This is our software presentaton topic & we describe this is linked with health science

1. Artificial intelligence for diabetes case
management: The intersection of
physical and mental health
[Casey C. Bennetta , b,∗
a College of Computing and Digital Media, DePaul University, Chicago, IL, USA
b AI & Machine Learning Group, CVS Health, Chicago, IL, USA]

2. Presented by:
Jannatul Nayeem Himel (ID: 163-15-8395)
Maksudur Rahman ( ID: 162-15-7955)
Md Nazmul Hossain Mir (ID: 163-15-8386)
Rajiur Rahman (ID: 161-15-6793
Presented to:
Mr. Abdus Sattar
Assistant Professor
Department of CSE
Daffodil International University
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3. Contents:
1. Abstract
2. Introduction
a. Objectives
b. Research Goal
c. Research Questions
3. Literature Review
4. Research Methods
5. Main work of the paper
6. Results/Findings
7. Conclusion
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4. Abstract
4
 Objective
 Methods
 Result
 Conclusion

5. Introduction
5
(a) Objectives:
• 30 million – 9.4% - 327 Billion USD – 2017
Complications - cardiovascular, renal, neuropathic, ophthalmic
High moratality rate, Depression, Bipolar
• Focused patients = Already diabetic, not pre-diabetic/future
diabetics

6. Introduction
6
(b) Research Goal
• Evaluate – cluster trajectories of diabetic patients
• Finding sub-groups – reduce later development of
complications
• Deployable system – real world – scalable,sustainable
• Integrate with existing practices – healthcare system

7. Introduction
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(c) Research Questions
• How Intersection of physical and mental health can be used to produce tools
to enhance case management for diabetes care?
• Are there particular co-morbidities causing patient trajectories to worsen (i.e.
switch from one cluster to another) that are alterable through some case
management intervention?
• Can we not only accurately cluster individual patients, but also predict when
one may be likely to switch clusters before such a switch occurs?

8. Place your screenshot here
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9. Literature Review
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 Systems to simulate and augment clinical-decision making in co-occurring
 physical and mental chronic illness.
 Data-driven approaches to selecting optimal treatments for mental health
 Robotic application – Dementia and aging-related issues
 MOSAIC Project – Europe – Complications – Type II – 83.8%
 Makino and others – Predict renal disease
 CDC – US – Forcasting models – Screening health issues

10. Research Methods
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(a)Data:
1) Insurance Claims Data
2) Case Management Notes
3) Social Determinants of Health
(b)Modelling Approach:
1) EM Clustering
2) Logistic Regression
3) Neural Network
4) SVM and others

11. Research Methods
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(c) Feature Engineering
Raw data – Geographic Risk Factor

12. Main work
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 Finding critical connection – Mental Health Issue – Diabetic
complications
 Predicting complications (Renal etc) development – Insurance claims
data
 Narrow down – number of patients – cost effective

13. Results
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14. Results
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15. Results
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16. Conclusion
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Ethical statement:
The authors have no ethical conflicts, financial or personal or
otherwise, related to the research presented herein.
Conflicts of interest:
The authors have no conflict of interest related to the research
presented herein. The information contained herein is not confidential,
and has been presented in various public presentations over the past 2
years.
Acknowledgements:
This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.

17. Thank You
Any Questions?
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