1. PEBI 2016-2017
ANAP-ATIH 2020

2. About us : Deep Data
Rayhana MAZHOUD
Sahar SAYEB
Sawsen LARBI
Med. Amine KAMMOUN
Mohamed SELTEN
Rawia BEN HAMIDA

3. Outline
Introduction
1
2
3
4
5
6
7
Modeling
Analysis
Reporting
Data Minning
Big Data
Conclusion

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INTRODUCTION
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5. 5
Identify waste in
the system
Reduces the risk
of bottlenecks
Access and share
information
Real-time
analysis & quick
navigation
Decision making
BI
Know your
business
Introduction

6. 6
• Developing recommendations
and tools
• Helps health-care establishment
• Improving the medical service
• Optimize their management
and organization
• Collecting and analyzing the data of
medical establishments
• Realizes studies on the hospitable
costs
• Participates in the mechanisms of
financing of establishments.
HOSPI DIAG
Entreprise Presentation

7. 7
Procedure of realization

8. Modeling
& Analysis
Data Mining
Big Data
8
Used Tools
Reporting

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Modeling
9

10. 10
Subject-oriented
Integrated Non-volatile
Time-variant
Modeling : Data Warehouse

11. 11
Modeling : Data Warehouse

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ANALYSIS
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13. 13
Fast Response Time
Agregation
Simple and rapid access
Deploy
Hierarchy
Analysis : OLAP

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REPORTING
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15. Reporting
The process of receiving/ providing
information or reports
Make Better decisions
Understand better your business
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16. Reporting
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Histogram of number of stay/ session

17. Reporting
17Number of ALD' sessions at EBNL

18. Reporting
18Histogram of number of ALD’ sessions by establishment's category

19. Reporting
19Histogram number of beds by regions

20. Reporting
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Top 10 establishments according to Ald sessions number

21. Reporting
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AP-HP-Seine-Saint-Denis activity domains
AP-HP-ValDe-Mane activity domains: activity domains
AP-HP-Haut-Seine activity domains
AP-HPParis activity domains

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DATA MINING
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23. Data Mining Techniques
UnSupervised
Learning
Supervised
Learning
23

24. • For Example : Patients coming from the region of CH BOURG EN BRESSE
and who are <75 years ,, go More frequently in a short time of stay in
ALD per session.
Unsupervised learning : Association Rule
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25. 25
Gbm
Deep
Learning
Multiple
Regression
Random
ForestAs a result for the
challenge query we
choose the GBM model
(lowest RMSE )
Supervised learning : Deep learning

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BIG DATA
26

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Big Data
27
Configuration file of stream

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Big Data
28
Starting the stream

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Big Data
29
Fetched data with apache flume

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Big Data
30
Insert Data from json to sql

31. Big Data
31
Used words after the Real Madrid Vs Celta Vigo game

32. Big Data
32
Activity on the Real Madrid Facebook page

33. Big Data
33Activity on the FCBarcelone facebook page

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CONCLUSION
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35. Conclusion
35
Information into
knowlege
Decisions into
Profits
Data into
Information
Knowledge into
Decisions

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Thank You