Dr. AuYeung has a background in engineering and applied mathematics from his college and graduate school years, during which he became well-versed in biomedical data analytics. The topic of his Ph.D. dissertation was risk stratification and prediction of sudden cardiac arrest based upon the heart rate time series of patients with heart failure. After obtaining his Ph.D. in 2016, he went to Massachusetts General Hospital with a focus on reduction of false alarms in intensive care units. Since joining OHSU in 2019, he has been building on his expertise in biomedical data analytics and researching the characterization and prediction of neuropsychiatric symptoms in patients with dementia.

Publications

Au-Yeung WTM, Miller L, Beattie Z, May R, Cray HV, Kabelac Z, Katabi D, Kaye J, Vahia IV. Monitoring Behaviors of Patients With Late-Stage Dementia Using Passive Environmental Sensing Approaches: A Case Series. Am J Geriatr Psychiatry. 2021 Apr 22;. doi: 10.1016/j.jagp.2021.04.008. [Epub ahead of print] PubMed PMID: 34039534.

Au-Yeung WTM, Miller L, Beattie Z, Dodge HH, Reynolds C, Vahia I, Kaye J. Sensing a problem: Proof of concept for characterizing and predicting agitation. Alzheimers Dement (N Y). 2020;6(1):e12079. doi: 10.1002/trc2.12079. eCollection 2020. PubMed PMID: 32864417; PubMed Central PMCID: PMC7443743.

Au-Yeung WTM, Kaye JA, Beattie Z. Step Count Standardization: Validation of Step Counts from the Withings Activite using PiezoRxD and wGT3X-BT. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4608-4611. doi: 10.1109/EMBC44109.2020.9176511. PubMed PMID: 33019020; PubMed Central PMCID: PMC7759156.

Beattie Z, Miller L, Almirola C, Au-Yeung WTM, Bernard H, Cosgrove K, Dodge H, Gamboa C, Golonka O, Gothard S, Harbison S, Irish S, Kornfeld J, Lee J, Marcoe J, Mattek N, Quinn C, Reynolds C, Riley T, Rodrigues N, Sharma N, Siqueland M, Thomas N, Truty T, Wall R, Wild K, Wu C, Karlawish J, Silverberg N, Barnes L, Czaja S, Silbert L, Kaye J. The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community. Digital Biomarkers. 2020; 4(1):100-118. doi: 10.1159/000512208.

Au-Yeung WTM, Sevakula RK, Sahani AK, Kassab M, Boyer R, Isselbacher EM, Armoundas AA. Real-Time Machine Learning-Based Intensive Care Unit Alarm Classification without Prior Knowledge of the Underlying Rhythm, European Heart Journal - Digital Health, 2021;, ztab058,

Sevakula RK, Au-Yeung WTM, Singh JP, Heist EK, Isselbacher EM, Armoundas AA. State-of-the-Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System. J Am Heart Assoc. 2020 Feb 18;9(4):e013924. doi: 10.1161/JAHA.119.013924. Epub 2020 Feb 13. Review. PubMed PMID: 32067584; PubMed Central PMCID: PMC7070211.

Au-Yeung WTM, Sahani AK, Isselbacher EM, Armoundas AA. Reduction of false alarms in the intensive care unit using an optimized machine learning based approach. NPJ Digit Med. 2019;2:86. doi: 10.1038/s41746-019-0160-7. eCollection 2019. PubMed PMID: 31508497; PubMed Central PMCID: PMC6728371.

Au-Yeung WTM, Reinhall PG, Bardy GH, Brunton SL. Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data. PLoS One. 2018;13(11):e0207215. doi: 10.1371/journal.pone.0207215. eCollection 2018. PubMed PMID: 30427880; PubMed Central PMCID: PMC6235358.

Au-Yeung WTM, Reinhall PG, Poole JE, Anderson J, Johnson G, Fletcher RD, Moore HJ, Mark DB, Lee KL, Bardy GH. SCD-HeFT: Use of R-R interval statistics for long-term risk stratification for arrhythmic sudden cardiac death. Heart Rhythm. 2015 Oct;12(10):2058-66. doi: 10.1016/j.hrthm.2015.06.030. Epub 2015 Jun 19. PubMed PMID: 26096609; PubMed Central PMCID: PMC4583791.