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AI and Big Data in Health Sector Opportunities and challenges | Big Data Demystified

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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?

Published in: Engineering
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AI and Big Data in Health Sector Opportunities and challenges | Big Data Demystified

  1. 1. A Joke…
  2. 2. Big Data Healthcare What is it? What are the opportunities? What are the Challenges?
  3. 3. Big Data It is everywhere! Not only Health care
  4. 4. Let’s Exercise
  5. 5. Let’s Exercise
  6. 6. Several Use Cases
  7. 7. 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.
  8. 8.  Personalized medicines  Cost reduction  Care in Real Time  Increase transparency  Improve patient outcomes  Research and development
  9. 9. 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
  10. 10. The Risk of Discrimination
  11. 11. The Risk of Bias
  12. 12. The Risk of Privacy (linkage)
  13. 13. Conclusion
  14. 14. 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

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