Asthma UK Centre for Applied Research

Arif Budiarto

Project: Machine Learning-based Prognostic Models for Improved Asthma Management Using UK-Wide Electronic Health Records

PhD overview

PhD Title: Machine Learning-based Prognostic Models for Improved Asthma Management Using UK-Wide Electronic Health Records

Funded by: Asthma UK Centre for Applied Research and University of Edinburgh

Supervisors: Dr Ahmar Shah and Professor Aziz Sheikh

Based at: University of Edinburgh

Email: s1461240@sms.ed.ac.uk

Arif Budiarto
Asthma UK Centre for Applied Research PhD student, Arif Budiarto

This PhD project aims to develop risk prediction models that can help improve the management of patients with asthma, thereby improving their lives and reducing the healthcare burden. More specifically, I will leverage existing healthcare datasets available through the Asthma UK Centre for Applied Research to develop and validate algorithms that can predict asthma attacks and prevent them.

Asthma attacks occur after a sustained worsening of symptoms that can potentially be life-threatening if not promptly treated. Asthma attacks often lead to hospitalisation and represent a significant socioeconomic burden. Consequently, early identification of such episodes can prompt early intervention and prevent severe episodes.

will use various data-driven methods including both traditional statistical methods and machine learning methods in this project. This includes survival analysis using cox regression and supervised learning methods such as logistic regression and decision trees. I will also explore the use of deep learning methods to investigate if we can further improve risk prediction algorithms.

About me

I am a computer scientist with a primary research focus on the implementation of computer science methodologies (machine learning, AI, bioinformatics) in the healthcare domain/medical informatics.

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