Postpartum Haemorrhage Risk Prediction Model Developed by Machine Learning Algorithms: A Single-Centre Retrospective Analysis of Clinical Data
Background: Postpartum haemorrhage (PPH) is a serious complication and a cause of maternal mortality after delivery. This study used machine learning algorithms and new feature selection methods to build an efficient PPH risk prediction model and provided new ideas and reference methods for PPH risk...
Saved in:
Main Authors: | Wenhuan Wang (Author), Chanchan Liao (Author), Hongping Zhang (Author), Yanjun Hu (Author) |
---|---|
Format: | Book |
Published: |
IMR Press,
2024-03-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Risk factors for postpartum haemorrhage in women with histologically verified placenta accreta spectrum disorders: a retrospective single-centre cross-sectional study
by: Naghmeh Ghaem Maghami, et al.
Published: (2023) -
Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study
by: Carlo Ricciardi, et al.
Published: (2024) -
Machine learning opportunities to predict obstetric haemorrhages
by: Yu. S. Boldina, et al.
Published: (2024) -
Prenatal anaemia and risk of postpartum haemorrhage: a cohort analysis of data from the Predict-PPH study
by: Kehinde S. Okunade, et al.
Published: (2024) -
Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population
by: Santosh Yogendra Shah, et al.
Published: (2023)