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)
DISCLOSURES
DISCLOSURES
My great love to innovative ideas
My great love to innovative ideas
BIG DATA
BIG 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 DATA
BIG 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 DATA
BIG 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 SIZES
LARGE 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
LARGE DATA SIZES
LARGE DATA SIZES
• 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
LARGE DATA SIZES
LARGE DATA SIZES
• 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 SETS
LARGE 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
LARGE DATA SETS
LARGE DATA SETS
• 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…
LARGE DATA SETS
LARGE DATA SETS
• 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 BASICS
BIG DATA BASICS
VOLUME
In 2020, it is estimated there will be 44 times
more data than 2009. Thirty-nine Zetabytes compared to 800.000 Petabytes
VELOCITY
Represents the increasing frequency with which data is delivered
VARIETY
It signifies the many forms in which data exists VOLUME
In 2020, it is estimated there will be 44 times
more data than 2009. Thirty-nine Zetabytes compared to 800.000 Petabytes
VELOCITY
Represents the increasing frequency with which data is delivered
VARIETY
It signifies the many forms in which data exists
THE NEED FOR ELECTRICAL MEDICAL
RECORDS (EMR)
THE NEED FOR ELECTRICAL MEDICAL
RECORDS (EMR)
Development of EMR will permit integration of biological data, clinical information, patient
information and clinical outcomes
Large population or specific groups of patient with selected characteristics could be easily identified with the availability of electronic medical records In genomic research EMR, facilitate analysis genetic and molecular information from large subject
populations allowing studies to be more powerful than small cohort studies.
Development of EMR will permit integration of biological data, clinical information, patient
information and clinical outcomes
Large population or specific groups of patient with selected characteristics could be easily identified with the availability of electronic medical records In genomic research EMR, facilitate analysis genetic and molecular information from large subject
populations allowing studies to be more powerful than small cohort studies.
PREDICTIVE ANALYTICS
PREDICTIVE ANALYTICS
PREDICTIVE ANALYTICS
PREDICTIVE ANALYTICS
It Is the practice of extracting information from
existing data sets, in order to determine patterns
and predict future outcomes and trends.
Predictive analytics does not tell you what will
happen in future.
It Is the practice of extracting information from
existing data sets, in order to determine patterns
and predict future outcomes and trends.
Predictive analytics does not tell you what will
happen in future.
PREDICTIVE ANALYTICS
PREDICTIVE ANALYTICS
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 ANALYTICS BIG DATA AND PREDICTIVE ANALYTICS They will study whether we can predict an
individual's disease course as early as possible, by inferring their subtype.
In many diseases, is not one, but many different subtypes.
Analyzing patterns allow us to ask why different individuals show different disease trajectories They will study whether we can predict an
individual's disease course as early as possible, by inferring their subtype.
In many diseases, is not one, but many different subtypes.
Analyzing patterns allow us to ask why different individuals show different disease trajectories
BIG DATA AND CLINICAL
OUTCOMES
BIG DATA AND CLINICAL
OUTCOMES
HOW BIG DATA HELPS HEALTHCARE
HOW 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 HEALTH
BIG DATA IN THE DIGITAL HEALTH
1. Web & Social media data(Smart phone apps, health plan websites)
2. Machine-to-machine data(Sensors, meters, different devices)
3. Transactions data(Health care claims, billing
records, in both semi-structured and unstructured formats)
4. Biometric data
5. Human general data
6. Pharmaceutical & Medtech, R&D data
1. Web & Social media data(Smart phone apps, health plan websites)
2. Machine-to-machine data(Sensors, meters, different devices)
3. Transactions data(Health care claims, billing
records, in both semi-structured and unstructured formats)
4. Biometric data
5. Human general data
6. Pharmaceutical & Medtech, R&D data
Usually addresses the following six categories of information
Usually addresses the following six categories of information
FACTORS DRIVING THE BIG DATA MARKET IN THE HEALTHCARE SECTOR
FACTORS DRIVING THE BIG DATA MARKET IN THE 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 GROWTH OF BIG DATA
FACTORS INHIBITING THE GROWTH OF BIG DATA
• A resistance to a systems-approach by the medical community
• The operational gap between payer & provider front office
• 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 medical community
• The operational gap between payer & provider front office
• 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
BIOBANKS
BIOBANKS
BIOBANKS
BIOBANKS
A collection of biological material (e.g.
animal, plant, human-skin, blood, organs,
hair, saliva etc) with corresponding
documentation that can be used for research
purposes.
A collection of biological material (e.g.
animal, plant, human-skin, blood, organs,
hair, saliva etc) with corresponding
documentation that can be used for research
purposes.
Personalized medicine is a new model of
healthcare treatment.
It delivers targeted diagnostics, treatment and
advice on nutrition, which are tailored to an
individual.
Personalized or precision medicine will be
effective for the majority of people once genetic
and molecular information derived from their
samples, has been systematically understood.
Personalized medicine is a new model of
healthcare treatment.
It delivers targeted diagnostics, treatment and
advice on nutrition, which are tailored to an
individual.
Personalized or precision medicine will be
effective for the majority of people once genetic
and molecular information derived from their
samples, has been systematically understood.
CAN BIOBANKS BE USED FOR
PERSONALISED MEDICINE ?
CAN BIOBANKS BE USED FOR
PERSONALISED MEDICINE ?
CAN BIOBANKS BE USED FOR
PERSONALISED MEDICINE ?
CAN BIOBANKS BE USED FOR
PERSONALISED MEDICINE ?
To achieve the targets we need to study genetic
and molecular information.
We need to predict the risk of disease, identify
new targets for treatments and also identify
markers predicting positive or negative reaction
to treatment options.
Therefore biobanks are needed as they contain
a large pool of resources in the form of coded
genetic materials
To achieve the targets we need to study genetic
and molecular information.
We need to predict the risk of disease, identify
new targets for treatments and also identify
markers predicting positive or negative reaction
to treatment options.
Therefore biobanks are needed as they contain
a large pool of resources in the form of coded
genetic materials
“WHAT WE’RE TALKING ABOUT HERE IS
THE TRANSFORMATION OF MEDICINE”
Scott Zeger,
Vice Provost for Research
Johns Hopkins University, USA
The biomedical sciences have been the pillar of
the 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 IS
THE TRANSFORMATION OF MEDICINE”
Scott Zeger,
Vice Provost for Research
Johns Hopkins University, USA
The biomedical sciences have been the pillar of
the 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
