I am researching ways to apply representation learning to time series data in healthcare.
Patient information recorded in electronic health records (EHR) are recorded as asynchronous events. For machine learning models, it is often convenient to convert these representations into synchronous blocks of time. This transformation results in discretised data with exceptionally high amounts of missing data (i.e. blood samples are not drawn hourly). I am interested in ways to represent patient trajectories as a single vector in a robust manner so that we can fine tune models for automated clinical decisions, perform domain adaptation to span multiple institutes, and apply constraints to learn fair representations.
- Che, Zhengping, et al. "Recurrent neural networks for multivariate time series with missing values." Scientific reports 8.1 (2018): 6085.
- Song, Huan, et al. "Attend and diagnose: Clinical time series analysis using attention models." Thirty-Second AAAI Conference on Artificial Intelligence. 2018.
- Zemel, Rich, et al. "Learning fair representations." International Conference on Machine Learning. 2013.
- Alsentzer, Emily, et al. "Publicly available clinical BERT embeddings." arXiv preprint arXiv:1904.03323 (2019).
- Purushotham, Sanjay, et al. "Variational recurrent adversarial deep domain adaptation." (2016).
For a list of my publications, please check my Google Scholar page.