Digital Health & AI In Healthcare Conference Papers

C23. KJ Ng, RSJ Goh, KS Fong, DHT Tan, CT Leung, SH Ong, J. Tan, M. Motani, and MJR Lim, “Artificial Intelligence-based Agglomerative Clustering of Patient Phenotypes and Disease Factors in Spontaneous Intracerebral Hemorrhage”, 2024 Congress of Neurological Surgeons (CNS) Annual Meeting, Houston, Texas, USA, Sep. 2024. [Link]

C22. K.S. Fong and M. Motani, “Explainable and Privacy-Preserving Machine Learning via Domain-Aware Symbolic Regression”, ACM Conference on Health, Inference, and Learning (ACM-CHIL 2024), New York, NY, Jun. 2024. [Link] [Link]

C21. K.S. Fong and M. Motani, “Symbolic Regression for Discovery of Medical Equations: A Case Study on Glomerular Filtration Rate Estimation Equations”, IEEE Conference on Artificial Intelligence (IEEE CAI 2024), Singapore, June 2024. [Link] [Link]

C20. E. Toh, B. Chek, S.H. Ong, P.I. Ngam, T.T. Yeo, V. Nga, M. Motani, M. Lim, “Predicting Clinically Significant Hematoma Expansion and Outcomes in Patients with Intracerebral Hemorrhage”, AI Health Summit, Singapore, Nov. 2023. [Link]

C19. E. Toh, B. Yan, I. Lim, D. Yap, W.J. Wee, K.J. Ng, V. Nga, T.T. Yeo, M. Motani, and M. Lim, “The Role of Intracranial Pressure Variability as a Predictor for Intracranial Hypertension and Mortality in Critically Ill Patients,” Brain and Spine, Vol. 3, Supp. 1, Abstracts of EANS2023, Sep 2023. [Link]

C18. D. Ho and M. Motani, “Multi-view Modelling of Longitudinal Health Data for Improved Prognostication of Colorectal Cancer Recurrence”, Machine Learning for Healthcare (MLHC), New York, NY, USA, Aug. 2023. [Link]

C17. C. Ming, G.J.W. Lee, Y.H. Teo, Y.N. Teo, E.M.S. Toh, T.Y.W. Li, C.Y. Guo, J. Ding, X. Zhou, H.L. Teoh, S.C. Seow, L.L.L. Yeo, C.H. Sia, M. Motani, and B.Y.Q. Tan, “Machine Learning Modelling to Predict Atrial Fibrillation Detection in Embolic Stroke of Undetermined Source Patients”, 9th European Stroke Conference, Munich, Germany, May 2023. [Link]

C16. D. Ho and M. Motani, “Machine and Deep Learning methods for Predicting Immune Checkpoint Blockade Response”, Machine Learning for Health (ML4H 2022), New Orleans, USA, Nov. 2022. [Link]

C15. MJR Lim, RQHao Chong, KJ Ng, B. Tan, LLL Yeo, YL Low, B. Soon, WNH Loh, K. Teo, VDW Nga, TT Yeo, and M. Motani, “Prognostication of Outcomes and Surgical Intervention in Spontaneous Intraparenchymal Hemorrhage: A Propensity Score-matched Analysis with Support Vector Machine ”, 2022 Congress of Neurological Surgeons (CNS) Annual Meeting, San Francisco, CA, USA, Oct. 2022. [Link]

C14. V.V. Lee,  A.T.L. Truong, W. Thone, X.Z. Low, N. Le, S. Vijayakumar, N.Y. Lau, C.E. Chua, K.T.H. Siah, Mehul Motani, D. Ho, and  A. Blasiak, “It takes a village: engineering and behavioral research toward the development of a digital outcome measure for constipation management that patients and doctors want to use”, United European Gastroenterology (UEG Week 2022), Vienna, Austria, Oct. 2022. [Link]

C13. A. Li, M. L. Ong, C. W. Oei, W. Lian, H. P. Phua, L. H. Htet, W. Y. Lim, and M. Motani, “Unified Auto Clinical Scoring (Uni-ACS) with Interpretable ML models”, Machine Learning for Healthcare (MLHC 2022), Durham, NC, USA, Aug. 2022. [Link]

C12. D. Ho, I.B.H. Tan, and M. Motani, “Prognosticating Colorectal Cancer Recurrence using an Interpretable Deep Multi-view Network”, Machine Learning for Health 2021 (ML4H), Proceedings for Machine Learning Research (PMLR), Dec. 2021. [Link]

C11. D. Ho, I.B.H. Tan, and M. Motani, “Deep Multi-View Learning for Colorectal Cancer Prediction”, SingHealth Duke-NUS Scientific Congress 2021, Poster presentation, Sep. 2021.

C10. D. Ho, I.B.H. Tan, and M. Motani, “Predictive models for colorectal cancer recurrence using multi-modal healthcare data”, ACM Conference on Health, Inference, and Learning (ACM-CHIL 2021), Virtual Conference, Apr. 2021. [Link]

C9. D. Ho, DQ Chong, B. Tay , IB Tan, and M. Motani, “Prognosticating colorectal cancer recurrence using machine learning techniques”, 2020 IEEE International Conference on E-Health Networking, Application and Services (HEALTHCOM) [Link]

C8. S. Liu and M. Motani, “Long-range Prediction of Vital Signs Using Generative Boosting via LSTM Networks”, NeurIPS 2019 Workshop on Machine Learning for Health (ML4H), Vancouver, Canada, Dec. 2019. [Link]

C7. M.L. Ong, A. Li, and M. Motani, “Explainable and Actionable Machine Learning Models for Electronic Health Record Data”, 17th Int’l Conf on Biomedical Eng, Abstract Number: ICBME1309, Singapore, Dec. 2019. [Link]

C6. D. Ho, F.L. Leong, D.Q.Q. Chong, I.B.H. Tan, P. Krishnaswamy, and M. Motani, “Deep Learning Based Prediction of Colorectal Cancer Recurrence and Survival”, 17th Int’l Conf on Biomedical Eng, Abstract Number: ICBME1397, Singapore, Dec. 2019.

C5. S. Liu, J. Yao and M. Motani, “Early Prediction of Vital Signs Using Generative Boosting via LSTM Networks”, IEEE BIBM 2019, San Diego,CA, USA, Nov. 2019. [Link]

C4. S. Liu, M.L. Ong, K.K. Mun, J. Yao and M. Motani, “Early Prediction of Sepsis via SMOTE Upsampling and Mutual Information Based Downsampling”, International Conference in Computing in Cardiology (CINC), Sep. 2019. [Link]

C3. J. Yao, M.L. Ong, K.K. Mun, S. Liu and M. Motani, “Hybrid Feature Learning Using Autoencoders for Early Prediction of Sepsis”, International Conference in Computing in Cardiology (CINC), Sep. 2019. [Link]

C2. S. Liu and M. Motani, “Feature Selection Based on Unique Relevant Information for Health Data”, NeurIPS 2018 Workshop on Machine Learning for Health (ML4H), Montreal, Canada, Dec. 2018. [Link]

C1. S. Liu, C. Zhou, Y. Jia, and M. Motani, “SURI: Feature Selection Based on Unique Relevant Information for Health Data”’, IEEE BIBM 2018, Madrid, Spain, Dec. 2018. [Link]

Digital Health & AI In Healthcare Journal Papers

J10. K.J. Ng, R.S.J. Goh, S.S.H. Goh, L.L.L. Yeo, M. Motani, B.Y.Q. Tan, and M.J.R. Lim, “Prediction of Poor Functional Status Post Acute Ischemic Stroke: A Machine Learning Approach”, Manuscript submitted for journal publication, 2024.

J9. C. Ming, G.J.W. Lee, Y.H. Teo, Y.N. Teo, E.M.S. Toh, T.Y.W. Li, C.Y. Guo, J. Ding, X. Zhou, H.L. Teoh, S.C. Seow, L.L.L. Yeo, C.H. Sia, M. Motani, and B.Y.Q. Tan, “Machine Learning Modelling to Predict Atrial Fibrillation Detection in Embolic Stroke of Undetermined Source Patients” Journal of Personalized Medicine, accepted, May 2024.

J8. M. Lim, R. Quek, K.J. Ng, B. Tan, L. Yeo, Y.L. Low, B. Soon, W. Loh, K. Teo, V. Nga, T.T. Yeo and M. Motani, “Prognostication of Outcomes in Spontaneous Intracerebral Hemorrhage: A Propensity Score–Matched Analysis with Support Vector Machine”, World Neurosurgery, vol. 182, Feb. 2024. [Link]

J7. J. Sumner, A. Bundele, H. W. Lim, P. Phan, M. Motani, and A. Mukhopadhyay, “Developing an artificial intelligence-driven nudge intervention to improve medication adherence: A human-centred design approach”, Journal of Medical Systems, vol. 48, article 3, Nov. 2023 [Link]

J6. E.M.S. Toh, K.J. Ng, M. Motani, and M.J.R. Lim, “Letter: Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success from Video” Neurosurgery, 93(3), Sep 2023. [Link]

J5. E.M.S. Toh, B. Yan, I.C. Lim, D. Yap, W.J. Wee, K.J. Ng, V.D.W. Nga, M. Motani, and M.J.R. Lim, “The Role of Intracranial Pressure Variability as a Predictor for Intracranial Hypertension and Mortality in Critically Ill Patients”, Journal of Neurosurgery, May 2023. [Link]

J4. A. Mukhopadhyay, J. Sumner, L.H. Ling, R. Quek, A. Tan, G.G. Teng, S.K. Seetharaman, S.P.K. Gollamudi, D. Ho, M. Motani “Personalized dosing using the CURATE.AI algorithm: Protocol for a feasibility study in patients with hypertension and type II diabetes mellitus”, Int J Environ Res Public Health. 2022 Jul 23; 19(15): 8979. [Link]

J3. M. Lim, R. Quek, K.J. Ng, W. Loh, L. Sein, K. Teo, V. Nga, T.T. Yeo and M. Motani, “Machine Learning Prognosticates Functional Outcomes better than Clinical Scores in Spontaneous Intracerebral Haemorrhage”, Journal of Stroke and Cerebrovascular Diseases, Volume 31, Issue 2, Feb. 2022. [Link] [Link]

J2. D.L.T. Wong et al., “An Integrated Wearable Wireless Vital Signs Biosensor for Continuous Inpatient Monitoring”, IEEE Sensors Journal, vol. 20, no. 1, pp. 448-462, Jan. 2020. [Link]

J1. C. Zhou, J. Yao and M. Motani, “Optimizing Autoencoders for Learning Deep Representations from Health Data”, IEEE J. Biomed. Health Inform., vol. 23, no. 1, pp. 103-111, Jan. 2019. [Link]