Research Scientist. massachusetts institute of technology
E25-505 · 77 Massachusetts Ave. · Cambridge, MA 02139 · LILEHMAN@MIT.EDU
I am a research scientist in the Laboratory for Computational Physiology (LCP) at the MIT Institute for Medical Engineering & Science (IMES). My research focuses on the use of machine learning techniques to derive insights from physiological and clical data for informed treatment decision making. My interests include representation learning, structure discovery, generative probabilistic models, switching state-space models, Bayesian non-parametric learning of disease phenotypes, and, more recently, off-policy reinforcement learning.
I received my Master’s degree in Computer Science from Georgia Institute of Technology, and my Ph.D. from Massachusetts Institute of Technology in June 2005.
Research/Post-doc position available: I am looking for a highly-motivated post-doc to join my research team.
Switching State-Space Approaches for Modeling Physiological Dynamics
- "A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction", Li-wei H. Lehman, Ryan P. Adams, Louis Mayaud, George B. Moody, Atul Malhotra, Roger G. Mark, Shamim Nemati, IEEE Journal of Biomedical and Health Informatics, 19(3):1068-1076, May 2015. doi:10.1109/JBHI.2014.2330827. Preprint.
- "Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring", Li-wei H. Lehman, Matthew J. Johnson, Shamim Nemati, Ryan P. Adams, Roger G. Mark, Chapter 11 in Advanced State Space Methods for Neural and Clinical Data, Cambridge University Press, 2015. Publisher's Version.
- "A Model-Based Machine Learning Approach to Probing Autonomic Regulation from Nonstationary Vital-Sign Time Series", Li-wei H. Lehman, Roger G. Mark, Shamim Nemati, IEEE Journal of Biomedical and Health Informatics, Vol. 22, No. 1, January 2018. doi:10.1109/JBHI.2016.2636808. Preprint available at IEEE Explorer.
- “Learning Outcome-Discriminative Dynamics in Multivariate Physiological Cohort Time Series,” Nemati S, Lehman LH, Adams RP, Proceedings of the 35th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013.
- “Discovering shared dynamics in physiological signals: Application to patient monitoring in ICU,” Lehman L, Nemati S, Adams RP, Mark R, Proceedings of the 34th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, 2012.
Deep Representation Learning from Physiological Data
- "Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics," Eric P. Lehman, Rahul G. Krishnan, Xiaopeng Zhao, Roger G. Mark, Li-wei H. Lehman, Machine Learning for Healthcare , Stanford, CA, 2018.
- “Patient Prognosis from Vital Sign Time Series: Combining Convolutional Neural Networks with a Dynamical Systems Approach,” Li-wei H. Lehman, Mohammad Ghassemi, Jasper Snoek, and Shamim Nemati, Proceedings of the Computing in Cardiology, France Nice, September 2015.
Machine Learning for Informed Clinical Decision Making
- "Evaluating Reinforcement Learning Algorithms in Observational Health Settings," Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-wei H. Lehman, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, Finale Doshi-Velez, https://arxiv.org/abs/1805.12298v1, 2018.
- "Improving Sepsis Treatment Strategies by Combining
Deep and Kernel-Based Reinforcement Learning," Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-wei H. Lehman, Andrew Ross, Aldo Faisal, Finale Doshi-Velez, Proceedings of the AMIA Annual Symposium, 2018.
- "Adherence to Individualized Fluid and Vasopressor Dosing Recommendation is Associated with Mortality Reduction in Sepsis: A Machine Learning Approach," Shamim Nemati, Matthew Stanley, Fereshteh Razmi, Timothy Buchman, Li-wei Lehman, presented at ICCAI, July 2017, abstract to appear in Journal of Critical Care.
Deriving Insights from Clinical Text: Bayesian Non-parametric Learning of Patient Phenotypes
- “Risk stratification of ICU patients using topic models inferred from unstructured progress notes,” Lehman LH, Saeed M, Long W, Lee J, Mark RG, Proceedings of the AMIA Annual Symposium, 505-511, Nov. 2012.
- “Latent Topic Discovery of Clinical Concepts from Hospital Discharge Summaries of a Heterogeneous Patient Cohort,” Lehman LH, Long W, Saeed M, Mark RG, Proceedings of the 36th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago, August 2014.
Electronic Health Records and Medical Informatics
- “ MIMIC-III, a freely accessible critical care database,” Alistair E. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mornin Feng, Mohammad Ghassemi, Benjamin Moody, Pete Szolovits, Leo Celi, Roger G. Mark, Scientific Data (Nature publishing) , 3:160035, May 2016. Published online 24 May 2016. (doi:10.1038/sdata.2016.35) (PMID:27219127)
- “Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access intensive care unit database,” Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman L, Moody G, Heldt T, Kyaw TH, Moody B, Mark RG, Crit Care Med, 39(5):952–960, 2011.