Lecturer has Deep experience defining Cloud computing, security models for IaaS, PaaS, and SaaS architectures specifically as the architecture relates to IAM. Deep Experience Defining Privacy protection Policy, a big fan of GDPR interpretation. DeelExperience in Information security, Defining Healthcare security best practices including AI and Big Data, IT Security and ICS security and privacy controls in the industrial environments. Deep knowledge of security frameworks such as Cloud Security Alliance (CSA), International Organization for Standardization (ISO), National Institute of Standards and Technology (NIST), IBM ITCS104 etc. What Will You learn: Every day, the website collects a huge amount of data. The data allows to analyze the behavior of Internet users, their interests, their purchasing behavior and the conversion rates. In order to increase business, big data offers the tools to analyze and process data in order to reveal competitive advantages from the data. What Healthcare has to do with Big Data How AI can assist in patient care? Why some are afraid? Are there any dangers?

1. A Joke…

3. Big Data Healthcare
What is it?
What are the opportunities?
What are the Challenges?

4. Big Data It is everywhere!
Not only Health
care

5. Let’s
Exercise

6. Let’s
Exercise

7. Several Use Cases

10. Creating risk scores based on lab testing,
biometric data, claims data, patient-generated
health data, and the social determinants of health
can give healthcare providers insight into which
individuals might benefit from enhanced services
or wellness activities.
AVOIDING 30-DAY HOSPITAL READMISSIONS
predictive analytics can warn providers when a
patient’s risk factors indicate a high likelihood for
readmission within the 30-day window.
GETTING AHEAD OF PATIENT DETERIORATION
Machine learning strategies are particularly well suited to
predicting clinical events in the hospital, such as
the development of an acute kidney injury (AKI) or sepsis.
PREVENTING SUICIDE AND PATIENT SELF-HARM
Early identification of individuals likely to cause harm to
themselves can ensure that these patients receive the
mental healthcare they need to avoid serious events,
including suicide.
DEVELOPING PRECISION MEDICINE AND NEW THERAPIES
AS PRECISION MEDICINE AND GENOMICS GAIN STEAM,
PROVIDERS AND RESEARCHERS ARE TURNING TO
ANALYTICS TO SUPPLEMENT TRADITIONAL CLINICAL
TRIALS AND DRUG DISCOVERY TECHNIQUES.

11.  Personalized medicines
 Cost reduction
 Care in Real Time
 Increase transparency
 Improve patient outcomes
 Research and development

14. Is there a Relation between High Blood
Pressure to Coronary Heart Disease
Usual SBP (mmHg)
120 140 160 180
1
2
4
8
16
32
64
128
256
Age at risk:
80-89
70-79
60-69
50-59
40-49
50,000 people
Usual SBP (mmHg)
120 140 160 180
1
2
4
8
16
32
64
128
256
Age at risk:
80-89
70-79
60-69
50-59
40-49
5000 people
Usual SBP (mmHg)
120 140 160 180
1
2
4
8
16
32
64
128
256
Age at risk:
80-89
70-79
60-69
50-59
40-49
500,000 people

20. The Risk of
Discrimination

21. The Risk of Bias

22. The Risk of Privacy
(linkage)

23. Conclusion

24. Ten simple rules for responsible
big data research
Acknowledge that data are people and can do harm
Recognize that privacy is more than a binary value
Guard against the reidentification of your data
Practice ethical data sharing
Consider the strengths and limitations of your data;
big does not automatically mean better
Debate the tough, ethical choices
Develop a code of conduct for your organization,
research community, or industry
Design your data and systems for auditability
Engage with the broader consequences of data and
analysis practices
Know when to break these rules