User Embedding Long Short-Term Model Based Fecundity Prediction Model Using Proposed Fecundity Dataset
Abstract
Fecundity prediction is a process that helps couples to understand their fertility status. Fecundity prediction as a domain could be supported by developed intelligent models using a computational method and fecundity data. Although fecundity data and models have been proposed, the problem of low data size and dimensionality of the proposed fecundity dataset has been identified due to the data collection approaches used and the problem of using a weak subfertility definition in the development of a User-embedding LSTM-based fecundity prediction model. To solve the identified problems, this study proposed a fecundity dataset by adopting a hybrid data collection approach using the strengths and disregarding the setbacks of existing data collection approaches and then proposed an improved User-embedding LSTM-based fecundity prediction model based on an improved subfertility definition. A large size fecundity dataset was generated and used for the implementation and evaluation of the existing and proposed LSTM-based fecundity prediction models and the proposed model generated better AUC-ROC evaluation results
Downloads
References
Barrett, J. C., & Marshall, J. (1969). The risk of conception on different days of the menstrual cycle. Population studies, 23(3), 455-461.
Buck Louis GM, Schisterman EF, Sweeney AM, Wilcosky TC, Gore‐Langton RE, Lynch CD, Boyd Barr D, Schrader SM, Kim S, Chen Z, Sundaram R. Designing prospective cohort studies for assessing reproductive and developmental toxicity during sensitive windows of human reproduction and development–the LIFE Study. Paediatric and perinatal epidemiology. 2011 Sep;25(5):413-24.
Chang, T., Gossa, W., Sharp, A., Rowe, Z., Kohatsu, L., Cobb, E. M., & Heisler, M. (2014). Text messaging as a community-based survey tool: a pilot study. BMC public health, 14(1), 936.
Colombo, B., Masarotto, G., & Menstrual Cycle Fecundability Study Group. (2000). Daily fecundability: first results from a new data base. Demographic research, 3.
Colombo, B., Mion, A., Passarin, K., & Scarpa, B. (2006). Cervical mucus symptom and daily fecundability: first results from a new database. Statistical Methods in Medical Research, 15(2), 161-180.
DeJonckheere, M. J., Vaughn, L. M., & Jacquez, F. (2017). Latino immigrant youth living in a nontraditional migration city: A social-ecological examination of the complexities of stress and resilience. Urban Education, 52(3), 399-426.
DeJonckheere, M., Nichols, L. P., Vydiswaran, V. V., Zhao, X., Collins-Thompson, K., Resnicow, K., & Chang, T. (2019). Using Text Messaging, social media, and Interviews to Understand What Pregnant Youth Think About Weight Gain During Pregnancy. JMIR formative research, 3(2), e11397.
Dunson, D. B. (2001). Bayesian modeling of the level and duration of fertility in the menstrual cycle. Biometrics, 57(4), 1067-1073.
Dunson, D. B., & Colombo, B. (2003). Bayesian modeling of markers of day-specific fertility. Journal of the American Statistical Association, 98(461), 28-37.
Dunson, D. B., & Stanford, J. B. (2005). Bayesian inferences on predictors of conception probabilities. Biometrics, 61(1), 126-133.
Ecochard, R. (2006). Heterogeneity in fecundability studies: issues and modelling. Statistical methods in medical research, 15(2), 141-160.
Ecochard, R., & Clayton, D. G. (2000). Multivariate parametric random effect regression models for fecundity studies. Biometrics, 56(4), 1023-1029.
Falzone, A. E., Brindis, C. D., Chren, M. M., Junn, A., Pagoto, S., Wehner, M., & Linos, E. (2017). Teens, tweets, and tanning beds: rethinking the use of social media for skin cancer prevention. American journal of preventive medicine, 53(3), S86-S94.
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential biases in machine learning algorithms using electronic health record data. JAMA internal medicine, 178(11), 1544-1547.
Greil, A. L. (1997). Infertility and psychological distress: a critical review of the literature. Social science & medicine, 45(11), 1679-1704.
Jiawei, H., & Kamber, M. (2001). Data Mining: Concept And Techniques [M].[S. l.]. America: Morgan Kaufmann Publishers, 223-224.
Liu, B., Shi, S., Wu, Y., Thomas, D., Symul, L., Pierson, E., & Leskovec, J. (2019). Predicting pregnancy using large-scale datafrom a women’s Health tracking mobile application. In The World Wide Web Conference (pp. 2999-3005). ACM.
Lum, K. J., Sundaram, R., Buck Louis, G. M., & Louis, T. A. (2016). A Bayesian joint model of menstrual cycle length and fecundity. Biometrics, 72(1), 193-203.
Maddox, T. M., Rumsfeld, J. S., & Payne, P. R. (2019). Questions for artificial intelligence in health care. Jama, 321(1), 31-32
McDonald, J. W., Rosina, A., Rizzi, E., & Colombo, B. (2011). Age and fertility: can women wait until their early thirties to try for a first birth? Journal of biosocial science, 43(6), 685-700.
Mikkelsen, E. M., Hatch, E. E., Wise, L. A., Rothman, K. J., Riis, A., & Sørensen, H. T. (2009). Cohort profile: the Danish web-based pregnancy planning study—‘Snart-Gravid’. International journal of epidemiology, 38(4), 938-943.
Olah C. (2015, August 27), Understanding LSTM Networks, Colah’s blog, https://colah.github.io/posts/2015-08-Understanding-LSTMs/.
Pennoni, F., Barbato, M., & Del Zoppo, S. (2017). a latent Markov Model with covariates to study Unobserved heterogeneity among Fertility Patterns of couples employing natural Family Planning Methods. Frontiers in Public Health, 5, 186.
Scarpa, B., & Dunson, D. B. (2007). Bayesian methods for searching for optimal rules for timing intercourse to achieve pregnancy. Statistics in medicine, 26(9), 1920-1936.
Scherwitzl, E. B., Danielsson, K. G., Sellberg, J. A., & Scherwitzl, R. (2016). Fertility awareness-based mobile application for contraception. The European Journal of Contraception & Reproductive Health Care.Smarr, M. M., Sapra, K. J., Gemmill, A., Kahn, L. G., Wise, L. A., Lynch, C. D., ... & Lobdell, D. T. (2017). Is human fecundity changing? A discussion of research and data gaps precluding us from having an answer. Human Reproduction, 32(3), 499-504.
Schwartz, D., MacDonald, P. D. M., & Heuchel, V. (1980). Fecundity, coital frequency and the viability of ova. Population Studies, 34(2), 397-400.
Sharp, A. L., Chang, T., Cobb, E., Gossa, W., Rowe, Z., Kohatsu, L., & Heisler, M. (2014). Exploring real-time patient decision-making for acute care: a pilot study. Western Journal of Emergency Medicine, 15(6), 675.
Smarr, M. M., Sapra, K. J., Gemmill, A., Kahn, L. G., Wise, L. A., Lynch, C. D., ... & Lobdell, D. T. (2017). Is human fecundity changing? A discussion of research and data gaps precluding us from having an answer. Human Reproduction, 32(3), 499-504.
Stanford J. B., Smith K. R. (2000). Characteristics of women associated with continuing instruction in the Creighton Model Fertility Care System. Contraception. 1;61(2):121-9.
Stead, W. W. (2018). Clinical implications and challenges of artificial intelligence and deep learning. Jama, 320(11), 1107-1108.
Symul, L., Wac, K., Hillard, P., &Salathe, M. (2018). Assesment of Mestrual Health Status and Evolution through Mobile Apps for FertitlityAwareness .BioRxiv, 385054.
Van der Steeg, J. W., Steures, P., Eijkemans, M. J., Habbema, J. D. F., Hompes, P. G., Broekmans, F. J., ... & Mol, B. W. (2006). Pregnancy is predictable: a large-scale prospective external validation of the prediction of spontaneous pregnancy in subfertile couples. Human reproduction, 22(2), 536-542.
Copyright (c) 2023 Muhammad Ahmad Shehu, Muhammad Bashir Abdullahi, PhD, Mohammed Danlami Abdulmalik, PhD, Opeyemi Aderike Abisoye, PhD
This work is licensed under a Creative Commons Attribution 4.0 International License.
