A Bayesian network for complex pain-related features of endometriosis

Kiser AC, Schliep KC, Yandell M, Eilbeck K

doi:10.1016/j.fertnstert.2022.08.629

A Bayesian network for complex pain-related features of endometriosis

K Eilbeck, A C Kiser, Karen C Schliep, Mark Yandell

University of Utah ROR

Fertility and Sterility, 118(4), e222

DOI 10.1016/j.fertnstert.2022.08.629

Abstract

No abstract available for this article.

Topics

Endometriosis > Diagnosis > Computational Modeling Endometriosis > Symptoms > Pain Classification Diagnostics > Statistical Methods > Bayesian Networks

endometriosis pain Bayesian network analysis, complex pain features endometriosis computational modeling, Bayesian network endometriosis symptom classification, endometriosis pelvic pain machine learning prediction, endometriosis pain phenotyping probabilistic model, Kiser Schliep endometriosis pain Bayesian, endometriosis symptom associations network analysis, chronic pelvic pain endometriosis diagnostic modeling, endometriosis clinical features statistical modeling fertility sterility

DOI 10.1016/j.fertnstert.2022.08.629 10.1016/j.fertnstert.2022.08.629

Cite this article

Kiser, A. C., Schliep, K. C., Yandell, M., & Eilbeck, K. (2022). A Bayesian network for complex pain-related features of endometriosis. *Fertility and Sterility*, *118*(4), e222. https://doi.org/10.1016/j.fertnstert.2022.08.629

A Bayesian network for complex pain-related features of endometriosis

Abstract

Topics

Cite this article

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