Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms
Seon Ui Lee, Sae Kyung Choi, Yun Sung Jo, Jeong Ha Wie, Jae Eun Shin, Yeon Hee Kim, Kicheol Kil, Hyun Sun Ko, Tao Huang · 2024
Read Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms by Seon Ui Lee, Sae Kyung Choi, Yun Sung Jo, Jeong Ha Wie, Jae Eun Shin, Yeon Hee Kim, Kicheol Kil, Hyun Sun Ko, Tao Huang (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11595523.txt.
Description
The authors present a 2024 study building a machine learning model that forecasts late fetal growth restriction. When a fetus stops growing well late in pregnancy, risks climb sharply, and early warning lets doctors watch the right mothers. Data came from seven hospitals over eleven years, covering more than thirty-two thousand pregnancies.
Two models were built, one from first-trimester data and one adding information up to twenty-eight weeks. The later model discriminated better in both training and test sets. A simplified version, using only the features an importance ranking picked, performed about as well. Prediction was modest rather than sharp, with precision-recall scores staying low. Still, the authors say a lean model could help flag individual risk by the early third trimester.
Catalog record
| CREATOR | Seon Ui Lee, Sae Kyung Choi, Yun Sung Jo, Jeong Ha Wie, Jae Eun Shin, Yeon Hee Kim, Kicheol Kil, Hyun Sun Ko, Tao Huang |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 29 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |