Technologies for Prediction of Preeclampsia

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters

Hypertensive disorders of pregnancy (HDPs) remain a major challenge in maternal health. Early prediction of HDPs is crucial for timely intervention. Most existing predictive machine learning (ML) models rely on costly methods like blood, urine, genetic tests, and ultrasound, often extracting features from data gathered throughout pregnancy, delaying intervention. This study developed an ML model to identify HDP risk before clinical onset using affordable methods. Features were extracted from blood pressure (BP) measurements, body mass index values (BMI) recorded during the first and second trimesters, and maternal demographic information. We employed a random forest classification model for its robustness and ability to handle complex datasets. Our dataset, gathered from large academic medical centers in Atlanta, Georgia, United States (2010-2022), comprised 1,190 patients with 1,216 records collected during the first and second trimesters. Despite the limited number of features, the model's performance demonstrated a strong ability to accurately predict HDPs. The model achieved an F1-score, accuracy, positive predictive value, and area under the receiver-operating characteristic curve of 0.76, 0.72, 0.75, and 0.78, respectively. In conclusion, the model was shown to be effective in capturing the relevant patterns in the feature set necessary for predicting HDPs. Moreover, it can be implemented using simple devices, such as BP monitors and weight scales, providing a practical solution for early HDPs prediction in low-resource settings with proper testing and validation. By improving the early detection of HDPs, this approach can potentially help with the management of adverse pregnancy outcomes.

Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields

This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy sets, including Cosine similarity, Jaccard similarity, and Exponential similarity. Additionally, we propose Entropy, Inclusion, and Distance measures, providing a robust theoretical foundation supported by developed theorems that explain the interactions between these metrics. The practical implications of these theoretical advancements are demonstrated through various case studies. Specifically, we apply the similarity measures to predict preeclampsia, a severe condition affecting pregnant women, showcasing their potential in medical diagnostics. The entropy measure is used to identify the optimal materials manufacturing method for medical surgical robots, underscoring its importance in ensuring patient safety and the effectiveness of medical procedures. Furthermore, the inclusion measure is employed in pattern recognition tasks, highlighting its utility in complex data analysis. The comparative and superiority analysis shows the effectiveness of our research. The novel aspect of this study is the implementation of information metrics for LDMFS. These efforts aim to enhance the impact and practical applicability of linear diophantine multi-fuzzy sets, fostering innovation and improving outcomes across multiple fields.

Is high normotension a norm or a risk factor for perinatal complications: prospective cohort study

Aim. Comparative assessment of the effect of high normotension and hypertension 12nd stage on the risk of gestational and perinatal complications. Materials and methods. A prospective cohort study (n=110) assessing the effect of high normotension on the risk of gestational complications and pregnancy outcomes was conducted. The main group (n=70) included 30 patients with high normotension subgroup A, and 40 patients with hypertension 12nd stage subgroup B. The comparison group included 40 patients with "white coat hypertension". Results. The most frequent complications in the 2nd and 3rd trimesters of pregnancy were toxaemia, threatened miscarriage, edema (detected in more than half of patients with high normotension and hypertension 12nd stage) and well as threatened preterm labor (p0.05). The frequency of pre-eclampsia development in subgroups A and B did not differ significantly, however, in patients with high normotension in the 3rd trimester during a test of endothelium-dependent vasodilation were detected signs of endothelial dysfunction, which may be one of the mechanisms for the subsequent formation of hypertension in these patients. Placental insufficiency of varying severity was detected only in subgroups A and B. Placental insufficiency, along with intrauterine fetal hypoxia of different etiologies, were the most frequent causes of emergency caesarean section, occurring only in the main group 3 (75.0%) in subgroup A and 6 (66.67%) in subgroup B and associated with blood pressure above 130/85 mm Hg. Conclusion. The importance of preventing blood pressure increase to high normal rate is explained by the development of endothelial dysfunction at late gestation, which can serve as the mechanisms of hypertension formation in this category of pregnant women.