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Surgical Hot Spotting

Surgical Hot Spotting Machine Learning–Assessed Preoperative Risk Factors and Medicare Super-Utilization After Surgery Original Investigation Research Care. 2008;46(7):718-725. doi:10.1097/MLR. 32. Tan P-N, Steinbach M, Karpatne A, Kumar V. Am. 2012;94(9):794-800. doi:10.2106/JBJS.K. 0b013e3181653d6b Introduction to Data Mining. 2nd ed. New York, NY: 00072 Pearson Education Inc; 2019. 25. Deo RC. Machine learning in medicine. 42. Bozic KJ, Ong K, Lau E, et al. Estimating risk in Circulation. 2015;132(20):1920-1930. doi:10.1161/ 33. Stuart EA. Matching methods for causal Medicare patients with THA: an electronic risk CIRCULATIONAHA.115.001593 inference: a review and a look forward. Stat Sci. calculator for periprosthetic joint infection and 2010;25(1):1-21. doi:10.1214/09-STS313 mortality. Clin Orthop Relat Res. 2013;471(2):574-583. 26. Hastie T, Tibshirani R, Friedman JH. doi:10.1007/s11999-012-2605-z The Elements of Statistical Learning: Data Mining, 34. Stuart EA, DuGoff E, Abrams M, Salkever D, Inference, and Prediction. 2nd ed. New York, NY: Steinwachs D. Estimating causal effects in 43. Moghadamyeghaneh Z, Hanna MH, Hwang G, Springer; 2009. doi:10.1007/978-0-387-84858-7 observational studies using electronic health data: et al. Outcome of preoperative weight loss in challenges and (some) solutions. EGEMS (Wash DC). colorectal surgery. Am J Surg. 2015;210(2):291-297. 27. Corey KM, Kashyap S, Lorenzi E, et al. 2013;1(3). doi:10.1016/j.amjsurg.2015.01.019 Development and validation of machine learning models to identify high-risk http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA Surgery American Medical Association

Surgical Hot Spotting

Chhabra, Karan R.; Ghaferi, Amir A.; Nathan, Hari
JAMA Surgery , Volume 154 (11) – Nov 14, 2019
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References (4)

Publisher
American Medical Association
Copyright
Copyright 2019 American Medical Association. All Rights Reserved.
ISSN
2168-6254
eISSN
2168-6262
DOI
10.1001/jamasurg.2019.2999
Publisher site
See Article on Publisher Site

Abstract

Machine Learning–Assessed Preoperative Risk Factors and Medicare Super-Utilization After Surgery Original Investigation Research Care. 2008;46(7):718-725. doi:10.1097/MLR. 32. Tan P-N, Steinbach M, Karpatne A, Kumar V. Am. 2012;94(9):794-800. doi:10.2106/JBJS.K. 0b013e3181653d6b Introduction to Data Mining. 2nd ed. New York, NY: 00072 Pearson Education Inc; 2019. 25. Deo RC. Machine learning in medicine. 42. Bozic KJ, Ong K, Lau E, et al. Estimating risk in Circulation. 2015;132(20):1920-1930. doi:10.1161/ 33. Stuart EA. Matching methods for causal Medicare patients with THA: an electronic risk CIRCULATIONAHA.115.001593 inference: a review and a look forward. Stat Sci. calculator for periprosthetic joint infection and 2010;25(1):1-21. doi:10.1214/09-STS313 mortality. Clin Orthop Relat Res. 2013;471(2):574-583. 26. Hastie T, Tibshirani R, Friedman JH. doi:10.1007/s11999-012-2605-z The Elements of Statistical Learning: Data Mining, 34. Stuart EA, DuGoff E, Abrams M, Salkever D, Inference, and Prediction. 2nd ed. New York, NY: Steinwachs D. Estimating causal effects in 43. Moghadamyeghaneh Z, Hanna MH, Hwang G, Springer; 2009. doi:10.1007/978-0-387-84858-7 observational studies using electronic health data: et al. Outcome of preoperative weight loss in challenges and (some) solutions. EGEMS (Wash DC). colorectal surgery. Am J Surg. 2015;210(2):291-297. 27. Corey KM, Kashyap S, Lorenzi E, et al. 2013;1(3). doi:10.1016/j.amjsurg.2015.01.019 Development and validation of machine learning models to identify high-risk

Journal

JAMA Surgery – American Medical Association

Published: Nov 14, 2019

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