Paper presentation at the London Computing Conference 2017 Abstract- Artificial Intelligence (AI) and Machine Learning (ML) have shown great promise in the field of medicine and healthcare. This paper seeks to understand how AI and ML have been applied to the realm of patient care within the context of chronic disease, with a specific focus on Inflammatory Bowel Disease (IBD). First we present an overview of IBD, highlighting the nature of the disease and some of the challenges the various stakeholders face within the framework of outpatient care. Then we outline the current state of research into the application of AI and ML in general clinical care and in relation to IBD. After which we explore how both AI and ML have been utilized to help with the outpatient treatment of chronic illnesses which share similar challenges to that of IBD, such as Diabetes, mental health conditions and Parkinson ’s disease, so as to gain precedent into how similar techniques may be used to assist in IBD outpatient support. Finally, we discuss the work that has passed and explore the prospect of future research into the field.

1. Nader Al-shamma
Dr Ali Jwaid
Intelligent Systems and Inflammatory Bowel Disease:
Exploring the Potential for Outpatient Support
Computing Conference 2017
18-20 July 2017 | London UK

2. What is the problem?
“[…]a diagnosis of IBD is not a death sentence, but it
can often be a life sentence. [..] For patients and their families, the impact
of a chronic disease will bring changes in their personal, social and
emotional lives.”
Thomas, G (2008)
Counselling and Reflexive Research in Healthcare : Working Therapeutically with Clients
with Inflammatory Bowel Disease, Jessica Kingsley Publishers, London.
The solution?
Can advances in machine learning and artificial intelligence help tackle some of the challenges faced by those
affected by IBD?

3. How big is the problem?
Global rates of IBD are increasing, as a
result the illness is fast becoming an
international problem.
3 Million
Across
Europe
€4.6 to €5.6
Billion
Per year
300,000
In
UK

4. Areas of focus:
Diet and Nutrition
Lifestyle and
Self-management
Patient Monitoring
and Insight

5. Applied AI and Machine Learning: Application Domain
Prognostics
Diagnostics Outpatient Support (?)

6. AI and Machine Learning in Action
Diagnosis:
● Matalka et al. (2013) Artificial Neural Network (ANN) and a Probabilistic Neural Network(PNN) used to diagnose IBD.
● 98.31% Rate of Accuracy.
Prognosis:
● Waljeeet al. 2010 used Random Forest to analysis laboratory data to determinethe success of thioprinetreatment.Results: Area under Receiver Operating Characteristic
(AUROC) curve of 0.856 vs AUROC of 0.594 from traditional testing techniques.
● Hardalaç et al 2015 used ANN to determine the success of treating patients with the immunosuppressant drug Azathioprine. Taking into
account patient health and lifestyle metrics. Results: predict the efficacy of Azathioprine use with a precision of 79%.
Outpatient Support: ?????

7. Next Steps:

8. Contact Nader Al-Shamma
Nader.alshamma@ieee.org
twitter.com/nadershamma
Nadershamma.com
uk.linkedin.com/in/nadershamma

9. Credits
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