A Bayesian network model for predicting pregnancy after in vitro fertilization

Predictive model for live birth outcomes in single euploid frozen embryo transfers: a comparative analysis of logistic regression and machine learning approaches

PURPOSE

To develop and validate a predictive model for live birth (LB) outcomes in single euploid frozen embryo transfers (seFET) based on patient's characteristics and embryo parameters.

METHODS

A retrospective cohort study was performed including 1979 seFET performed between March 2017 and December 2023. Prediction models were built using logistic regression (LR), random forest classifier (RFC), support vector machines (SVM), and a gradient booster (XGBoost). Considered variables associated with LB outcomes were blastocyst expansion, blastocyst inner cell mass (ICM) and TE quality, day (D) of TE biopsy (D5, D6, and D7), female age and body mass index (BMI), distance from the uterine fundus at embryo transfer, endometrial preparation as natural cycles (NC) or hormonal replacement therapy (HRT), and endometrial thickness. Model performance was evaluated using area under the precision-recall curve and calibration metrics.

RESULTS

Variables that were negatively associated with LB rate were BMI (OR = 0.79 [0.64-0.96], P = 0.020 for overweight and OR = 0.76 [0.60-0.95], P = 0.015 for obese class I/II), ICM grade B (OR = 0.72 [0.57-0.90], P = 0.005) or C (OR = 0.21 [0.15-0.30], P < 0.001), TE grade C (OR = 0.32 [0.24-0.43], P < 0.001), and blastocyst biopsied on D6 (OR = 0.66 [0.55-0.80], P < 0.001 or D7 (OR = 0.19[0.09-0.37], P < 0.001). The LR model was the best in terms of overall classification performance (C-statistics: 0.626 ± 0.018 vs. 0.606 ± 0.018, 0.581 ± 0.018, 0.601 ± 0.017, LR vs. RFC, XGBoost, and SVM, respectively, P < 0.001). A prediction model of LB outcome was developed and is free to access: https://artfertilityclinics.shinyapps.io/ABLE/ .

CONCLUSION

LR demonstrated a stable validation performance and superior LB prediction, aiding as a predictive tool for patient counselling and assessing success in seFET cycles.

Using feature optimization and LightGBM algorithm to predict the clinical pregnancy outcomes after in vitro fertilization

Background

According to a recent report by the WHO, approximately 17.5\% (about one-sixth) of the global adult population is affected by infertility. Consequently, researchers worldwide have proposed various machine learning models to improve the prediction of clinical pregnancy outcomes during IVF cycles. The objective of this study is to develop a machine learning(ML) model that predicts the outcomes of pregnancies following in vitro fertilization (IVF) and assists in clinical treatment.

Methods

This study conducted a retrospective analysis on provincial reproductive centers in China from March 2020 to March 2021, utilizing 13 selected features. The algorithms used included XGBoost, LightGBM, KNN, Naïve Bayes, Random Forest, and Decision Tree. The results were evaluated using performance metrics such as precision, recall, F1-score, accuracy and AUC, employing five-fold cross-validation repeated five times.

Results

Among the models, LightGBM achieved the best performance, with an accuracy of 92.31%, recall of 87.80%, F1-score of 90.00\%, and an AUC of 90.41%. The model identified the estrogen concentration at the HCG injection(etwo), endometrium thickness (mm) on HCG day(EM TNK), years of infertility(Years), and body mass index(BMI) as the most important features.

Conclusion

This study successfully demonstrates the LightGBM model has the best predictive effect on pregnancy outcomes during IVF cycles. Additionally, etwo was found to be the most significant predictor for successful IVF compared to other variables. This machine learning approach has the potential to assist fertility specialists in providing counseling and adjusting treatment strategies for patients.

New frontiers in embryo selection

Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.

Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models

Embryo selection is a critical step in assisted reproduction: good selection criteria are expected to increase the probability of inducing a pregnancy. Machine learning techniques have been applied for implantation prediction or embryo quality assessment, which embryologists can use to make a decision about embryo selection. However, this is a highly uncertain real-world problem, and current proposals do not model always all the sources of uncertainty. We present a novel probabilistic graphical model that accounts for three different sources of uncertainty, the standard embryo and cycle viability, and a third one that represents any unknown factor that can drive a treatment to a failure in otherwise perfect conditions. We derive a parametric learning method based on the Expectation-Maximization strategy, which accounts for uncertainty issues. We empirically analyze the model within a real database consisting of 604 cycles (3125 embryos) carried out at Hospital Donostia (Spain). Embryologists followed the protocol of the Spanish Association for Reproduction Biology Studies (ASEBIR), based on morphological features, for embryo selection. Our model predictions are correlated with the ASEBIR protocol, which validates our model. The benefits of accounting for the different sources of uncertainty and the importance of the cycle characteristics are shown. Considering only transferred embryos, our model does not further discriminate them as implanted or failed, suggesting that the ASEBIR protocol could be understood as a thorough summary of the available morphological features.

Pseudo contrastive labeling for predicting IVF embryo developmental potential

In vitro fertilization is typically associated with high failure rates per transfer, leading to an acute need for the identification of embryos with high developmental potential. Current methods are tailored to specific times after fertilization, often require expert inspection, and have low predictive power. Automatic methods are challenged by ambiguous labels, clinical heterogeneity, and the inability to utilize multiple developmental points. In this work, we propose a novel method that trains a classifier conditioned on the time since fertilization. This classifier is then integrated over time and its output is used to assign soft labels to pairs of samples. The classifier obtained by training on these soft labels presents a significant improvement in accuracy, even as early as 30 h post-fertilization. By integrating the classification scores, the predictive power is further improved. Our results are superior to previously reported methods, including the commercial KIDScore-D3 system, and a group of eight senior professionals, in classifying multiple groups of favorable embryos into groups defined as less favorable based on implantation outcomes, expert decisions based on developmental trajectories, and/or genetic tests.

Benchmarking prognosis methods for survivability - A case study for patients with contingent primary cancers

BACKGROUND

There is an increasing number of patients with a first primary cancer who are diagnosed with a second primary cancer, but prognosis methods to predict the survivability of a patient with multiple primary cancers have not been fully benchmarked.

METHODS

This study investigated the five-year survivability prognosis performances of six machine learning approaches. These approaches are: artificial neural network, decision tree (DT), logistic regression, support vector machine, naïve Bayes (NB), and Bayesian network (BN). A synthetic minority over-sampling technique (SMOTE) was used to solve the imbalanced problem, and a nationwide cancer patient database containing 7,845 subjects in Taiwan was used as a sample source. Ten primary and secondary cancers and their key variables affecting the survivability of the patients were identified.

RESULTS

All the models using SMOTE improved sensitivity and specificity significantly. NB has the highest performance in terms of accuracy and specificity, whereas BN has the highest performance in terms of sensitivity. Further, the computational time and the power of knowledge representation of NB, BN, and DT outperformed the others.

CONCLUSIONS

Selecting the appropriate prognosis models to predict survivability of patients with two contingent primary cancers can aid precise prediction and can support appropriate treatment advice.

Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

BACKGROUND

To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing.

METHODS

This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.

RESULTS

For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree.

CONCLUSION

Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020)

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review

In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that possess unique methodical features to model dependencies in complex networks, such as forward and backward propagation (inference) of disruptions. BNs have transitioned from an emerging topic to a growing research area in supply chain (SC) resilience and risk analysis. As a result, there is an acute need to review existing literature to ascertain recent developments and uncover future areas of research. Despite the increasing number of publications on BNs in the domain of SC uncertainty, an extensive review on their application to SC risk and resilience is lacking. To address this gap, we analyzed research articles published in peer-reviewed academic journals from 2007 to 2019 using network analysis, visualization-based scientometric analysis, and clustering analysis. Through this study, we contribute to literature by discussing the challenges of current research, and, more importantly, identifying and proposing future research directions. The results of our survey show that further debate on the theory and application of BNs to SC resilience and risk management is a significant area of interest for both academics and practitioners. The applications of BNs, and their conjunction with machine learning algorithms to solve big data SC problems relating to uncertainty and risk, are also discussed.

Survivability modelling using Bayesian network for patients with first and secondary primary cancers

BACKGROUND AND OBJECTIVE

Multiple primary cancers significantly threat patient survivability. Predicting the survivability of patients with two cancers is challenging because its stochastic pattern relates with numerous variables.

METHODS

In this study, a Bayesian network (BN) model was proposed to describe the occurrence of two primary cancers and predict the five-year survivability of patients using probabilistic evidence. Eleven types of major primary cancers and contingent occurrences of secondary cancers were investigated. A nationwide two-cancer database involving 7,845 patients in Taiwan was investigated. The BN topology is rigorously examined and imbalanced dataset is processed by the synthetic minority oversampling technique. The proposed BN survivability prognosis model was compared with benchmark approaches.

RESULTS

The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity, which is a critical performance index for the non-survival group.

CONCLUSIONS

Using the proposed BN model, one can estimate the posterior probabilities for every query provided appropriate prior evidences. The potential survivability information of patients, treatment effects, and socio-demographics factor effects predicted by the proposed model can help in cancer treatment assessment and cancer development monitoring.

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse monitoring of the embryonic morphokinetics, and PGS are innovative techniques that increased the success of the ART. In the same trend, the use of artificial intelligence (AI) techniques is being intensively researched whether in the embryo or spermatozoa selection. Despite several studies already published, the use of AI within assisted reproduction clinics is not yet a reality. This is largely due to the different AI techniques that are being proposed to be used in the daily routine of the clinics, which causes some uncertainty in their use. To shed light on this complex scenario, this review briefly describes some of the most frequently used AI algorithms, their functionalities, and their potential use. Several databases were analyzed in search of articles where applied artificial intelligence algorithms were used on reproductive data. Our focus was on the classification of embryonic cells and semen samples. Of a total of 124 articles analyzed, 32 were selected for this review. From the proposed algorithms, most have achieved a satisfactory precision, demonstrating the potential of a wide range of AI techniques. However, the evaluation of these studies suggests the need for more standardized research to validate the proposed models and their algorithms. Routine use of AI in assisted reproduction clinics is just a matter of time. However, the choice of AI technique to be used is supported by a better understanding of the principles subjacent to each technique, that is, its robustness, pros, and cons. We provide some current (although incipient) and potential uses of AI on the clinic routine, discussing how accurate and friendly it could be. Finally, we propose some standards for AI research on the selection of the embryo to be transferred and other future hints. For us, the imminence of its use is evident, providing a revolutionary milestone that will impact the ART.

Computational prediction of implantation outcome after embryo transfer

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.

A Review of Machine Learning Approaches in Assisted Reproductive Technologies

INTRODUCTION

Assisted reproductive technologies (ART) are recent improvements in infertility treatment. However, there is no significant increase in pregnancy rates with the aid of ART. Costly and complex process of ART's makes them as challenging issues. Computational prediction models could predict treatment outcome, before the start of an ART cycle.

AIM

This review provides an overview on machine learning-based prediction models in ART.

METHODS

This article was executed based on a literature review through scientific databases search such as PubMed, Scopus, Web of Science and Google Scholar.

RESULTS

We identified 20 papers reporting on machine learning-based prediction models in IVF or ICSI settings. All of the models were validated only by internal validation. Therefore, external validation of the models and the impact analysis of them were the missing parts of the all studies.

CONCLUSION

Machine learning-based prediction models provide a clinical decision support tool for both clinicians and patients and lead to improvement in ART success rates.

Big data analytics for preventive medicine

Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence

Mathematics rules the world of science. Innovative technologies based on mathematics have paved the way for implementation of novel strategies in assisted reproduction. Ascertaining efficient embryo selection in order to secure optimal pregnancy rates remains the focus of the in vitro fertilization scientific community and the strongest driver behind innovative approaches. This scoping review aims to describe and analyze complex models based on mathematics for embryo selection, devices, and software most widely employed in the IVF laboratory and algorithms in the service of the cutting-edge technology of artificial intelligence. Despite their promising nature, the practicing embryologist is the one ultimately responsible for the success of the IVF laboratory and thus the one to approve embracing pioneering technologies in routine practice. Applied mathematics and computational biology have already provided significant insight into the selection of the most competent preimplantation embryo. This review describes the leap of evolution from basic mathematics to bioinformatics and investigates the possibility that computational applications may be the means to foretell a promising future for the IVF clinical practice.

Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment

Objectives

In this study, a clinical decision support system was developed to help general practitioners assess the need for orthodontic treatment in patients with permanent dentition.

Methods

We chose a Bayesian network (BN) as the underlying model for assessing the need for orthodontic treatment. One thousand permanent dentition patient data sets chosen from a hospital record system were prepared in which one data element represented one participant with information for all variables and their stated need for orthodontic treatment. To evaluate the system, we compared the assessment results based on the judgements of two orthodontists to those recommended by the decision support system.

Results

In a BN decision support model, each variable is modelled as a node, and the causal relationship between two variables may be represented as a directed arc. For each node, a conditional probability table is supplied that represents the probabilities of each value of this node, given the conditions of its parents. There was a high degree of agreement between the two orthodontists (kappa value = 0.894) in their diagnoses and their judgements regarding the need for orthodontic treatment. Also, there was a high degree of agreement between the decision support system and orthodontists A (kappa value = 1.00) and B (kappa value = 0.894).

Conclusions

The study was the first testing phase in which the results generated by the proposed system were compared with those suggested by expert orthodontists. The system delivered promising results; it showed a high degree of accuracy in classifying patients into groups needing and not needing orthodontic treatment.

Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment

BACKGROUND AND OBJECTIVE

Aspiration of a good-quality sperm during intracytoplasmic sperm injection (ICSI) is one of the main concerns. Understanding the influence of individual sperm morphology on fertilization, embryo quality, and pregnancy probability is one of the most important subjects in male factor infertility. Embryologists need to decide the best sperm for injection in real time during ICSI cycle. Our objective is to predict the quality of zygote, embryo, and implantation outcome before injection of each sperm in an ICSI cycle for male factor infertility with the aim of providing a decision support system on the sperm selection.

METHODS

The information was collected from 219 patients with male factor infertility at the infertility therapy center of Alzahra hospital in Rasht from 2012 through 2014. The prepared dataset included the quality of zygote, embryo, and implantation outcome of 1544 injected sperms into the related oocytes. In our study, embryo transfer was performed at day 3. Each sperm was represented with thirteen clinical features. Data preprocessing was the first step in the proposed data mining algorithm. After applying more than 30 classifiers, 9 successful classifiers were selected and evaluated by 10-fold cross validation technique using precision, recall, F1, and AUC measures. Another important experiment was measuring the effect of each feature in prediction process.

RESULTS

In zygote and embryo quality prediction, IBK and RandomCommittee models provided 79.2% and 83.8% F1, respectively. In implantation outcome prediction, KStar model achieved 95.9% F1, which is even better than prediction of human experts. All these predictions can be done in real time.

CONCLUSIONS

A machine learning-based decision support system would be helpful in sperm selection phase of ICSI cycle to improve the success rate of ICSI treatment.

Validation of artificial neural network models for predicting biochemical markers associated with male infertility

Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres.

ABBREVIATIONS

AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous epididymal sperm spiration; RBFN: radical basis function network; SRNN: simple recurrent neural network; SVM: support vector machines; TSE: testicular sperm extraction; WHO: World Health Organization.

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

Machine learning techniques have been previously used to assist clinicians to select embryos for human-assisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve machine learning techniques for embryo selection. In this study, a dataset of 330 consecutive cycles (and associated embryos) carried out by the Unit of Assisted Reproduction of the Hospital Donostia (Spain) throughout 18 months has been analyzed. The problem of the embryo selection has been modeled by a novel weakly supervised paradigm, learning from label proportions, which considers all the available data, including embryos whose fate cannot be certainly established. Furthermore, all the collected features, describing cycles and embryos, have been considered in a multi-variate data analysis. Our integral solution has been successfully tested. Experimental results show that the proposed technique consistently outperforms an equivalent approach based on standard supervised classification. Embryos in this study were selected for transference according to the criteria of the Spanish Association for Reproduction Biology Studies. Obtained classification models outperform these criteria, specifically reordering medium-quality embryos.

Influence of sperm impact angle on successful fertilization through mZP oscillatory spherical net model

According to the available literature, penetrating sperm creates an oblique path trough Zona pellucida (ZP)--the most outer surface of oocytes. Considering fertilization process as an oscillatory phenomenon, the influence of sperm impact angle relative to the oscillatory behavior of mouse ZP is described by using the discrete continuum mechanical model in the form of a spherical net model. A parametric frequency analysis of oscillatory behavior of knot material particles in the mouse ZP (mZP) spherical net model is conducted by using generalized Lussajous curves. The influence of impact angles of sperm cells on the corresponding knot mass particles' resultant trajectory is discussed. Favorable sperm impact angles for successful fertilization are identified.

Identifying high-cost patients using data mining techniques and a small set of non-trivial attributes

In this paper, we use data mining techniques, namely neural networks and decision trees, to build predictive models to identify very high-cost patients in the top 5 percentile among the general population. A large empirical dataset from the Medical Expenditure Panel Survey with 98,175 records was used in our study. After pre-processing, partitioning and balancing the data, the refined dataset of 31,704 records was modeled by Decision Trees (including C5.0 and CHAID), and Neural Networks. The performances of the models are analyzed using various measures including accuracy, G-mean, and Area under ROC curve. We concluded that the CHAID classifier returns the best G-mean and AUC measures for top performing predictive models ranging from 76% to 85%, and 0.812 to 0.942 units, respectively. We also identify a small set of 5 non-trivial attributes among a primary set of 66 attributes to identify the top 5% of the high cost population. The attributes are the individual׳s overall health perception, age, history of blood cholesterol check, history of physical/sensory/mental limitations, and history of colonic prevention measures. The small set of attributes are what we call non-trivial and does not include visits to care providers, doctors or hospitals, which are highly correlated with expenditures and does not offer new insight to the data. The results of this study can be used by healthcare data analysts, policy makers, insurer, and healthcare planners to improve the delivery of health services.

Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: a case study of Taiwan

The Bayesian network (BN) is a promising method for modeling cancer metastasis under uncertainty. BN is graphically represented using bioinformatics variables and can be used to support an informative medical decision/observation by using probabilistic reasoning. In this study, we propose such a BN to describe and predict the occurrence of brain metastasis from lung cancer. A nationwide database containing more than 50,000 cases of cancer patients from 1996 to 2010 in Taiwan was used in this study. The BN topology for studying brain metastasis from lung cancer was rigorously examined by domain experts/doctors. We used three statistical measures, namely, the accuracy, sensitivity, and specificity, to evaluate the performances of the proposed BN model and to compare it with three competitive approaches, namely, naive Bayes (NB), logistic regression (LR) and support vector machine (SVM). Experimental results show that no significant differences are observed in accuracy or specificity among the four models, while the proposed BN outperforms the others in terms of sampled average sensitivity. Moreover the proposed BN has advantages compared with the other approaches in interpreting how brain metastasis develops from lung cancer. It is shown to be easily understood by physicians, to be efficient in modeling non-linear situations, capable of solving stochastic medical problems, and handling situations wherein information are missing in the context of the occurrence of brain metastasis from lung cancer.