The Nemati Lab at Emory University



Machine Learning in Critical Care

  1. Predicting Sepsis
  2. Lehman, L, R Adams, L Mayaud, G Moody, A Malhotra, R Mark, and S Nemati. 2014. “A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction.” Biomedical and Health Informatics, IEEE Journal of. 2015 May;19(3):1068-76.
    Nemati, S, and RP Adams. 2014. “Supervised Learning in Dynamic Bayesian Networks.” Neural Information Processing Systems (NIPS) Workshop on Deep Learning and Representation Learning. Montreal, Canada. Publisher's Version

  3. Medication Dosing
  4. Nemati, Shamim, Mohammad M. Ghassemi, and Gari D. Clifford. 2016. “ Optimal Medication Dosing from Suboptimal Clinical Examples: A Deep Reinforcement Learning Approach .” Engineering in Medicine and Biology Society (EMBC), 2016 Annual International Conference of the IEEE, Orlando, FL. --Accepted

  5. Sentiment Analysis
  6. Ghassemi, Mohammad M., Roger G. Mark, and Shamim Nemati. 2015. “A visualization of evolving clinical sentiment using vector representations of clinical notes.” Computing in Cardiology Conference (CinC). Nice, France.

Computational Neuroscience/Psychiatry

  1. Deep Brain Stimulation
  2. Harati, Sahar, Andrea Crowell, Jun Kong, Helen Mayberg and Shamim Nemati. 2016. “ Discriminating Clinical Phases of Recovery From Major Depressive Disorder using the Dynamics of Facial Expression .” Engineering in Medicine and Biology Society (EMBC), 2016 Annual International Conference of the IEEE, Orlando, FL. --Accepted

  3. Brain Machine Interface
  4. Nemati, S, SW Linderman, and Z Chen. 2014. “A Probabilistic Modeling Approach for Uncovering Neural Population Rotational Dynamics.” Cosyne.PDF icon draft_pjpca.pdf
    Fagg, Andrew H, Nicholas G Hatsopoulos, Victor de Lafuente, Karen A Moxon, Shamim Nemati, James M Rebesco, Ranulfo Romo, et al. 2007. “Biomimetic brain machine interfaces for the control of movement.” The Journal of Neuroscience vol 27,no 44. Society for Neuroscience: 11842–11846.


  1. Arrhythmia Detection
  2. Nemati, Shamim, Mohammad M. Ghassemi, Vaidehi Ambai, Nino Isakadze, Oleksiy Levantsevych, Amit Shah, and Gari D. Clifford. 2016. “ Monitoring and Detecting Atrial Fibrillation using Wearable Technology .” Engineering in Medicine and Biology Society (EMBC), 2016 Annual International Conference of the IEEE, Orlando, FL. --Accepted

  3. Signal Fusion
  4. Shamim, Nemati, Malhotra Atul, Clifford Gari D, and others. 2010. “Data fusion for improved respiration rate estimation.” EURASIP journal on advances in signal processing 2010. Hindawi Publishing Corporation.

Cardiovascular and Respiratory Modeling

  1. Transfer Entropy
  2. Nemati, S, BA Edwards, Joon Lee, B Pittman-Polletta, JP Butler, and A Malhotra. 2013. “Respiration and heart rate complexity: effects of age and gender assessed by band-limited transfer entropy.” Respiratory physiology & neurobiology 189. Elsevier: 27–33.
    Lee, Joon, Shamim Nemati, Ikaro Silva, Bradley A Edwards, James P Butler, and Atul Malhotra. 2012. “Transfer entropy estimation and directional coupling change detection in biomedical time series.” Biomedical engineering online 11. BioMed Central: 1–17.

  3. Transfer Path Analysis
  4. Nemati, Shamim, Bradley A Edwards, Scott A Sands, Philip J Berger, Andrew Wellman, George C Verghese, Atul Malhotra, and James P Butler. 2011. “Model-based characterization of ventilatory stability using spontaneous breathing.” Journal of Applied Physiology 111. Am Physiological Soc: 55–67.

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