dr. Matthieu-P. Schapranow Program Manager E-Health & Life Sciences

Hasso Plattner Institute

August-Bebel-Str. 88 14482 Potsdam, Germany

[email protected]

http://we.analyzegenomes.com/