[Risk assessment in ovarian hyperstimulation syndrome (OHS) using the machine learning system (Decision Master) in 155 in-vitro fertilisations and embryo-transfer (IVF/ET) cycles with a long stimulation protocol].
In 155 selected IVF/ET cycles stimulated with the long protocol 25 cycles with severe OHS are included which turned up later on (purposely overrepresented). An inductive machine learning program is described both in informatics and medical essentials. It is tested whether there exists an algorithm for ruling out the above-mentioned complication in the follicular phase of the same cycle already. By cross validation 89% of the OHS could be predicted and proven by practical rules using hormone and ultrasound values to avoid similar events in ongoing or further cycles.
Haake
K W
KW
Abteilung für Humane Reproduktion und Endokrinologie, Universitätsfrauenklinik Leipzig.
List
P
P
Baier
D
D
Zimmermann
G
G
Pretzsch
G
G
Alexander
H
H
D000328:Adult; D003663:Decision Trees; D004624:Embryo Transfer / Q000009:adverse effects*; D005260:Female; D005307:Fertilization in Vitro / Q000009:adverse effects*; D006801:Humans; D008490:Medical Informatics; D016471:Ovarian Hyperstimulation Syndrome / Q000209:etiology*; D018570:Risk Assessment
Risikoerkennung des ovariellen Uberstimulationssyndroms (OHS) mit Hilfe des maschinellen Lernverfahrens "Decision Master" bei 155 In-Vitro-Fertilisation und Embryotransfer (IVF/ET)-Zyklen mit dem langen Stimulationsprotokoll.