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Pregnancy

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Machine learning model-based preterm birth prediction and clinical nomogram: A big retrospective cohort study.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: This study sought to develop a multifactorial predictive model for preterm birth risk, with the goal of providing clinical practitioners with early prevention.

Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields.

Scientific reports
This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy se...

Application of artificial intelligence in VSD prenatal diagnosis from fetal heart ultrasound images.

BMC pregnancy and childbirth
BACKGROUND: Developing a combined artificial intelligence (AI) and ultrasound imaging to provide an accurate, objective, and efficient adjunctive diagnostic approach for fetal heart ventricular septal defects (VSD).

IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.

Reproduction (Cambridge, England)
IN BRIEF: Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of...

A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.

Scientific reports
Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer o...

Prediction of clinical pregnancy outcome after single fresh blastocyst transfer during in vitro fertilization: an ensemble learning perspective.

Human fertility (Cambridge, England)
To establish a predictive model for clinical pregnancy outcomes following the transfer of a single fresh blastocyst in vitro fertilization (IVF). 615 patients (492 in training set and 123 in test set) who underwent the first single and fresh blastocy...

Building a machine learning-based risk prediction model for second-trimester miscarriage.

BMC pregnancy and childbirth
BACKGROUND: Second-trimester miscarriage is a common adverse pregnancy outcome that imposes substantial economic and psychological pressures on both the physical and mental well-being of patients and their families. Currently, there is a scarcity of ...

Predictive modeling of gestational weight gain: a machine learning multiclass classification study.

BMC pregnancy and childbirth
BACKGROUND: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, ...

Healthy nutrition and weight management for a positive pregnancy experience in the antenatal period: Comparison of responses from artificial intelligence models on nutrition during pregnancy.

International journal of medical informatics
BACKGROUND: As artificial intelligence AI-supported applications become integral to web-based information-seeking, assessing their impact on healthy nutrition and weight management during the antenatal period is crucial.

A multimodal deep learning-based algorithm for specific fetal heart rate events detection.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance automatic monitoring and intelligent assessment of fetal well-being.