A Review of Machine Learning Approaches in Assisted Reproductive Technologies

Raef, Behnaz; Ferdousi, Reza

doi:10.5455/aim.2019.27.205-211

Cited by 44 publications

(30 citation statements)

References 34 publications

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“…They learn from previous observations and experiences to gradually achieve the desired performance in specific tasks. Various ML techniques used at different phases of IVF treatment and their performance are reported in the recent review [25].…”

Section: B Machine Learningmentioning

confidence: 99%

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A SWOT Analysis of Human- and Machine Learning- Based Embryo Assessment

Tran

Dang

et al. 2020

IEEE Access

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Section: B Machine Learningmentioning

confidence: 99%

“…Another study utilized a DL model for automated embryo quality evaluation by analyzing timelapse images [85]. The impact of AI in IVF and embryo selection are viewed in different papers [3,86,14,25].…”

Section: Opportunity Analysis For Ml-based Embryo Selectionmentioning

confidence: 99%

A SWOT Analysis of Human- and Machine Learning- Based Embryo Assessment

Tran

Dang

et al. 2020

IEEE Access

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“…[11][12][13][14][15] Unlike the experimental methods, the computational approaches show high degrees of flexibility and result in high-throughput outcomes, providing an opportunity to study and analyze clusters of old and new data to discover novel knowledge. [16][17][18][19] These mathematical models can disclose the hidden conditions in which the interactions occur. As a result, new approaches need to be implemented for the prediction of DDIs with high precision in order to provide key information for the promotion of the health by preventing any side effects of administered multi-drug therapy and hence adverse reactions.…”

Section: Data Acquisition and Preparationmentioning

confidence: 99%

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

2019

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Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45 000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2 000 000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200 000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.

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“…19,26 Moreover, the lack of categorization and ordering of the features in the previous studies led to difficult interpretation and comparison of the results. 27 In a recent publication, 28 we introduced 20 prediction models on ART. The prediction target of all these models is pregnancy (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In a recent publication, 28 we introduced 20 prediction models on ART. The prediction target of all these models is pregnancy (i.e.…”

Section: Introductionmentioning

confidence: 99%

Computational prediction of implantation outcome after embryo transfer

Raef

Maleki

Ferdousi

2019

Health Informatics J

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The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle.

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A Review of Machine Learning Approaches in Assisted Reproductive Technologies

Cited by 44 publications

References 34 publications

A SWOT Analysis of Human- and Machine Learning- Based Embryo Assessment

A SWOT Analysis of Human- and Machine Learning- Based Embryo Assessment

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

Computational prediction of implantation outcome after embryo transfer

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