P

2. 153

3. 154

4. 155

5. 156

6. Healthcare Data Sources and Basic
Analytics
• Electronic Health Records
• Biomedical ImageAnalysis
• Sensor and Biomedical SignalAnalysis
• Genomic DataAnalysis
• Clinical Text Mining
• Mining Biomedical Literature
• Social MediaAnalysis
157

7. Clinical Text and Data Mining
158
o Data is what we collect and store, and knowledge is
what helps us to make informed decisions.
o The extraction of knowledge from data is called data
mining.
o Data mining can also be defined as the exploration and
analysis of large quantities of data in order to discover
meaningful patterns and rules.

8. Why Not Traditional Data Analysis?
159
1 Tremendous amount of data, Algorithms must be highly scalable to handle
such as terabytes of data
2 High-dimensionality of data
– Micro-array may have tens of thousands of dimensions
3- High complexity of data
– Data streams and sensor data
– Time-series data, temporal data, sequence data
– Structure data, graphs, social networks and multi-linked data
– Heterogeneous databases and legacy databases

9. Data Mining: Confluence of Multiple Disciplines
160

10. Typical Data Mining System
161

11. o AI in healthcare is the use of complex algorithms and
software to emulate human cognition in the analysis of
complicated health related data.
o Specifically, AI is the ability for computer algorithms to
approximate conclusions without direct human input.
162
Artificial Intelligence (AI)

12. o ML is a sub-field of AI, ML is a data driven approach focus
on creating algorithms that has the ability to learn from the
data without being programmed.
o Example: the Child and the hot stuff.
163
Machine Learning (ML)

13. The most popular ML tasks are:
o Classification: to predict a class of an object (for example
spam emails based on the content, or female/male based
on the web activity).
o Regression: to predict a continuous value for an object (for
example Colorectal Cancer incidence for next year).
o Clustering: to group similar objects together. 164
Machine Learning (ML) Tasks

14. 165

15. AI Application in Healthcare
166
 Clinical decision support system is an application that
uses pre-established rules and guidelines that can be
created and edited by the healthcare organization, and
integrates clinical data from several sources to
generate alerts and treatment suggestions”.