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
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.
I 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.
Publications
- Budiarto A, Tsang KCH, Wilson AM, Sheikh A, Shah SA. Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review. JMIR AI 2023;2:e46717. doi: 10.2196/46717
- Yusuf, I., Pardamean, B., Baurley, J.W. et al. Genetic risk factors for colorectal cancer in multiethnic Indonesians. Sci Rep 11, 9988 (2021). DOI: 10.1038/s41598-021-88805-4
- Steven Amadeus, Tjeng Wawan Cenggoro, Arif Budiarto, Bens Pardamean. A Design of Polygenic Risk Model with Deep Learning for Colorectal Cancer in Multiethnic Indonesians. Procedia Computer Science, Volume 179, 2021, Pages 632-639, ISSN 1877-0509, DOI: 10.1016/j.procs.2021.01.049.
- Bharuno Mahesworo, Arif Budiarto, Bens Pardamean. Systematic Evaluation of Cross Population Polygenic Risk Score on Colorectal Cancer. Procedia Computer Science, Volume 179, 2021, Pages 344-351, ISSN 1877-0509, DOI: 10.1016/j.procs.2021.01.015.
- Arif Budiarto, Bharuno Mahesworo, Alam Ahmad Hidayat, Ika Nurlaila, Bens Pardamean. Gaussian Mixture Model Implementation for Population Stratification Estimation from Genomics Data. Procedia Computer Science, Volume 179, 2021, Pages 202-210, ISSN 1877-0509, DOI: 10.1016/j.procs.2020.12.026.
- Nicholas Dominic, Daniel, Tjeng Wawan Cenggoro, Arif Budiarto, Bens Pardamean. Transfer learning using inception-ResNet-v2 model to the augmented neuroimages data for autism spectrum disorder classification. Commun. Math. Biol. Neurosci., 2021 (2021), Article ID 39 DOI: 10.28919/cmbn/5565
- Nurlaila I, Hidayat AA, Budiarto A, Mahesworo B, Purwandari K, Pardamean B. Dietary Intake as Determinant Nongenetic Factors to Colorectal Cancer Incidence and Staging Progression: A Study in South Sulawesi Population, Indonesia. Nutr Cancer. 2021 Jan 7:1-9. DOI: 10.1080/01635581.2020.1839516. Epub ahead of print. PMID: 33410363.
- B. Mahesworo, A. Budiarto, A. A. Hidayat, H. Soeparno and B. Pardamean. Sleep Quality and Daily Activity Association Assessment From Wearable Device Data. 2020 International Conference on Information Management and Technology (ICIMTech), 2020, pp. 197-202, DOI: 10.1109/ICIMTech50083.2020.9211281.
- Bharuno Mahesworo, Tjeng Wawan Cenggoro, Arif Budiarto, Favorisen Rosyking Lumbanraja, Bens Pardamean. Phosphorylation site prediction using gradient tree boosting, Commun. Math. Biol. Neurosci., 2020 (2020), Article ID 48 DOI:
- Pardamean B, Soeparno H, Budiarto A, Mahesworo B, Baurley J. Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health. Healthc Inform Res. 2020 Apr;26(2):83-92. DOI: 10.4258/hir.2020.26.2.83. Epub 2020 Apr 30. PMID: 32547805; PMCID: PMC7278513.
- Tjeng Wawan Cenggoro, Bharuno Mahesworo, Arif Budiarto, James Baurley, Teddy Suparyanto, Bens Pardamean. Features Importance in Classification Models for Colorectal Cancer Cases Phenotype in Indonesia. Procedia Computer Science, Volume 157, 2019, Pages 313-320, ISSN 1877-0509, DOI: 10.1016/j.procs.2019.08.172.
- Arif Budiarto, Bharuno Mahesworo, James Baurley, Teddy Suparyanto, Bens Pardamean. Fast and Effective Clustering Method for Ancestry Estimation. Procedia Computer Science, Volume 157, 2019, Pages 306-312, ISSN 1877-0509, DOI: 10.1016/j.procs.2019.08.171.
- Bens Pardamean, Haryono Soeparno, Bharuno Mahesworo, Arif Budiarto, James Baurley. Comparing the Accuracy of Multiple Commercial Wearable Devices: A Method. Procedia Computer Science, Volume 157, 2019, Pages 567-572, ISSN 1877-0509, DOI: 10.1016/j.procs.2019.09.015.
- Favorisen Rosyking Lumbanraja, Bharuno Mahesworo, Tjeng Wawan Cenggoro, Arif Budiarto, Bens Pardamean. An Evaluation of Deep Neural Network Performance on Limited Protein Phosphorylation Site Prediction Data. Procedia Computer Science, Volume 157, 2019, Pages 25-30, ISSN 1877-0509, DOI: 10.1016/j.procs.2019.08.137.
- James W. Baurley, Arif Budiarto, Muhamad Fitra Kacamarga, and Bens Pardamean. 2018. A Web Portal for Rice Crop Improvements. Int. J. Web Portals 10, 2 (July 2018), 15–31. DOI: 10.4018/IJWP.2018070102