Thyroid pathology is reported internationally in 5–10% of all pregnancies. The overall aim of this research was to determine the prevalence of hypothyroidism and risk factors during the first trimester screening in a Mexican patients sample. We included the records of 306 patients who attended a prenatal control consultation between January 2016 and December 2017 at the Women’s Institute in Monterrey, Mexico. The studied sample had homogeneous demographic characteristics in terms of age, weight, height, BMI (body mass index) and number of pregnancies. The presence of at least one of the risk factors for thyroid disease was observed in 39.2% of the sample. Two and three clusters were identified, in which patients varied considerably among risk factors, symptoms and pregnancy complications. Compared to Cluster 0, one or more symptoms or signs of hypothyroidism occurred, while Cluster 1 was characterized by healthier patients. When three clusters were used, Cluster 2 had a higher TSH (thyroid stimulating hormone) value and pregnancy complications. There were no significant differences in perinatal variables. In addition, high TSH levels in first trimester pregnancy are characterized by pregnancy complications and decreased newborn weight. Our findings underline the high degree of disease heterogeneity with existing pregnant hypothyroid patients and the need to improve the phenotyping of the syndrome in the Mexican population.

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Hyper-heuristics have arisen as methods that increase the generality of existing solvers. They have proven helpful for dealing with complex problems, particularly those related to combinatorial optimization. Their recent growth in popularity has increased the daily amount of text in the related literature. This information is primarily unstructured, mainly text that traditional computer data systems cannot process. Traditional systematic literature review studies exhibit multiple limitations, including high time consumption, lack of replicability, and subjectivity of the results. For this reason, text mining has become essential for researchers in recent years. Therefore, efficient text mining techniques are needed to extract meaningful information, patterns, and relationships. This study adopts a literature review of 963 journal and conference papers on hyper-heuristic-related works. We first describe the essential text mining techniques, including text preprocessing, word clouds, clustering, and frequent association rule learning in hyper-heuristic publications. With that information, we implement visualization tools to understand the most frequent relations and topics in the hyper-heuristic domain. The main findings highlight the most dominant topics in the literature. We use text mining analysis to find widespread manifestations, representing the significance of the different areas of hyper-heuristics. Furthermore, we apply clustering to provide seven categories showing the associations between the topics related to hyper-heuristic literature. The vast amount of data available that we find opens up a new opportunity for researchers to analyze the status of hyper-heuristics and help create strategic plans regarding the scope of hyper-heuristics. Lastly, we remark that future work will address the limitations of collecting information from multiple data sources and analyze book chapters related to hyper-heuristics.

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