Publication

Peer Reviewed Journal Articles

  1. Podell, J., Pergakis, M., Yang, S., Felix, R., Parikh, G., Chen, H., Chen, L., Miller, C., Hu, P. and Badjatia, N., 2022. Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications. Neurocritical Care, pp.1-14.
  2. Chen, L.K., Ramsey, J. and Dubrawski, A., 2021. Affect, Support, and Personal Factors: Multimodal Causal Models of One-on-one Coaching. Journal of Educational Data Mining, 13(3), pp.36-68.
  3. Yoon JH, Mu L,  Chen L, Dubrawski A, Hravnak M, Pinsky MR, Clermont G. Predicting tachycardia as a surrogate for instability in the intensive care unit. Journal of clinical monitoring and computing. 2019 Feb 14:1-3.
  4. Bose E, Chen L, Clermont G, Dubrawski A, Pinsky MR, Ren D, Hoffman LA, Hravnak M. Risk for cardiorespiratory instability following transfer to a monitored step-down unit. Respiratory care. 2017 Apr 1;62(4):415-22.
  5. Chen L, Ogundele, O., Clermont, G., Hravnak, M., Pinsky, M.R. and Dubrawski, A.W., 2017. Dynamic and personalized risk forecast in step-down units. Implications for monitoring paradigms. Annals of the American Thoracic Society, 14(3), pp.384-391.
  6. Chen L, Dubrawski, A., Wang, D., Fiterau, M., Guillame-Bert, M., Bose, E., Kaynar, A.M., Wallace, D.J., Guttendorf, J., Clermont, G. and Pinsky, M.R., 2016. Using supervised machine learning to classify real alerts and artifact in online multi-signal vital sign monitoring data. Critical care medicine, 44(7), p.e456
  7. Hravnak M, Chen L, Dubrawski, A., Bose, E., Clermont, G. and Pinsky, M.R., 2016. Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. Journal of clinical monitoring and computing, 30(6), pp.875-888.
  8. Hravnak M, Chen L, Dubrawski, A., Bose, E. and Pinsky, M.R., 2015. Temporal distribution of instability events in continuously monitored step-down unit patients: implications for rapid response systems. Resuscitation, 89, pp.99-105.
  9. Dubrawski A, Ostlund J, Chen L, Computationally efficient scoring of activity using demographics and connectivity of entities.  Artur Dubrawski, John K. Ostlund, Lujie Chen, Andrew W. Moore. Information Technology and Management  04/2012; 11(2):77-89.

Peer-Reviewed Conference Proceedings

  1. Chen L. Gerritsen D.  Building Interpretable Descriptors for Student Posture Analysis in a Physical Classroom. In Proceedings of the 14th International Conference on Educational Data Mining (EDM). June 2021.
  2. Mandalapu, V., Chen, L, Chen, Z, Gong, J., Student-centric Model of Login Patterns: A Case Study with Learning Management Systems. In Proceedings of the 14th International Conference on Educational Data Mining (EDM). June 2021.
  3. Chen L.  Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data.  In Proceedings of the 11th International Learning Analytics and Knowledge (LAK) Conference. March 2021.
  4. Goswami M,  Chen, L, Dubrawski A. Discriminating Cognitive Disequilibrium and Flow in Problem Solving: A Semi-supervised Approach Using Involuntary Dynamic Behavioral Signals. In AAAI 2020 Special Track on AI for Social Impact.
  5. Chen, L, Gjekmarkaj E, Dubrawski A. Parent as a Companion for Solving Challenging Math Problems: Insights from Multi-modal Observational Data. June 2019, In Proceedings of the 12th International Conference on Educational Data Mining (EDM).
  6. Chen, L. Supporting Math Problem Solving Coaching for Young Students: A Case for Weak Learning Companion. In Proceedings of the 10th International Conference on Artificial Intelligence in Education (AIED). Young Researcher Track paper. June 2018.
  7. Chen L, Dubrawski A. Accelerated apprenticeship: Teaching data science problem solving skills at scale. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale 2018 Jun 26 (pp. 1-4).
  8. Chen L, Dubrawski A. Learning from Learning Curves: Discovery of Interpretable Learning Trajectory Groups. In Proceedings of the 7th International Learning Analytics and Knowledge (LAK) Conference. March 2017.
  9. Chen L, Li X, Xia Z, Song Z, Morency LP, Dubrawski A. Riding an emotional roller-coaster: A multimodal study of young child’s math problem solving activities. June 2016, In Proceedings of the 9th International Conference on Educational Data Mining (EDM). (Exemplary paper nomination)
  10. Chen L, Clermont G,  Hravnak M, Pinsky MR, Dubrawski A. A Framework for Visual Tracking of Risk and its Drivers in Monitoring Patients Susceptible for Cardiorespiratory Instability.  November 2016, The American Medical Informatics Association Annual Symposium.
  11. Chen L, Dubrawski A, Clermont G, Hravnak M, Pinsky MR. Modelling Risk of Cardio-Respiratory Instability as a Heterogeneous Process.  November 2015, The American Medical Informatics Association Annual Symposium. (2nd place student paper competition award, 3rd place for “Best of Student Papers in Knowledge Discovery and Data Mining” by Knowledge Discovery and Data Mining (KDDM) Working Group)
  12. Dubrawski A, Sarkar P, Chen L. Trade-offs between Agility and Reliability of Predictions in Dynamic Social Networks Used to Model Risk of Microbial Contamination of Food.  International Conference on Advances in Social Network Analysis and Mining, ASONAM, Athens, Greece, 20-22 July 2009  (Best paper award)

Peer Reviewed Workshop Paper

  1. Chen L, Admoni H,  Dubrawski A, Toward A Companion Robot Fostering Perseverance in Math – A Pilot Study. March 2018, Human Robot Interaction 2018, Robot for Learning Workshop
  2. Mandalapu, V., Gong, J., & Chen, L.  Do we need to go Deep? Knowledge Tracing with Big Data. AAAI Workshop on AI Education, 2021

Conference/Poster Presentation (Juried/Refereed) – selected 

  1. Chen L, Dubrawski A, Pellathy T, J. Yoon, Clermont G, Pinsky MR, Hravnak M. Artificial Intelligence Assists Junior Clinicians in Assessing Risk of Severe Cardio-respiratory Instability in Monitored Patients. Intensive Care Medicine Experimental. 2019.
  2. Chen L, Clermont G, Pellathy T, Wertz A, Yoon J, Pinsky MR, Dubrawski A, Hravnak M. Stratifying Severity of Instability in Continuously Monitored Patients. Critical Care Medicine. 2019 . (Gold Snapshot Award SCCM 2019)
  3. Chen L, Dubrawski A, Clermont G, Pellathy T, Wertz A, Pinsky MR, Hravnak M. Model Based Estimation of Instability Severity Level in Continuously Monitored Patients. Intensive Care Medicine Experimental. 2018 6(suppl 2):59
  4. Wang X, Chen L, Hravnak M, Clermont G, Pinsky MR, Dubrawski A. Utility of Anti-hypertension Prescription Orders in Predicting Future Hypertensive Instability Events. The American Medical Informatics Association Annual Symposium, Washington, DC, November 4-8, 2017
  5. Chen L, Clermont G, Hravnak M,  Pinsky MR.  Dubrawski A, . Personalized Cardiorespiratory Instability Risk Evolution Among Continuously Monitored Patients. Critical Care Medicine. 2017.
  6. Hravnak M, Chen L, Dubrawski A, Clermont G, Pinsky MR. Machine learning can pre-identify patients at risk for cardiorespiratory instability before a medical emergency team call. Critical Care Medicine. 2017. (Star Research Presentation)
  7. Chen L,  Hravnak M, Clermont G, Pinsky MR,  Dubrawski A.  Cardiorespiratory instability risk escalation patterns: an association study with risk factors and length of stay. Intensive Care Medicine. 2016.
  8. Chen L, Dubrawski A, Clermont G, Hravnak M, Pinsky MR . Predicting risk progression trajectory for Cardiorespiratory Instability in continuously monitored Step-down Unit patients. Congress of Critical Care Medicine. 2016.
  9. Hravnak M,  Chen L, Dubrawski A, Clermont G, Pinsky MR. Machine Learning Can Reduce Rate of Monitor Alarms​. Critical Care. 2016.
  10. Chen L,  Dubrawski A,  Hravnak M, Clermont G, Pinsky MR. Predicting Cardiorespiratory Instability risk trajectory groups in continuously monitored patients. Congress of Critical Care Medicine. 2016.
  11. Hravnak M, Chen L, Dubrawski A, Clermont G, Pinsky MR. Cardiorespiratory instability alert subtypes in monitored step-down unit patients have low entropy.  Congress of Critical Care Medicine. 2016.
  12.  Chen L, Dubrawski A, Hravnak M, Clermont G, Pinsky MR.  Forecasting cardio-respiratory instability in monitored patients: A machine learning approach. Congress of Critical Care Medicine. 2015.
  13. Hravnak M, Chen L, Dubrawski A, Wang D, Bose E, Clermont G, Pinsky MR.  Random Forest machine learning models separate vital sign events as real or artifact in continuous monitoring data. Critical Care Medicine. 2015.

Invited Professional Presentations (Non-Juried/Refereed)

  1. Augmenting Human Perceptual and Reasoning Capabilities: From Healthcare to Education, Uniformed Services University of the Health Sciences (USUHS) Big Data/ML Interest Group, February 2021