The document discusses the increasing complexity and volume of data generated in medicine, emphasizing the importance of big data in improving healthcare outcomes through better diagnosis, treatment, and patient monitoring. It highlights various applications, including the use of predictive models for managing diabetes and breast cancer risk, and addresses challenges such as engaging healthcare professionals with data tools. Additionally, it notes that while big data has the potential to revolutionize healthcare, it has yet to fulfill its promise fully due to reliance on human intuition in medical practices.

1. Big Data In Medicine
Nasir Arafat

2. Why Big data in medicine?
• The data generated by Hospitals or health insurance companies are
becoming bigger and complex
• Data are in different formats from human genetics to routine clinical
documentation, from internal imaging to motion capture, from digital
epidemiology to pharmacokinetics and from treatment pathways to life
course assessment
• Data science have improved healthcare industry from understanding
disease to better diagnose and helping patients monitor their own
conditions.

3. Sources of Medical Data
• The clinical trials data (data for clinical research) can be achieved
using electronic health records (EHR) or web based system.
• EHR designed to stores range of data (demographics, medical history,
immunization status, lab test results, radiology images)
• It reduces data replication and records can be shared through network
connected
• BIG DATA is the tool for medical science just like stethoscope, ECG
or thermometer
• We can collect data like heart rate, temperatures, body chemistry,
weight, ECG using medical instruments

4. Electronic Health record

5. How Big Data is being used for medicine
• Cleaver usage of data to make better decisions for the medical science
and health industry.
• Monitor the safety of drug treatment
• Better understand diseases, and develop drugs and treatments that can
change lives.
• Some EMR systems automatically monitor clinical events by
analysing patient data from an EHR to predict, detect events.
• Social media globalize health (important source of health data)

6. • We can collect heart rate, body temperature, weight, height, body
chemistry, ECG data.
• Without frame of reference this data is not be medically analysed .
• Until we have a better understanding of what can be considered
normal.

7. Big data is disruptive technology for medicine
• Radical changes in health care due to advances in genetic medicine
Data science is driving force in disruptive technologies born of genetic
medicine.
• Big data is going to revolutionize the health industry by advancing the
way of treatment, monitoring, diagnosis and medication.
• Yet Big data has not become so much useful to the health industry as
we are expecting and many medical traditionalists think that data
science can never replace human intuition & institution for doctors. So
the big data has yet to revolutionize the health industry.

8. Problem with using Big data
• One of the biggest problem around big data, and the predictive models
that could build on that data, really centres on how you engage others
to benefits from that information.
• Beyond the tools that we need to engage non-computational
individuals in this type of information and decision making.
• Training is the way to handle this problem

9. What big data means to patients
• Big data in medicine can build better health profiles and better predictive models around
individuals patients so that disease could be better diagnosed and treated.
• Future for patients is engaging them as a partner in this new mode of understanding their health
and wellness better and understanding how to make better decisions around those elements.
• Most of their data collection will be passive, so individual won’t have to be actively every day
(logging things)
• They’ll agree to have their data used in this way because they get some perceived benefit.

10. Extent to which big data make difference
in medicine
• Drug makers use big data to improve allergy medication supply chain
technology to make sure their products are in stock.
• They have advanced their supply chain technology and are able to
combine data from thousands of stores and compare the information
with other data to ensure they are meeting customer demand.
• Even some companies uses real time data sources like google fly
trends, twitter trends and temperature forecasts to predict when
products will be in high demand. There is some web based software
that helps drug makers to review data reports which are customizable
and can be analysed daily

11. Cancer genomics (DNA sequencing) using big data
to advance breast cancer risk prediction
• With more than 230,000 new cases of invasive breast cancer
diagnosed every year, according to the American cancer society.
• Big data can be new tool that may help diagnose and treat breast
cancer more effectively. Processing medical data generated by various
clinical trials, genomic sequencing and individual case studies
promises to unlock secrets that could not be readily observed by
human eyes.

12. Prediction of pre-diabetes
• Google is planning to tackle diabetes with the help of big data
analytics and innovative Internet of Things technologies.
• Using data analytics we can create and sample models for doctors that
could predict pre-diabetes patients would gain the most from treatment
with a drug that prevents diabetes. The researchers use data from 3k
peoples from an area as a clinical trial of diabetes prevention
• the diabetes prevention program, including a variety of different
health factors such as blood sugar levels and waist to hip ratio. even by
combining Google’s interest in miniaturized medical devices that
contribute to real-time patient monitoring.

13. Google to Tackle Diabetes Using Big Data and
Internet of Things
• The company previously explored the development of a contact lens that
continuously measures a patient’s blood glucose with little active effort. The lens
would theoretically eliminate the need for finger-prick blood glucose tests while
ensuring that patients are constantly monitored for dangerous fluctuations.
• Google was granted a patent for the device in March of 2015, though a
representative declined to state whether that meant the lens was approaching
market readiness.

14. Ebola (could Big data analytics help?)
• In 2014 more than 27,000 cases of Ebola
• Fatal illness in humans that kills more than 11,000 last year
• We can minimize the spread of Ebola Virus Using Big Data analytics.
• Using Infographics (visualisation representation of data) we can isolate the affected area to minimize the
spread of Ebola virus.
• Mobile mapping is being used for this process as mobile phones widely owned by everyone and are proving
rich source of data in a region.

15. Big data as a Future of medicine
• Penn Medicine taps big data to save lives: Penn medicine’s modern
big data initiatives applications alert doctors of at-risk patients. Penn
plans to take the platform open source for other healthcare providers
next year. The potential to deliver better healthcare has been one of
the driving factors behind the push for big data analytics in medicine
in recent years.
• Future of early cancer detection using micro RNA detection platform
for molecular data gathering, analysing and interpretation. Small RNA
molecules found in the blood reflect a person’s health status

16. The data lab cancer information program (project)
• This project is funded by data lab, digital health & care institute and
Stratified medicine. The objective of this project is to develop
prototype cancer intelligence system (CIS) directed towards fulfilling
the needs of cancer patients and clinicians in improving care and
outcomes for patients.
• By combining and exploiting existing data sets and unstructured
information sources it is envisaged that the CIS shall generate new
insights into patient outcomes and clinical treatments and outcomes
in Scotland.
(http://www.thedatalab.com/Cancer-Intelligent-System)

17. CIS (prototype)