Patient Outcome Prediction

Medical Record

Patient Outcome Prediction

Accurate patient outcome prediction is important for severity of illness estimation, care planning, and resource allocation. We have extensively applied machine learning to two large-scale, public intensive care databases called MIMIC and eICU to predict outcomes such as in- and out-of-hospital mortality and length of stay, as well as adverse events such as hypotensive episodes.

In particular, we have developed methodologies that can identify similar patients in a data-driven way so that predictive modeling can be personalized. In order to support this line of research, we have also created a visual data exploration tool and investigated the data quality and redundancy in MIMIC.


Related Publications

JMIR

A. Kline, T. Kline, Z. Shakeri Hossein Abad, and J. Lee. Using item response theory for explainable machine learning in predicting mortality in the intensive care unit: case-based approach. Journal of Medical Internet Research, 22(9):e20268, September 2020.

EMBC2

A. Kline, T. Kline, Z. Shakeri Hossein Abad, and J. Lee. Novel feature selection for artificial intelligence using item response theory for mortality prediction. The 2020 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5729-5732, 2020.

EMBc

M. Al-Jefri, J. Lee, and M. James. Predicting acute kidney injury after surgery. The 2020 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5606-5609, 2020.

EMBC-Zahra

Z. Shakeri Hossein Abad, A. Kline, and J. Lee. Evaluation of machine learning-based patient outcome prediction using patient-specific difficulty and discrimination indices. The 2020 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5446-5449, 2020.

EMBc

F. Lucini, K. Fiest, H. T. Stelfox, and J. Lee. Delirium prediction in the intensive care unit: a temporal approach. The 2020 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5527-5530, 2020.

AI

J. Lee. Is artificial intelligence better than human clinicians in predicting patient outcomes? Journal of Medical Internet Research, 22(8):e19918, August 2020.

Pub 21

Y. Xu, J. Lee, and  J. A. Dubin. Similarity-based random survival forest, arXiv preprint arXiv:1903.01029, March 2019. 

P20

A. Sharafoddini, JA. Dubin, DM. Maslove, J. Lee.  A new insight into missing data in intensive care unit patient profiles: observational study, JMIR Med Inform 2019;7(1):e11605, January 2019.

Pub1

I. E. R. Waudby-Smith, N. Tran, J. A. Dubin, and J. Lee. Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients. PLoS One, 13(6):e0198687, June 2018.

Pub2

A. Sharafoddini, J. A. Dubin, and J. Lee. Patient similarity in prediction models based on health data: a scoping review. JMIR Medical Informatics, 5(1):e7, March 2017.

Pub3
Pub4

J. Lee and D. M. Maslove. Customization of a severity of illness score using local electronic medical record data. Journal of Intensive Care Medicine, 32(1):38-47, January 2017.

Pub5

J. Lee, E. Ribey, and J. R. Wallace. A web-based data visualization tool for the MIMIC-II database. BMC Medical Informatics and Decision Making, 16:15, February 2016.

Pub6

M. G. Shrime, B. S. Ferket, D. J. Scott, J. Lee, D. Bradford, T. Pollard, Y. M. Arabi, H. M. Al-Dorzi, R. M. Baron, M. G. Myriam Hunink, L. A. Celi, and P. S. Lai. How long is long enough? Time-limited trials for critically-ill patients with cancer. JAMA Oncology, 2(1):76-83, January 2016.

Pub7

J. Lee and D. M. Maslove. Using information theory to identify redundancy in common laboratory tests. BMC Medical Informatics and Decision Making, 15:59, July 2015.

Pub8

J. Lee, D. M. Maslove, and J. A. Dubin. Personalized mortality prediction driven by electronic medical data and a patient similarity metric. PLoS One, 10(5):e0127428, May 2015

Pub9

T. Mandelbaum, J. Lee, D. J. Scott, R. G. Mark, A. Malhotra, M. D. Howell, and D. Talmor. Empirical relationships among oliguria, creatinine, mortality, and renal replacement therapy in the critically ill. Intensive Care Medicine, 39(3):414-419, March 2013.

Pub10

D. J. Scott, J. Lee, I. Silva, S. Park, G. B. Moody, L. A. Celi, and R. G. Mark. Accessing the public MIMIC-II intensive care relational database for clinical research. BMC Medical Informatics and Decision Making, 13:9, January 2013.

P11

J. Lee, R. Kothari, J. A. Ladapo, D. J. Scott, and L. A. Celi. Interrogating a clinical database to study treatment of hypotension in the critically ill. BMJ Open, 2(3):e000916, June 2012.

P12

S. Hunziker, L. Celi, J. Lee, and M. D. Howell. Red cell distribution width improves the SAPS score for risk prediction in unselected critically ill patients. Critical Care, 16(3):R89, May 2012.

P13

J. Lee and R. G. Mark. An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care. BioMedical Engineering OnLine, 9:62, October 2010.

P14

A. Sharafoddini, J. A. Dubin, and J. Lee. Finding similar patient subpopulations in the ICU using laboratory test ordering patterns. In: Proceedings of the 2018 7th International Conference on Bioinformatics and Biomedical Science (ICBBS), 72-77, 2018.

P15

M. A. H. Zahid and J. Lee. Mortality prediction with self normalizing neural networks in intensive care unit patients. In: Proceedings of the 2018 IEEE-EMBS International Conference on Biomedical and Health Informatics, 226-229, 2018.

P16

N. Tran and J. Lee. Using multiple sentiment dimensions of nursing notes to predict mortality in the intensive care unit. In: Proceedings of the 2018 IEEE-EMBS International Conference on Biomedical and Health Informatics, 283-286, 2018.

P17

J. Lee. Personalized mortality prediction for the critically ill using a patient similarity metric and bagging. In: Proceedings of the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics, 332-335, 2016.

P18

L. Lehman, M. Saeed, W. Long, J. Lee, and R. Mark. Risk stratification of ICU patients using topic models inferred from unstructured progress notes. In: Proceedings of the American Medical Informatics Association 2012 Annual Symposium, 505-511, 2012.

P19

J. Lee, D. J. Scott, M. Villarroel, G. D. Clifford, M. Saeed, and R. G. Mark. Open-access MIMIC-II database for intensive care research. In: Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 8315-8318, 2011.

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