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Predicting patient-reported symptom clusters in prostate cancer patients : a machine learning approach

(2022) EUROPEAN UROLOGY. In European Urology 81(Supplement 1). p.S1687-S1688
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Urology

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MLA
Rammant, Elke, et al. “Predicting Patient-Reported Symptom Clusters in Prostate Cancer Patients : A Machine Learning Approach.” EUROPEAN UROLOGY, vol. 81, no. Supplement 1, Elsevier BV, 2022, pp. S1687–88, doi:10.1016/s0302-2838(22)01229-5.
APA
Rammant, E., Deman, E., Poppe, L., Bultijnck, R., Dirix, P., De Meerleer, G., … Van Hoecke, S. (2022). Predicting patient-reported symptom clusters in prostate cancer patients : a machine learning approach. EUROPEAN UROLOGY, 81(Supplement 1), S1687–S1688. https://doi.org/10.1016/s0302-2838(22)01229-5
Chicago author-date
Rammant, Elke, Emile Deman, Lindsay Poppe, Renée Bultijnck, P. Dirix, G. De Meerleer, K. Haustermans, et al. 2022. “Predicting Patient-Reported Symptom Clusters in Prostate Cancer Patients : A Machine Learning Approach.” In EUROPEAN UROLOGY, 81:S1687–88. Elsevier BV. https://doi.org/10.1016/s0302-2838(22)01229-5.
Chicago author-date (all authors)
Rammant, Elke, Emile Deman, Lindsay Poppe, Renée Bultijnck, P. Dirix, G. De Meerleer, K. Haustermans, Ann Van Hecke, D. Azria, J. Chang-Claude, A. Choudhury, D. De Ruysscher, M. Lambrecht, B.S. Rosenstein, P. Seibold, E. Sperk, R.P. Symonds, R. Valdagni, A. Vega, A. Webb, C. West, Liv Veldeman, Valerie Fonteyne, and Sofie Van Hoecke. 2022. “Predicting Patient-Reported Symptom Clusters in Prostate Cancer Patients : A Machine Learning Approach.” In EUROPEAN UROLOGY, 81:S1687–S1688. Elsevier BV. doi:10.1016/s0302-2838(22)01229-5.
Vancouver
1.
Rammant E, Deman E, Poppe L, Bultijnck R, Dirix P, De Meerleer G, et al. Predicting patient-reported symptom clusters in prostate cancer patients : a machine learning approach. In: EUROPEAN UROLOGY. Elsevier BV; 2022. p. S1687–8.
IEEE
[1]
E. Rammant et al., “Predicting patient-reported symptom clusters in prostate cancer patients : a machine learning approach,” in EUROPEAN UROLOGY, Amsterdam, the Netherlands, 2022, vol. 81, no. Supplement 1, pp. S1687–S1688.
@inproceedings{01GSCHKZYVXR6P3NYEHYDDV7FR,
  articleno    = {{A1153}},
  author       = {{Rammant, Elke and Deman, Emile and Poppe, Lindsay and Bultijnck, Renée and Dirix, P. and De Meerleer, G. and Haustermans, K. and Van Hecke, Ann and Azria, D. and Chang-Claude, J. and Choudhury, A. and De Ruysscher, D. and Lambrecht, M. and Rosenstein, B.S. and Seibold, P. and Sperk, E. and Symonds, R.P. and Valdagni, R. and Vega, A. and Webb, A. and West, C. and Veldeman, Liv and Fonteyne, Valerie and Van Hoecke, Sofie}},
  booktitle    = {{EUROPEAN UROLOGY}},
  issn         = {{0302-2838}},
  keywords     = {{Urology}},
  language     = {{eng}},
  location     = {{Amsterdam, the Netherlands}},
  number       = {{Supplement 1}},
  pages        = {{A1153:S1687--A1153:S1688}},
  publisher    = {{Elsevier BV}},
  title        = {{Predicting patient-reported symptom clusters in prostate cancer patients : a machine learning approach}},
  url          = {{http://doi.org/10.1016/s0302-2838(22)01229-5}},
  volume       = {{81}},
  year         = {{2022}},
}

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