Similar articles for PMID: 27232332

Year	Number of Results
2010	1
2012	1
2013	3
2014	5
2015	9
2016	9
2017	4
2018	24
2019	32
2020	12
2021	10
2022	7
2023	3
2024	0

1

Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications.

Thottakkara P, Ozrazgat-Baslanti T, Hupf BB, Rashidi P, Pardalos P, Momcilovic P, Bihorac A. Thottakkara P, et al. PLoS One. 2016 May 27;11(5):e0155705. doi: 10.1371/journal.pone.0155705. eCollection 2016. PLoS One. 2016. PMID: 27232332 Free PMC article.

2

[Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].

Tang CQ, Li JQ, Xu DY, Liu XB, Hou WJ, Lyu KY, Xiao SC, Xia ZF. Tang CQ, et al. Zhonghua Shao Shang Za Zhi. 2018 Jun 20;34(6):343-348. doi: 10.3760/cma.j.issn.1009-2587.2018.06.006. Zhonghua Shao Shang Za Zhi. 2018. PMID: 29961290 Chinese.

3

Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics.

Adhikari L, Ozrazgat-Baslanti T, Ruppert M, Madushani RWMA, Paliwal S, Hashemighouchani H, Zheng F, Tao M, Lopes JM, Li X, Rashidi P, Bihorac A. Adhikari L, et al. PLoS One. 2019 Apr 4;14(4):e0214904. doi: 10.1371/journal.pone.0214904. eCollection 2019. PLoS One. 2019. PMID: 30947282 Free PMC article.

4

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

Bunn C, Kulshrestha S, Boyda J, Balasubramanian N, Birch S, Karabayir I, Baker M, Luchette F, Modave F, Akbilgic O. Bunn C, et al. Surgery. 2021 Mar;169(3):671-677. doi: 10.1016/j.surg.2020.07.045. Epub 2020 Sep 18. Surgery. 2021. PMID: 32951903 Free PMC article.

5

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform.

Ren Y, Loftus TJ, Datta S, Ruppert MM, Guan Z, Miao S, Shickel B, Feng Z, Giordano C, Upchurch GR Jr, Rashidi P, Ozrazgat-Baslanti T, Bihorac A. Ren Y, et al. JAMA Netw Open. 2022 May 2;5(5):e2211973. doi: 10.1001/jamanetworkopen.2022.11973. JAMA Netw Open. 2022. PMID: 35576007 Free PMC article.

6

Using Machine Learning to Predict Acute Kidney Injury After Aortic Arch Surgery.

Lei G, Wang G, Zhang C, Chen Y, Yang X. Lei G, et al. J Cardiothorac Vasc Anesth. 2020 Dec;34(12):3321-3328. doi: 10.1053/j.jvca.2020.06.007. Epub 2020 Jun 10. J Cardiothorac Vasc Anesth. 2020. PMID: 32636105

7

Validation of clinical scores predicting severe acute kidney injury after cardiac surgery.

Englberger L, Suri RM, Li Z, Dearani JA, Park SJ, Sundt TM 3rd, Schaff HV. Englberger L, et al. Am J Kidney Dis. 2010 Oct;56(4):623-31. doi: 10.1053/j.ajkd.2010.04.017. Epub 2010 Jul 13. Am J Kidney Dis. 2010. PMID: 20630639

8

Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications.

Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Xue B, et al. JAMA Netw Open. 2021 Mar 1;4(3):e212240. doi: 10.1001/jamanetworkopen.2021.2240. JAMA Netw Open. 2021. PMID: 33783520 Free PMC article.

9

Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma.

Lee Y, Ryu J, Kang MW, Seo KH, Kim J, Suh J, Kim YC, Kim DK, Oh KH, Joo KW, Kim YS, Jeong CW, Lee SC, Kwak C, Kim S, Han SS. Lee Y, et al. Sci Rep. 2021 Aug 3;11(1):15704. doi: 10.1038/s41598-021-95019-1. Sci Rep. 2021. PMID: 34344909 Free PMC article.

10

Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.

Corey KM, Kashyap S, Lorenzi E, Lagoo-Deenadayalan SA, Heller K, Whalen K, Balu S, Heflin MT, McDonald SR, Swaminathan M, Sendak M. Corey KM, et al. PLoS Med. 2018 Nov 27;15(11):e1002701. doi: 10.1371/journal.pmed.1002701. eCollection 2018 Nov. PLoS Med. 2018. PMID: 30481172 Free PMC article.

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