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For: Bolourani S, Tayebi MA, Diao L, Wang P, Patel V, Manetta F, Lee PC. Using machine learning to predict early readmission following esophagectomy. The Journal of Thoracic and Cardiovascular Surgery 2021;161:1926-1939.e8. [DOI: 10.1016/j.jtcvs.2020.04.172] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Article Influence: 11.0] [Reference Citation Analysis]
Number Citing Articles
1 Ishwaran H, O'Brien R. REPLY: THE STANDARDIZATION AND AUTOMATION OF MACHINE LEARNING FOR BIOMEDICAL DATA. J Thorac Cardiovasc Surg 2020:S0022-5223(20)32370-9. [PMID: 32868054 DOI: 10.1016/j.jtcvs.2020.07.113] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Seastedt KP, Moukheiber D, Mahindre SA, Thammineni C, Rosen DT, Watkins AA, Hashimoto DA, Hoang CD, Kpodonu J, Celi LA. A scoping review of artificial intelligence applications in thoracic surgery. Eur J Cardiothorac Surg 2021:ezab422. [PMID: 34601587 DOI: 10.1093/ejcts/ezab422] [Reference Citation Analysis]
3 Ishwaran H, O'Brien R. Commentary: The problem of class imbalance in biomedical data. J Thorac Cardiovasc Surg 2021;161:1940-1. [PMID: 32711988 DOI: 10.1016/j.jtcvs.2020.06.052] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
4 Bolourani S, Zanos TP, Wang P, Tayebi MA, Lee PC. Reply: In machine learning, the devil is in the details. J Thorac Cardiovasc Surg 2020:S0022-5223(20)32869-5. [PMID: 33208260 DOI: 10.1016/j.jtcvs.2020.10.052] [Reference Citation Analysis]
5 Qian C, Leelaprachakul P, Landers M, Low C, Dey AK, Doryab A. Prediction of Hospital Readmission from Longitudinal Mobile Data Streams. Sensors (Basel) 2021;21:7510. [PMID: 34833586 DOI: 10.3390/s21227510] [Reference Citation Analysis]
6 Jiang Z, Cai Y, Zhang X, Lv Y, Zhang M, Li S, Lin G, Bao Z, Liu S, Gu W. Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning. Front Aging Neurosci 2021;13:715517. [PMID: 34867266 DOI: 10.3389/fnagi.2021.715517] [Reference Citation Analysis]
7 Rossi LA, Melstrom LG, Fong Y, Sun V. Predicting post-discharge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data. J Surg Oncol 2021;123:1345-52. [PMID: 33621378 DOI: 10.1002/jso.26413] [Reference Citation Analysis]
8 Zhang X, Lv B, Rui L, Cai L, Liu F. Regression Analysis of Factors Based on Cluster Analysis of Acute Radiation Pneumonia due to Radiation Therapy for Lung Cancer. J Healthc Eng 2021;2021:3727794. [PMID: 34691377 DOI: 10.1155/2021/3727794] [Reference Citation Analysis]
9 Okusanya O, Sultan I. Commentary: How to catch a boomerang: Learning from hospital readmissions after thoracic surgery. J Thorac Cardiovasc Surg 2021;161:1945-6. [PMID: 32680638 DOI: 10.1016/j.jtcvs.2020.05.111] [Reference Citation Analysis]
10 Klein S, Duda DG. Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas. Cancers (Basel) 2021;13:4919. [PMID: 34638408 DOI: 10.3390/cancers13194919] [Reference Citation Analysis]
11 Murthy SC, Blackstone EH. Commentary: We prefer wisdom over knowledge. J Thorac Cardiovasc Surg 2021;161:1942-3. [PMID: 32771231 DOI: 10.1016/j.jtcvs.2020.06.057] [Reference Citation Analysis]
12 Altorki N, Sedrakyan A. Commentary: Can machine learning reduce readmissions after esophagectomy? A consummation devoutly to be wished. J Thorac Cardiovasc Surg 2021;161:1944-5. [PMID: 32711979 DOI: 10.1016/j.jtcvs.2020.05.054] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]