Open AccessTowards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes
Author(s) -
Lena Davidson,
Mary Regina Boland
Publication year - 2021
Publication title -
briefings in bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.204
H-Index - 113
eISSN - 1477-4054
pISSN - 1467-5463
DOI - 10.1093/bib/bbaa369
Subject(s) - artificial intelligence , pregnancy , machine learning , computer science , random forest , health informatics , medicine , support vector machine , public health , nursing , genetics , biology
Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes.