Artificial intelligence (AI) is revolutionising healthcare. Increasingly sensitive methods of data acquisition along with powerful machine-learning-based analysis methods have enabled the development of novel and innovative technologies to be used in the diagnosis and treatment of many diseases.
A leader in the field, Professor Wynne Hsu has developed numerous data mining, analytic, and knowledge discovery tools for biological and medical applications. These include tools that enable the exploration of disease incidence and the subsequent prediction of disease progression, tools that facilitate the identification of drug-disease-lab interactions for personalised medication recommendation, and tools for ‘data cleaning’, whereby data quality is improved through the identification of data artifacts, which are consequential of erroneous links between data entries of multiple database resources.
Prof Hsu currently pursues novel AI-based medical applications in collaboration with Singapore Health Services (SingHealth) to transform chronic care for hypertension, diabetes and hyperlipidemia (DHL) using AI. Leading a multidisciplinary team of clinicians, health services researchers and data scientists, Prof Hsu is currently developing an AI system known as JARVISDHL, which integrates similarity analysis, disease modelling and image analysis to identify patients that are at a high risk of disease complications in an Asian setting. The tool also guides patients in managing their chronic conditions through shared decision making so as to empower them to take charge of their own treatment options and lifestyle choices.
Successful implementation of the JARVISDHL system would achieve the vision of a proactive, personalised and right-site care for patients that provides evidence-based treatment options, quantifies personalised treatment benefits and utilise intelligent nudges for positive lifestyle change to minimise the risk of disease complications.
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Oei, R. W., Hsu, W., Lee, M. L., & Tan, N. C. (2023). Using similar patients to predict complication in patients with diabetes, hypertension, and lipid disorder: a domain knowledge-infused convolutional neural network approach. Journal of the American Medical Informatics Association, 30 (2), 273-281.
Foo, A., Hsu, W., Lee, M. L., & Tan, G. S. (2022, August). DP-GAT: A Framework for Image-based Disease Progression Prediction. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2903-2912).
Bhoi, S., Lee, M. L., Hsu, W., Fang, A. H. S., & Tan, N. C. (2022). Chronic Disease Management with Personalized Lab Test Response Prediction. In IJCAI (pp. 5038-5044).
Gao, Q., Tan, N. C., Fang, H. S. A., Lee, M. L., & Hsu, W. (2022). Glycaemic control of Asian patients with type-2 diabetes mellitus on tiered up-titration of metformin monotherapy: A one-year real-world retrospective longitudinal study in primary care. Diabetes Research and Clinical Practice, 187, 109874.
Bhoi, S., Lee, M. L., Hsu, W., Fang, H. S. A., & Tan, N. C. (2021). Personalizing medication recommendation with a graph-based approach. ACM Transactions on Information Systems (TOIS), 40 (3), 1-23.
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