首页|Study Findings from University of Pittsburgh School of Public Health Broaden Und erstanding of Preeclampsia (Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning)
Study Findings from University of Pittsburgh School of Public Health Broaden Und erstanding of Preeclampsia (Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on preeclampsia have been presented . According to news reporting out of Atlanta, Georgia, by NewsRx editors, resear ch stated, “The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator. We used data from 10,038 pregna nt women and a 10% subsample (N = 1004) to examine the extent to w hich the risk difference for the relation between fruit and vegetable consumptio n and preeclampsia risk changes under different seed values.”
University of Pittsburgh School of Publi c HealthAtlantaGeorgiaUnited StatesNorth and Central AmericaAlgorithmsCyborgsEmerging TechnologiesEpidemiologyMachine LearningPreeclampsiaPregnancy ComplicationsRisk and PreventionWomen’s Health
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