Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models-Reference-Cited by-同舟云学术

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Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models

Published:2021-11-17 Issue: Volume: Page:
ISSN:1752-8054
Container-title:Clinical and Translational Science
language:en
Short-container-title:Clin Transl Sci

Author:

Kim Bomee¹,Jang Yun Ji²,Cho Hae Ram²,Kim So Yeon²,Jeong Ji Eun²,Shim Mi Kyoung²,Kim Myeong Gyu³⁴

Affiliation:

1. Graduate School of Clinical Biohealth Ewha Womans University Seoul Korea

2. College of Pharmacy CHA University Pocheon Korea

3. College of Pharmacy Ewha Womans University Seoul Korea

4. Graduate School of Pharmaceutical Sciences Ewha Womans University Seoul Korea

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

General Pharmacology, Toxicology and Pharmaceutics,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/cts.13187

Reference28 articles.

1. The second wave: Toward responsible inclusion of pregnant women in research

2. Medication use in pregnancy: a cross-sectional, multinational web-based study

3. Addressing Barriers to Inclusion of Pregnant Women in Clinical Trials

4. Challenges in Designing Clinical Trials to Test New Drugs in the Pregnant Woman and Fetus

5. Exclusion of Pregnant Women From Industry-Sponsored Clinical Trials

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1. A clinical trial termination prediction model based on denoising autoencoder and deep survival regression;Quantitative Biology;2024-04-12

2. Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis;Frontiers in Oncology;2022-08-22

3. Regularizing the Deepsurv Network Using Projection Loss for Medical Risk Assessment;IEEE Access;2022

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