Smart Data for Smart Doctors:

How Algorithms Support Fighting Complex Diseases

Dr.-Ing. Matthieu-P. Schapranow

3rd Int’l Symposium Big Data in Medicine, Potsdam

Nov 20, 2017

■ Can we enable clinicians to take their therapy decisions:

□ Incorporating all available patient specifics,

□ Referencing latest lab results and worldwide medical knowledge, and

□ In an interactive manner during their ward round?

Our Motivation

Turn Precision Medicine Into Clinical Routine

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

2

Schapranow, Big Data

in Medicine, Nov 20,

2017

The Challenge

Distributed Heterogeneous Data Sources

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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 articlesHospital 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

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

Schapranow, Big Data

in Medicine, Nov 20,

2017Routine Data

Treatments and cost

reimbursements

Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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Schapranow, Big Data

in Medicine, Nov 20,

2017

Our Approach: AnalyzeGenomes.com

In-Memory Computing Platform for Big Medical Data

5In-Memory Computing Platform

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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Schapranow, Big Data

in Medicine, Nov 20,

2017

Our Approach: AnalyzeGenomes.com

In-Memory Computing Platform for Big Medical Data

6In-Memory Computing Platform

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

Schapranow, Big Data

in Medicine, Nov 20,

2017

Our Approach: AnalyzeGenomes.com

In-Memory Computing Platform for Big Medical Data

7In-Memory Computing Platform

Combined and Linked Data

Genome

Data

Cellular

Pathways

Genome

MetadataResearch

Publications

Pipeline and

Analysis Models

Drugs and

Interactions

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

IndexedSources

Schapranow, Big Data

in Medicine, Nov 20,

2017

Our Approach: AnalyzeGenomes.com

In-Memory Computing Platform for Big Medical Data

8In-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

MetadataResearch

Publications

Pipeline and

Analysis Models

Drugs and

Interactions

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

IndexedSources

Schapranow, Big Data

in Medicine, Nov 20,

2017

Our Approach: AnalyzeGenomes.com

In-Memory Computing Platform for Big Medical Data

9In-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

MetadataResearch

Publications

Pipeline and

Analysis Models

Drugs and

Interactions

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

Drug Response

AnalysisPathway Topology

AnalysisMedical

Knowledge

Cockpit

Oncolyzer

Clinical Trial

Recruitment

Cohort

Analysis

...

IndexedSources

Heart Failure

Sleeping disorder

Fibrosis

Blood pressure

Blood volume

Gene ex-pression

Hyper-trophyCalcium

meta-bolism

Energy meta-bolism

Iron deficiency

Vitamin-D deficiency

Gender

Epi-genetics

■ Integrated systems medicine based on

real-time analysis of healthcare data

■ Funding period: Mar 2015 – Dec 2018

■ Funded consortium partners:

Use Case:

Systems Medicine Model of Heart Failure (SMART)

Schapranow, Big Data

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Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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A R T

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T R A M

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S M

■ Patient: 63 years, male, smoker, chronic heart insufficiency, stage III-IV

1. Appointment I: Acquire systemic patient details, e.g. physiological and

blood markers

2. Predict patient-specific surgery outcome using clinical model

3. Select adequate surgery option and conduct valve replacement

4. Equip patient with sensors to allow regular monitoring

5. Appointment II: 6 weeks after surgery to validate outcome

Establish Systems Medicine Model for

Improved Treatment of Heart Failure

Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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■ Process definition together with

subject matter experts

■ Identification of time-consuming steps

■ Key contributions

□ Sharing of data

□ Improved communication

□ Reproducible data processing

□ Data analysis applications, e.g. for

interactive hypothesis validation

Requirements Engineering for System Medicine

Computer-aided Systems Medicine Process

Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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■ In-Memory Database (IMDB)

as data integration platform

■ Integration of partner data to

provide a holistic patient view

■ Event-driven process

notifications

■ Specific real-time analysis

workflows, e.g. for

RNAseq data

Integrated Systems Medicine

Data Processing Platform

Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

SMART IT Platform

Event

Notification

Data

AcquisitionData artifactData Artifacts

Statistical Data Analysis

Methods, Machine Learning,

Bioinformatics Tools

Analysis

Applications

Sync Client

Researcher/

Clinician

Specific Analysis

Applications

In-Memory Database

User and Event

Data

Hemodynamic

Parameters

Clinical Data

Omics Data

Sync Client

Sync Server

Importer for

non-NGS Data

Importer for

FASTQ Data

Modeling

Parameters

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SMART IT Platform

Event

Notification

Data

AcquisitionData artifactData Artifacts

Statistical Data Analysis

Methods, Machine Learning,

Bioinformatics Tools

Analysis

Applications

Sync Client

Researcher/

Clinician

Specific Analysis

Applications

In-Memory Database

User and Event

Data

Hemodynamic

Parameters

Clinical Data

Omics Data

Sync Client

Sync Server

Importer for

non-NGS Data

Importer for

FASTQ Data

Modeling

Parameters

Reproducible Data Processing:

Transfer of Processing Logic to Platform

Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

■ Example: Flexible protocol for processing of RNAseq data

■ Standardized model using Business Process Modeling and Notation (BPMN)

■ Use of standardized pipeline models enable reproducible researchRNA Seq Analysis_V2

TopHat

Trimmomatic

FASTQC

STAR

featureCounts

Counts Matrix

BAM-FileAligned Reads

FASTQC 2

FASTQ -Trimmed Reads

Pre-TrimmingQC-Report

FASTQ - Reads

Post-AlignmentQC-Report

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Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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■ Interdisciplinary partners collaborate on enabling interactive health research

■ Current 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

Use Case:

Smart Analysis Health Research Access (SAHRA)

Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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Elderly Care Planning in Mecklenburg-Vorpommern

Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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Geospational Data Exploration

Schapranow, Big Data

in Medicine, Nov 20,

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Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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■ Stratification of patient cohorts using patient specifics

■ Automatic matching of similar patients and patient anamnesis

■ Interactive graphical exploration of longitudinal patient data

Stratification of Hypertension Patients

and Longitudinal Data Analysis

Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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■ For patients

□ Support education and sovereignty of patients

□ Identify patient-specific details, e.g. clinical trials or medical experts

■ For clinicians

□ Identify similar patient cases, e.g. to find adequate therapy

□ Analyze pharmacokinetic correlations

■ For researchers

□ Enable real-time analysis of medical data, e.g. assess biological

pathways to evaluate impact of genetic variants

□ Analysis of structured and unstructured data, e.g. publications,

diagnosis, and EMR data

What to Take Home?

Test-drive it yourself: https://we.AnalyzeGenomes.com

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2017

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Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

Stay in Contact!

Dr.-Ing. Matthieu-P. Schapranow

Program Manager E-Health & Life Sciences

Hasso Plattner Institute

August-Bebel-Str. 88

14482 Potsdam, Germany

we.analyzegenomes.com

@AnalyzeGenomes

Schapranow, Big Data

in Medicine, Nov 20,

2017

Smart Data for Smart

Doctors: How

Algorithms Support

Fighting Complex

Diseases

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