Fitting the data from embryo implantation prediction: Learning from label proportions

Hernández-González, Jerónimo; Inza, Iñaki; Crisol-Ortíz, Lorena; Guembe, María A; Iñarra, María J; Lozano, José A.

doi:10.1177/0962280216651098

Cited by 31 publications

(24 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in our meta-analysis, prediction models of ongoing pregnancy in in vitro fertilization had point estimates of AUROCs ranging from 0.575 to 0.982. These were developed using a support vector machine [110], artificial neural networks [132,136,170], random forests [134,158], deep neural networks [148,153], naïve Bayes algorithms [126,135,150], and LRs [73,78,99,158,170]. Compared with a recent systematic review focusing on prediction for in vitro fertilization [143,194], the range of AUROCs was wider than that of the previous review.…”

Section: Comparisons With Prior Workmentioning

confidence: 99%

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

et al. 2020

View full text Add to dashboard Cite

Background Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. Objective This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making. Methods Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians’ decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2. Results Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07). Conclusions Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106

show abstract

Section: Comparisons With Prior Workmentioning

confidence: 99%

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This method classifies embryos into a categorical scale (A,B,C,D) using morphological criteria. In recent years, machine learning techniques have been used to assist clinicians in embryo selection and pregnancy prediction [5,6,7,8]. Most of them rely on supervised classification, meaning that only the embryos whose outcome is known (all embryos in the cycles were implanted or none were) are used for training.…”

Section: Introductionmentioning

confidence: 99%

Validation on Real Data of an Extended Embryo-Uterine Probabilistic Graphical Model for Embryo Selection

Torres-Martín¹,

Hernández-González²,

Cerquides³

2021

Frontiers in Artificial Intelligence and Applications

Self Cite

View full text Add to dashboard Cite

Embryo selection is a critical step in assisted reproduction (ART): a good selection criteria is expected to increase the probability of inducing pregnancy. In the past, machine learning methods have been used to predict implantation and to rank the most promising embryos. Here, we study the use of a probabilistic graphical model that assumes independence between embryos’ individual features and cycles characteristics. It also accounts for a third source of uncertainty attributed to unknown factors. We present an empirical validation and analysis of the behavior of the model within real data. The dataset describes 604 consecutive ART cycles carried out at Hospital Donostia (Spain), where embryo selection was performed following the Spanish Association for Reproduction Biology Studies (ASEBIR) protocol, based on morphological features. The performance of our model is evaluated with different metrics and the predicted probability densities are examined to obtain significant insights about the process. Special attention is given to the relation between the model and the ASEBIR protocol. We validate our model by showing that its predictions show correlation with the ASEBIR score when the score is not provided as a feature. However, once the selection based on this protocol has taken place, our model is unable to separate implanted and failed embryos when only embryo individual features are used. From here, we can conclude that ASEBIR score provides a good summary of morphological features.

show abstract

“…Theoretical guarantees have also been provided [30,11]. These methods have been applied to real domains such as spam filtering [36], poll prediction [31,44], embryo selection [17], fraud detection [38], manufacturing [43], brain-computer interfaces [20], high energy physics [8], etc. In regression, on the contrary, this genuine aggregated response has only been described, so far, for estimating the amount of black carbon in aerosol particles [27].…”

Section: Introductionmentioning

confidence: 99%

Aggregated outputs by linear models: An application on marine litter beaching prediction

Hernández-González

Inza

Granado

et al. 2019

Information Sciences

Self Cite

View full text Add to dashboard Cite

Fitting the data from embryo implantation prediction: Learning from label proportions

Cited by 31 publications

References 47 publications

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Validation on Real Data of an Extended Embryo-Uterine Probabilistic Graphical Model for Embryo Selection

Aggregated outputs by linear models: An application on marine litter beaching prediction

Contact Info

Product

Resources

About