big data, biobanks and predictive analytics for a...

BIG DATA, BIOBANKS AND

PREDICTIVE ANALYTICS FOR

A BETTER CLINICAL OUTCOME

BIG DATA, BIOBANKS AND

PREDICTIVE ANALYTICS FOR

A BETTER CLINICAL OUTCOME

Π. Ε. Βάρδας MD, PhD(London)Π. Ε. Βάρδας MD, PhD(London)

DISCLOSURESDISCLOSURES

My great love to innovative ideasMy great love to innovative ideas

BIG DATABIG DATA

It is a broad term for data sets, so large or

complex that traditional data processing

applications are inadequate.

It is a broad term for data sets, so large or

complex that traditional data processing

applications are inadequate.

BIG DATABIG DATA

To qualify as “big” in the sense that

information scientists use the term, a dataset

much reach a level of size and complexity, that

it becomes a challenge to store, process and

analyze by standard computational methods.

It is estimated that per capita computing

capacity has been doubling every 40 months

since the 1980’s

To qualify as “big” in the sense that

information scientists use the term, a dataset

much reach a level of size and complexity, that

it becomes a challenge to store, process and

analyze by standard computational methods.

It is estimated that per capita computing

capacity has been doubling every 40 months

since the 1980’s

BIG DATABIG DATA

• Challenges include analysis, capture, data curation,

search, sharing, storage, transfer, visualization and

information privacy.

• Data sets grow in size in part, because they are

increasingly being gathered by cheap and

numerous information sensing mobile devices,

remote sensing, cameras, microphones, radio-

frequency identification readers & wireless sensors.

• Challenges include analysis, capture, data curation,

search, sharing, storage, transfer, visualization and

information privacy.

• Data sets grow in size in part, because they are

increasingly being gathered by cheap and

numerous information sensing mobile devices,

remote sensing, cameras, microphones, radio-

frequency identification readers & wireless sensors.

LARGE DATA SIZESLARGE DATA SIZES

• BYTE

1 byte: A single character

10 bytes: A single word

100 bytes: A telegram

• KILOBYTE (1.000 bytes)

1 kilobyte : A very short story

10 kilobytes: An encyclopedic page

50 kilobytes: A compressed document image page

• BYTE

1 byte: A single character

10 bytes: A single word

100 bytes: A telegram

• KILOBYTE (1.000 bytes)

1 kilobyte : A very short story

10 kilobytes: An encyclopedic page

50 kilobytes: A compressed document image page


• MEGABYTE (1.000.000 bytes)

1 Megabyte: A small novel

10 Megabytes: A minute of high fidelity sound

100 Megabytes: One meter of shelved books

500 Megabytes: A CD-ROM

• GIGABYTE (1.000.000.000 bytes)

1 Gigabyte : A pickup truck filled with paper, or a movie

at TV quality

100 Gigabytes: A floor of academic journals

• MEGABYTE (1.000.000 bytes)

1 Megabyte: A small novel

10 Megabytes: A minute of high fidelity sound

100 Megabytes: One meter of shelved books

500 Megabytes: A CD-ROM

• GIGABYTE (1.000.000.000 bytes)

1 Gigabyte : A pickup truck filled with paper, or a movie

at TV quality

100 Gigabytes: A floor of academic journals


• TERABYTE (1.000.000.000.000 bytes)

1 Terabyte: All the X-Ray films in a large hospital

10 Terabytes: The printed collection of the US

Library of Congress

50 Terabytes: The contents of a large Mass Storage

System

• TERABYTE (1.000.000.000.000 bytes)

1 Terabyte: All the X-Ray films in a large hospital

10 Terabytes: The printed collection of the US

Library of Congress

50 Terabytes: The contents of a large Mass Storage

System

LARGE DATA SETSLARGE DATA SETS

• PETABYTE (1.000.000.000.000.000 bytes)

2 Petabytes: All US academic research libraries

20 Petabytes: All production of hard-disk drivers in

1995

200 Petabytes: All printed material

• PETABYTE (1.000.000.000.000.000 bytes)

2 Petabytes: All US academic research libraries

20 Petabytes: All production of hard-disk drivers in

1995

200 Petabytes: All printed material


• EXABYTE (1.000.000.000.000.000.000 bytes)5 Exabytes: All words ever spoken by human beings

• ZETABYTE (1.000.000.000.000.000.000.000 bytes)

• YOTTABYTE …

• XENOTTABYTE …

• SHILENTNOBYTE…

• DOMEGEMEGROTTEBYTE…

• EXABYTE (1.000.000.000.000.000.000 bytes)5 Exabytes: All words ever spoken by human beings

• ZETABYTE (1.000.000.000.000.000.000.000 bytes)

• YOTTABYTE …

• XENOTTABYTE …

• SHILENTNOBYTE…

• DOMEGEMEGROTTEBYTE…


• According to International Data Corporation, the

total amount of global data was expected to grow to

2.7 zettabytes during 2012. This is 48% up from 2011

• In 2020 it is estimated there will be 44 times more

data than in 2009

• That means 35 zettabytes compared to 800.000

Petabytes

• According to International Data Corporation, the

total amount of global data was expected to grow to

2.7 zettabytes during 2012. This is 48% up from 2011

• In 2020 it is estimated there will be 44 times more

data than in 2009

• That means 35 zettabytes compared to 800.000

Petabytes

PLEASE IMAGING..PLEASE IMAGING..

BIG DATA BASICSBIG DATA BASICSVOLUMEIn 2020, it is estimated there will be 44 timesmore data than 2009. Thirty-nine Zetabytes comparedto 800.000 Petabytes

VELOCITYRepresents the increasing frequency withwhich data is delivered

VARIETYIt signifies the many forms in which data exists

VOLUMEIn 2020, it is estimated there will be 44 timesmore data than 2009. Thirty-nine Zetabytes comparedto 800.000 Petabytes

VELOCITYRepresents the increasing frequency withwhich data is delivered

VARIETYIt signifies the many forms in which data exists

THE NEED FOR ELECTRICAL MEDICALRECORDS (EMR)

THE NEED FOR ELECTRICAL MEDICALRECORDS (EMR)

Development of EMR will permit integration ofbiological data, clinical information, patientinformation and clinical outcomes

Large population or specific groups of patient withselected characteristics could be easily identifiedwith the availability of electronic medical records

In genomic research EMR, facilitate analysis geneticand molecular information from large subjectpopulations allowing studies to be more powerfulthan small cohort studies.

Development of EMR will permit integration ofbiological data, clinical information, patientinformation and clinical outcomes

Large population or specific groups of patient withselected characteristics could be easily identifiedwith the availability of electronic medical records

In genomic research EMR, facilitate analysis geneticand molecular information from large subjectpopulations allowing studies to be more powerfulthan small cohort studies.

PREDICTIVE ANALYTICSPREDICTIVE ANALYTICS


It Is the practice of extracting information fromexisting data sets, in order to determine patternsand predict future outcomes and trends.

Predictive analytics does not tell you what willhappen in future.

It Is the practice of extracting information fromexisting data sets, in order to determine patternsand predict future outcomes and trends.

Predictive analytics does not tell you what willhappen in future.


Predictive Analytics (PA) uses technology and

Statistical methods to search through massive amounts

of information, analyzing it to predict outcomes for

individual patients.

That information can include data from past treatment

outcomes as well as the latest medical research

published in peer-reviewed journals and databases.

Predictive Analytics (PA) uses technology and

Statistical methods to search through massive amounts

of information, analyzing it to predict outcomes for

individual patients.

That information can include data from past treatment

outcomes as well as the latest medical research

published in peer-reviewed journals and databases.

…IN HEALTHCARE…IN HEALTHCARE

BIG DATA AND PREDICTIVE ANALYTICSBIG DATA AND PREDICTIVE ANALYTICS

They will study whether we can predict anindividual's disease course as early as possible, byinferring their subtype.

In many diseases, is not one, but many differentsubtypes.

Analyzing patterns allow us to ask why differentindividuals show different disease trajectories

They will study whether we can predict anindividual's disease course as early as possible, byinferring their subtype.

In many diseases, is not one, but many differentsubtypes.

Analyzing patterns allow us to ask why differentindividuals show different disease trajectories

BIG DATA AND CLINICALOUTCOMES

BIG DATA AND CLINICALOUTCOMES

HOW BIG DATA HELPS HEALTHCAREHOW BIG DATA HELPS HEALTHCARE

• Big Data has tremendous potential to add value in

all healthcare settings.

• Big Data solutions can help organizations

personalize care, engage patients, reduce variability

and cost and improve quality.

• Personalization whether based on genomic data,

standard test data, or a combination of the two,

requires the integration and analysis of much larger

volumes of data.

• Big Data has tremendous potential to add value in

all healthcare settings.

• Big Data solutions can help organizations

personalize care, engage patients, reduce variability

and cost and improve quality.

• Personalization whether based on genomic data,

standard test data, or a combination of the two,

requires the integration and analysis of much larger

volumes of data.

BIG DATA IN THE DIGITAL HEALTHBIG DATA IN THE DIGITAL HEALTH

1. Web & Social media data(Smart phone apps, healthplan websites)

2. Machine-to-machine data(Sensors, meters,different devices)

3. Transactions data(Health care claims, billingrecords, in both semi-structured and unstructuredformats)

4. Biometric data5. Human general data6. Pharmaceutical & Medtech, R&D data

1. Web & Social media data(Smart phone apps, healthplan websites)

2. Machine-to-machine data(Sensors, meters,different devices)

3. Transactions data(Health care claims, billingrecords, in both semi-structured and unstructuredformats)

4. Biometric data5. Human general data6. Pharmaceutical & Medtech, R&D data

Usually addresses the following six categories ofinformation

Usually addresses the following six categories ofinformation

FACTORS DRIVING THE BIG DATA MARKET INTHE HEALTHCARE SECTOR

FACTORS DRIVING THE BIG DATA MARKET INTHE HEALTHCARE SECTOR

• The need for improved clinical outcomes• The need for increased efficiency in managing

healthcare data• The presence of Federal healthcare mandates in some

segments• The double digit growth in the HER• The increased focus on value-based medicine• The need for personalized medicine that’s based on

analytics• The need for improved decision support• The need to reduce pharmaceutical cost• The need to reduce clinical testing costs

• The need for improved clinical outcomes• The need for increased efficiency in managing

healthcare data• The presence of Federal healthcare mandates in some

segments• The double digit growth in the HER• The increased focus on value-based medicine• The need for personalized medicine that’s based on

analytics• The need for improved decision support• The need to reduce pharmaceutical cost• The need to reduce clinical testing costs

FACTORS INHIBITING THE GROWTHOF BIG DATA

FACTORS INHIBITING THE GROWTHOF BIG DATA

• A resistance to a systems-approach by the medicalcommunity

• The operational gap between payer & provider frontoffice

• The acute IT staff shortage in healthcare• A lack of comparable & transparent data in healthcare• Financial constraints• Concerns regarding ensuring patient confidentiality• The low costs of traditional analytics techniques• The lack of interoperability between healthcare

systems

• A resistance to a systems-approach by the medicalcommunity

• The operational gap between payer & provider frontoffice

• The acute IT staff shortage in healthcare• A lack of comparable & transparent data in healthcare• Financial constraints• Concerns regarding ensuring patient confidentiality• The low costs of traditional analytics techniques• The lack of interoperability between healthcare

systems

BIOBANKSBIOBANKS

BIOBANKSBIOBANKS

A collection of biological material (e.g.animal, plant, human-skin, blood, organs,hair, saliva etc) with correspondingdocumentation that can be used for researchpurposes.

A collection of biological material (e.g.animal, plant, human-skin, blood, organs,hair, saliva etc) with correspondingdocumentation that can be used for researchpurposes.

Personalized medicine is a new model ofhealthcare treatment.

It delivers targeted diagnostics, treatment andadvice on nutrition, which are tailored to anindividual.

Personalized or precision medicine will beeffective for the majority of people once geneticand molecular information derived from theirsamples, has been systematically understood.

Personalized medicine is a new model ofhealthcare treatment.

It delivers targeted diagnostics, treatment andadvice on nutrition, which are tailored to anindividual.

Personalized or precision medicine will beeffective for the majority of people once geneticand molecular information derived from theirsamples, has been systematically understood.

CAN BIOBANKS BE USED FORPERSONALISED MEDICINE ?




To achieve the targets we need to study geneticand molecular information.

We need to predict the risk of disease, identifynew targets for treatments and also identifymarkers predicting positive or negative reactionto treatment options.

Therefore biobanks are needed as they containa large pool of resources in the form of codedgenetic materials

To achieve the targets we need to study geneticand molecular information.

We need to predict the risk of disease, identifynew targets for treatments and also identifymarkers predicting positive or negative reactionto treatment options.

Therefore biobanks are needed as they containa large pool of resources in the form of codedgenetic materials

“WHAT WE’RE TALKING ABOUT HERE ISTHE TRANSFORMATION OF MEDICINE”Scott Zeger,Vice Provost for ResearchJohns Hopkins University, USA

The biomedical sciences have been the pillar ofthe health care system for a long time.The new system will have two equal pillars:The biomedical sciences and the data sciences

“WHAT WE’RE TALKING ABOUT HERE ISTHE TRANSFORMATION OF MEDICINE”Scott Zeger,Vice Provost for ResearchJohns Hopkins University, USA

The biomedical sciences have been the pillar ofthe health care system for a long time.The new system will have two equal pillars:The biomedical sciences and the data sciences

To physicians of the time, the appropriate treatment

for “apparent death” was warmth and stimulation.

For this purpose, artificial respiration and the blowing

of smoke into the lungs or the rectum were thought to

be interchangeably useful. The smoke enema was

considered the most potent method, however, due to

the warming and stimulating properties associated

with tobacco in the pharmacopoeia of the period. At

the turn of the 19th century, tobacco smoke enemas

had become an established practice in Western

medicine, considered by Humane Societies to be as

important as artificial respiration. In the 1780s, the

Royal Humane Society installed resuscitation kits,

including smoke enemas, at various points along the

Thames.

To physicians of the time, the appropriate treatment

for “apparent death” was warmth and stimulation.

For this purpose, artificial respiration and the blowing

of smoke into the lungs or the rectum were thought to

be interchangeably useful. The smoke enema was

considered the most potent method, however, due to

the warming and stimulating properties associated

with tobacco in the pharmacopoeia of the period. At

the turn of the 19th century, tobacco smoke enemas

had become an established practice in Western

medicine, considered by Humane Societies to be as

important as artificial respiration. In the 1780s, the

Royal Humane Society installed resuscitation kits,

including smoke enemas, at various points along the

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