Development of a Dynamic Diagnosis Grading System for Infertility Using Machine Learning.

Importance: Many indicators need to be considered when judging the condition of patients with infertility, which makes diagnosis and treatment complicated. Objective: To construct a dynamic scoring system for infertility to assist clinicians in efficiently and accurately assessing the condition of patients with infertility. Design, Setting, and Participants: This prognostic study reviewed 95 868 medical records of couples with infertility in which women had undergone in vitro fertilization and embryo transfer at the Reproductive Center of Tongji Medical College, Huazhong University of Science and Technology, in Wuhan, Hubei, China, from January 2006 to May 2019. A dynamic diagnosis and grading system for infertility was constructed. The analysis was conducted between May 20, 2019, and April 15, 2020. Main Outcomes and Measures: Patients were divided into pregnant and nonpregnant groups according to eventual pregnancy results. The evaluation index system was constructed based on the test results of the significant difference between the 2 groups of indicators and the clinician's experience. Random forest machine learning was used to determine the weight of the index, and the entropy-based feature discretization algorithm classified the abnormality of the index and the patient's condition. A 10-fold cross-validation method was used to test the validity of the system. Results: A total of 60 648 couples with infertility were enrolled, in which 15 021 women became pregnant, with a mean (SD) age of 30.30 (4.02) years. A total of 45 627 couples were in the nonpregnant group, with a mean (SD) age among women of 32.17 (5.58) years. Seven indicators were selected to build the dynamic grading system for patients with infertility: age, body mass index, follicle-stimulating hormone level, antral follicle count, anti-Mullerian hormone level, number of oocytes, and endometrial thickness. The importance weight of each indicator obtained by the random forest algorithm was 0.1748 for age, 0.0785 for body mass index, 0.0581 for follicle-stimulating hormone level, 0.1214 for antral follicle count, 0.1616 for anti-Mullerian hormone level, 0.2307 for number of oocytes, and 0.1749 for endometrial thickness. The grading system divided the condition of the patient with infertility into 5 grades from A to E. The worst E grade represented a 0.90% pregnancy rate, and the pregnancy rate in the A grade was 53.82%. The cross-validation results showed that the stability of the system was 95.94% (95% CI, 95.14%-96.74%). Conclusions and Relevance: This machine learning-derived algorithm may assist clinicians in making an efficient and accurate initial judgment on the condition of patients with infertility.

Driving forces of China's multisector CO2 emissions: a Log-Mean Divisia Index decomposition.

To figure out which factor contributes more on carbon emissions caused by energy consumption, this research took multisector analysis based on the Log-Mean Divisia Index Method (LMDI) and decoupling theory to assess the driving factors of carbon dioxide (CO2) emissions in China's six sectors from 2003 to 2016. Our empirical results reveal that China's economy can be divided as three decoupling stages and exhibited a distinct tendency toward strong decoupling with a turning point in 2008. Thus, we discuss the impact of 2008 economic crisis on carbon emissions based on decomposition results. The empirical results of our study show the following five conclusions. (1) Most sectors in China are in weak decoupling state due to the inhibition of energy intensity on carbon emissions. (2) Different factors contribute differently to reducing emissions in different sectors, economic output has the most prominent effect, followed by energy intensity and population scale. (3) China's current carbon emission reduction measures benefit more on energy efficiency. (4) The economic crisis has greatly reduced energy efficiency and has no significant impact on other factors. (5) If all industries adjust their energy mix, carbon emissions in China can be reduced by almost 17% every year.

Prediction of in vitro fertilization outcome at different antral follicle count thresholds combined with female age, female cause of infertility, and ovarian response in a prospective cohort of 8269 women.

Antral follicle count (AFC) has been widely investigated for the prediction of clinical pregnancy or live birth. This study discussed the effects of AFC quartile levels on pregnancy outcomes combined with female age, female cause of infertility, and ovarian response undergoing in vitro fertilization (IVF) treatment. At present, many research about AFC mainly discuss its impact on clinical practice at different thresholds, or the analyses of AFC with respect to assisted reproductive technology outcomes under using different ovarian stimulation protocols. Factors that include ovarian sensitivity index, female age, and infertility cause are all independent predictors of live birth undergoing IVF/intracytoplasmic sperm injection, while few researchers discussed influence of female-related factors for clinical outcomes in different AFC fields.A total of 8269 infertile women who were stimulated with a long protocol with normal menstrual cycles were enrolled in the study, and patients were categorized into 4 groups based on AFC quartiles (1-8, 9-12, 13-17, and ≥18 antral follicles).The clinical pregnancy rates increased in the 4 AFC groups (28.25% vs 35.38% vs 37.38% vs 40.13%), and there was a negative association between age and the 4 AFC groups. In addition, female cause of infertility like polycystic ovary syndrome, Tubal factor, and other causes had great significance on clinical outcome, and ovarian response in medium (9-16 oocytes retrieved) had the highest clinical pregnancy rate at AFC quartiles of 1 to 8, 9 to 12, 13 to 17, and ≥18 antral follicles.This study concludes that the female-related parameters (female cause of infertility, female age, and ovarian response) combined with AFC can be useful to estimate the probability of clinical pregnancy.

Decision analysis about the cost-effectiveness of different in vitro fertilization-embryo transfer protocol under considering governments, hospitals, and patient.

OBJECTIVE: The aim of this study was to explore the benefits of in vitro fertilization (IVF) for patients and hospitals under different protocols and if IVF treatment should be incorporated into health care. PERSPECTIVE: The government should consider including IVF treatment in health insurance. Hospitals and patients could obtain the best benefit by following the hospital's recommended protocol. SETTING: This retrospective study was conducted from January 2014 to August 2017 at an academic hospital. METHODS: A total of 7440 patients used gonadotropin-releasing hormone agonists (GnRHa) protocol, 2619 patients used, gonadotropin-releasing hormone antagonists (GnRHant) protocol, and 1514 patients used GnRHa ultra-long protocol. Primary outcomes were live birth rate (LBR), cost-effectiveness, hospital revenue, and government investment. RESULTS: The cycle times for the GnRHa protocol and the GnRHa ultra-long protocol were significantly higher than the GnRHant protocol. Patients who were ≤29 years chose the GnRHant protocol. The cost of a successful cycle was 67,579.39 ±â€Š9,917.55 ¥ and LBR was 29.25%. Patients who were >30 years had the GnRHa protocol as the dominant strategy, as it was more effective at lower costs and higher LBR. When patients were >30 to ≤34 years, the cost of a successful cycle was 66,556.7 ±â€Š8,448.08 ¥ and the LBR was 31.05%. When patients were >35 years, the cost of a successful cycle was 83,297.92 ±â€Š10,918.05 ¥ and the LBR was 25.07%. The government reimbursement for a cycle ranged between 11,372.12 ±â€Š2,147.71 ¥ and 12,753.67 ±â€Š1,905.02 ¥. CONCLUSIONS: The government should consider including IVF treatment in health insurance. Hospitals recommend the GnRHant protocol for patients <29 years old and the GnRHa protocol for patients >30 years old, to obtain the best benefits. Patients could obtain the best benefit by using the protocol recommended by the hospital.

Comparison of recombinant and urinary follicle-stimulating hormones over 2000 gonadotropin-releasing hormone antagonist cycles: a retrospective study.

The objective of this paper was to compare the effect of recombinant follicle-stimulating hormone (rFSH) and urinary follicle-stimulating hormone (uFSH) on pregnancy rates and live birth rates with the gonadotropin-releasing hormone (GnRH) antagonist protocol in China. This retrospective study was conducted from January 2014 through August 2017. Patients treated with uFSH had significantly higher levels of luteinizing hormone (3.79 mIU/ml vs. 3.09 mIU/ml) and progesterone (0.93 ng/ml vs. 1.16 ng/ml) on the day of human chorionic gonadotropin (HCG) administration, and they also had higher pregnancy rates (24.19% vs. 22.86%). There was no significant difference in the rate of live births. In the logistic regression results of the rFSH group, the pregnancy rate was positively correlated with the level of luteinizing hormone, with an odds ratio (OR) of 1.09 (95% confidence interval [CI]: 1.00-1.18; P = 0.048). In the uFSH group, the pregnancy rate was negatively correlated with the progesterone level on the day of HCG administration, with an OR of 0.47 (95% CI: 0.27-0.77; P = 0.004). Our research concluded that uFSH performed better than rFSH in terms of pregnancy rates when it was associated with the GnRH antagonist protocol. Meanwhile, no significant differences in the rate of live births were observed between the two groups.

Fuzzy multi-objective medical service organization selection model considering limited resources and stochastic demand in emergency management.

In recent years, most countries around the world have faced increasing pressures in the realm of emergency management than ever before. Medical service organization selection is one of the most vital facets of emergency management. Meanwhile, during the selection process, many criteria may conflict with one another and information is uncertain, rendering decision-making processes complex. Hence, multi-objective optimization, fuzzy way and stochastic theories serve as suitable means of addressing such problems. In this paper, a fuzzy multi-objective linear model is developed to overcome medical service organization selection issues and uncertain information. Meanwhile, a fuzzy objective and weight are applied to enable the decision-maker to select suitable schemes while considering stochastic medical service demand. Moreover, real data cannot been obtained. Hence, according to actual conditions, we assume relative information. For illustrative purposes, a numerical example is presented to verify the effectiveness of the proposed model from experimental data.