The document discusses medical data analysis, including the different kinds of medical data like numerical, categorical, text, images and videos, as well as sources of medical data. It covers how machine learning and deep learning are used to analyze medical data for applications like predictive, descriptive, diagnostic and prescriptive analytics. Cloud tools from companies like IBM, Google, Amazon and Microsoft are also discussed for performing medical data analysis at scale.

1. Medical Data Analysis

2. About
the
speaker
Lavanya T S
SME AI, DS and Cloud Computing
• Senior Manager
• Technical Advisor, Academia
SkillUp Technologies
Enabling Continual Education
to Deliver Powerful Online Learning

3. Empowerment
Promise
At the end of this
session, you will
know about….
Introduction to Medical Data Analysis
Kinds of Medical Data
Sources of Medical Data
Machine Learning and Deep Learning
How is Medical Data Used
Type of Medical Data Analysis
Cloud Tools for Medical Data Analysis
Future of Medical Data Analysis
Use Cases
Q and A

4. Introduction
to Medical
Data
Analytics
Analyzing medical data – In structured and
Unstructured form
Using data analytics for AI solutions
Medical data officially started being stored from
1962
About 30-35% of the 2.5 quintillion bytes data
generated in the world every day as of May
2022, is healthcare data

5. Kinds of
Medical
data
•Numerical Data
•Categorical Data
•Text Data
•Images
•Videos
•Speech
•Signals

6. Sources of
Medical
data
•General numerical information, such as vital signs like heart
rate, respiratory rate, and temperature.
•Diagnostic-related information, like laboratory test results
from blood tests, genetic tests, culture results, and so on. It
can also include imagery like x-rays, CT Scans, MRI Scans.
•Text information like Treatment information, discharge
summary
•Other data including but not limited to – Administrative
data, Claims data, Health survey, patient diseases and
registries,

7. Machine
Learning,
Deep
Learning
1.Machine learning - Computers learn to think
and act with less human intervention.
Requires less computing power. Machine
learning can numeric and text data which are
structured.
1.Deep learning is about computers learning
to think using structures modelled on the
human brain – Neural networks. Requires high
computing power. Deep learning can analyze
images, videos, and unstructured data in ways
machine learning can’t easily do.

8. How is medical data used

9. Types of
medical
data analysis
Descriptive
Diagnostic
Predictive
Prescriptive
Discovery

10. Descriptive Analysis

11. Diagnostic Analysis

12. Predictive Analysis

13. Predictive Analysis

14. Prescriptive Analysis
Covid Vaccination
Prescription

15. Discovery Analysis
• Genome sequencing for COVID-19 is about developing a complete
picture of a virus's RNA.
• It involves obtaining positive COVID-19 samples and generating a complete
RNA sequence of that virus from that sample.

16. Future of Medical Data Analysis
Digital devices

17. Cloud Tools
for Medical
Data
Analysis
•IBM Watson Health, IBM Health Insights
•Google Health Care Data Engine
•Amazon Healthlake
•Azure for Healthcare

18. Amazon Healthlake

19. Use Case
Predictive Analytics with
Pima India Dataset
• Pima Indians are a Native American Tribe
• Genetically Susceptible to Obesity and
Diabetes one the raise in this century
• Dataset from National Institute of Diabetes
and Digestive and Kidney Diseases.
• Pregnancies, Glucose, Blood Pressure, Skin
Thickness, Insulin, BMI, Diabetes Pedigree
Function, Age, Outcome
• https://www.kaggle.com/datasets/uciml/pi
ma-indians-diabetes-database