Applying machine learning methods to psychosocial screening data to improve identification of prenatal depression: Implications for clinical practice and research
We utilized machine learning (ML) methods on data from the PROMOTE, a novel
psychosocial screening tool, to quantify risk for prenatal depression for individual patients …
psychosocial screening tool, to quantify risk for prenatal depression for individual patients …
Using the PROMOTE screener to identify psychosocial risk factors for prenatal substance use
A Azeem, M Lobel, C Heiselman… - Journal of Addiction …, 2024 - journals.lww.com
Methods We conducted a retrospective chart review of 1842 patients who completed the
PROMOTE screening instrument during their first prenatal visit to outpatient clinics of a New …
PROMOTE screening instrument during their first prenatal visit to outpatient clinics of a New …
Learning from Heterogeneous Data with Deep Gaussian Processes
Deep Gaussian processes (DGPs) are deep models represented by layers of Gaussian
processes (GPs). They are flexible Bayesian models capable of capturing highly nonlinear …
processes (GPs). They are flexible Bayesian models capable of capturing highly nonlinear …
[HTML][HTML] Saving time, signaling trust: Using the PROMOTE self-report screening instrument to enhance prenatal care quality and therapeutic relationships
Objectives Comprehensive screening of psychosocial vulnerabilities and substance use in
prenatal care is critical to promote the health and well-being of pregnant patients. Effective …
prenatal care is critical to promote the health and well-being of pregnant patients. Effective …
Uncertainty Quantification of Deep Generative Models Based on Gaussian Processes for Heterogeneous Incomplete Data
M Ajirak - 2023 - search.proquest.com
This dissertation focuses on the theoretical aspects of machine learning based on Gaussian
processes (GPs), aiming to tackle fundamental challenges related to the trustworthiness of …
processes (GPs), aiming to tackle fundamental challenges related to the trustworthiness of …