Diagnostic prénatal et diagnostic pré-implantatoire : arbre décisionnel, nouvelles pratiques ?

Steffann, Julie; Feyereisen, E.; Kerbrat, Violaine; Romana, Serge; Frydman, Nelly

doi:10.1051/medsci/20052111987

Cited by 5 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An alternative strategy for developing risk estimation models is to use machine learning–based methods such as tree-based models, which do not require assumptions regarding the variables and their relationships with the outcome. Therefore, these techniques can incorporate complex relationships in a completely data-driven manner, including nonlinearity or interactions that may be hard to detect by regression-based models [ 11 – 15 ]. There is thus the potential to improve AKI risk prediction by including machine learning methods as part of the model development strategy.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

et al. 2018

View full text Add to dashboard Cite

BackgroundThe current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI.Methods and findingsWe used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749–0.754) versus 0.711 (95% CI 0.708–0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615–0.0618) versus 0.0636 (95% CI 0.0634–0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988–1.028) versus 1.036 (95% CI 1.015–1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015–March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors.Conclusio...

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Scientific techniques are breaking down and redefining the concepts of parenthood, paternity and maternity; which is also an anthropological issue. States are called upon to respond positively to new desires or fantasies, as underlined by the Director of the European Centre for Law and Justice (Steffann et al, 2005): the desire to have a child with a dead person; the mother's desire to be the "father" of the child; the request for a presumption of maternity for the "mother's wife"; the desire to be the "mothers" of the same child... Jacques Cohen, head of an American laboratory, and one of the pioneers in human procreation, joyfully spoke of "the wonderful future that embryo sorting will allow" (Cohen, 1988).…”

Section: Biotechnology: the Other Side Of The Coinmentioning

confidence: 99%

Would the Convergence of Nanotechnology, Biotechnology, Information Technology and Cognitive Science Be a Springboard for Transhumanism and Posthumanism?

Sawadogo,

Simpore

2023

OJPP

View full text Add to dashboard Cite

Nanotechnologies, biotechnologies, information technologies and cognitive sciences (NBIC) have gradually gained traction in the United States of America (USA), subsequently expanding to Europe, and are now proliferating worldwide. Scientists are trying with more success to remove the causes of death by "repairing" humans, or even by "increasing" their physical and cognitive capacities. NBICs not only can help researchers promote "one health" by improving environmental conditions, human and animal health, but also, they can lead humanity towards transhumanism through eugenics. Thanks to the principle of totality, the intentional modification of the human body for therapeutic purposes through surgery has always been seen as a source of medical progress. But how far can the living human body be modified at will? Gilbert Hottois and Jean-François Mattei have deciphered transhumanism to question its alleged "humanism" and study its impact on our humanity. Today, science has gone further thanks to the possibilities offered by converging NBIC technologies and especially with the advent of human genome editing!

show abstract

“…Avec le dévelop-pement des techniques de procréation médicalement assistée et la possibilité de diagnostiquer sur l'embryon ou le foetus toutes sortes de maladies, on parle d'un « retour de l'eugénisme » [2] ou encore d'un « nouvel eugénisme » [3]. L'article 16-4 du Code civil aborde directement la question en interdisant les pratiques eugéniques tendant à la sélection des personnes [4], mais d'autres dispositions de la loi 94-654 autorisent, en les encadrant, certaines pratiques, comme le diagnostic préimplantatoire [5,21], qui pourraient être qualifiées d'eugéniques. Le terme « eugénisme » provient étymologiquement de eu signifiant bien ou bon et de genos signifiant race ou naissance.…”

unclassified

Sir Francis Galton : le fondateur de l’eugénisme

Aubert-Marson

2009

Med Sci (Paris)

View full text Add to dashboard Cite

Diagnostic prénatal et diagnostic pré-implantatoire : arbre décisionnel, nouvelles pratiques ?

Cited by 5 publications

References 22 publications

Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

Would the Convergence of Nanotechnology, Biotechnology, Information Technology and Cognitive Science Be a Springboard for Transhumanism and Posthumanism?

Sir Francis Galton : le fondateur de l’eugénisme

Contact Info

Product

Resources

About