Lee C - Search Results

1

Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis.

Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, Rinehart J, Cannesson M. Hatib F, et al. Among authors: lee c. Anesthesiology. 2018 Oct;129(4):663-674. doi: 10.1097/ALN.0000000000002300. Anesthesiology. 2018. PMID: 29894315 Free article.

2

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

Lee CK, Hofer I, Gabel E, Baldi P, Cannesson M. Lee CK, et al. Anesthesiology. 2018 Oct;129(4):649-662. doi: 10.1097/ALN.0000000000002186. Anesthesiology. 2018. PMID: 29664888 Free PMC article.

3

Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery.

Wingert T, Lee C, Cannesson M. Wingert T, et al. Among authors: lee c. Anesthesiol Clin. 2021 Sep;39(3):565-581. doi: 10.1016/j.anclin.2021.03.012. Epub 2021 Jul 12. Anesthesiol Clin. 2021. PMID: 34392886 Free PMC article. Review.

4

Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study.

Cannesson M, Hofer I, Rinehart J, Lee C, Subramaniam K, Baldi P, Dubrawski A, Pinsky MR. Cannesson M, et al. Among authors: lee c. BMJ Open. 2019 Dec 2;9(12):e031988. doi: 10.1136/bmjopen-2019-031988. BMJ Open. 2019. PMID: 31796483 Free PMC article.

5

Closed-loop fluid administration compared to anesthesiologist management for hemodynamic optimization and resuscitation during surgery: an in vivo study.

Rinehart J, Lee C, Canales C, Kong A, Kain Z, Cannesson M. Rinehart J, et al. Among authors: lee c. Anesth Analg. 2013 Nov;117(5):1119-29. doi: 10.1213/ANE.0b013e3182937d61. Anesth Analg. 2013. PMID: 23835453

6

Closed-loop fluid resuscitation: robustness against weight and cardiac contractility variations.

Rinehart J, Lee C, Cannesson M, Dumont G. Rinehart J, et al. Among authors: lee c. Anesth Analg. 2013 Nov;117(5):1110-8. doi: 10.1213/ANE.0b013e3182930050. Anesth Analg. 2013. PMID: 23835454

7

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

Hill BL, Brown R, Gabel E, Rakocz N, Lee C, Cannesson M, Baldi P, Olde Loohuis L, Johnson R, Jew B, Maoz U, Mahajan A, Sankararaman S, Hofer I, Halperin E. Hill BL, et al. Among authors: lee c. Br J Anaesth. 2019 Dec;123(6):877-886. doi: 10.1016/j.bja.2019.07.030. Epub 2019 Oct 15. Br J Anaesth. 2019. PMID: 31627890 Free PMC article.

8

Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set.

Hofer IS, Lee C, Gabel E, Baldi P, Cannesson M. Hofer IS, et al. Among authors: lee c. NPJ Digit Med. 2020 Apr 20;3:58. doi: 10.1038/s41746-020-0248-0. eCollection 2020. NPJ Digit Med. 2020. PMID: 32352036 Free PMC article.

9

[No title available]

[No authors listed] [No authors listed] PMID: 33597694

10

Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality.

Lee CK, Samad M, Hofer I, Cannesson M, Baldi P. Lee CK, et al. NPJ Digit Med. 2021 Jan 8;4(1):8. doi: 10.1038/s41746-020-00377-1. NPJ Digit Med. 2021. PMID: 33420341 Free PMC article.

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