Trump officials are expected to link the use of pain reliever Tylenol in pregnant women to autism, according to US media reports. At an Oval Office event on Monday, the US president will reportedly advise pregnant women in the US to only take Tylenol, known as paracetamol elsewhere, to relieve high fevers. At the Charlie Kirk memorial service on Sunday, Trump said he had an amazing announcement coming on autism, saying it was out of control but they might now have a reason why. Some studies have shown a link between pregnant women taking Tylenol and autism, but these findings are inconsistent and do not prove the drug causes autism. Tylenol is a popular brand of pain relief medication sold in the United States, Canada and some other countries.

Malnutrition among pregnant women is a major public health challenge in Ethiopia, increasing the risk of adverse maternal and neonatal outcomes. Traditional statistical approaches often fail to capture the complex and multidimensional determinants of nutritional status. This study develops a predictive model using ensemble machine learning techniques, leveraging data from the Ethiopian Demographic and Health Survey (2005-2020), comprising 18,108 records with 30 socio-demographic and health attributes. Data preprocessing included handling missing values, normalization, and balancing with SMOTE, followed by feature selection to identify key predictors. Several supervised ensemble algorithms including XGBoost, Random Forest, CatBoost, and AdaBoost were applied to classify nutritional status. Among them, the Random Forest model achieved the best performance, classifying women into four categories (normal, moderate malnutrition, severe malnutrition, and overnutrition) with 97.87% accuracy, 97.88% precision, 97.87% recall, 97.87% F1-score, and 99.86% ROC AUC. These findings demonstrate the effectiveness of ensemble learning in capturing hidden patterns from complex datasets and provide timely insights for early detection of nutritional risks. The results offer practical implications for healthcare providers, policymakers, and researchers, supporting data-driven strategies to improve maternal nutrition and health outcomes in Ethiopia.

Abstract--Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiologists requires expertise and sophisticated equipment, which would otherwise fail to improve the rate of identifying specific types of fetal central nervous system (CNS) abnormalities and result in unnecessary patient examinations. We construct a deep learning model to improve the overall accuracy of the diagnosis of fetal cranial anomalies to aid prenatal diagnosis. In our collected multi-center dataset of fetal craniocerebral anomalies covering four typical anomalies of the fetal central nervous system (CNS): anencephaly, encephalocele (including meningocele), holoprosencephaly, and rachischisis, patient-level prediction accuracy reaches 94.5%, with an AUROC value of 99.3%. In the subgroup analyzes, our model is applicable to the entire gestational period, with good identification of fetal anomaly types for any gestational period. Heatmaps superimposed on the ultrasound images not only provide a visual interpretation for the algorithm but also provide an intuitive visual aid to the physician by highlighting key areas that need to be reviewed, helping the physician to quickly identify and validate key areas. Finally, the retrospective reader study demonstrates that by combining the automatic prediction of the DL system with the professional judgment of the radiologist, the diagnostic accuracy and efficiency can be effectively improved and the misdiagnosis rate can be reduced, which has an important clinical application prospect. Optimizing the prenatal ultrasound diagnosis process can significantly reduce Ultrasonography is popular as a non-invasive and radiationfree the workload of the sonographer; therefore, the application of prenatal diagnostic method for its convenience and low artificial intelligence (AI) and deep learning (DL) techniques cost [1]. Antenatal ultrasound is a crucial imaging tool during in ultrasound imaging can significantly speed up the prenatal pregnancy. It not only assesses fetal growth and development examination process while improving the accuracy and consistency and detects congenital anomalies, but also provides important of the diagnosis. Deep learning, a subset of AI, automatically extracts ultrasound, physicians can assess the presence of congenital features from large amounts of data and performs efficient anomalies in the fetus with the help of two-dimensional (2D) pattern recognition and prediction using deep neural network and three-dimensional (3D) imaging, thus helping to significantly models [5].

The concept of Quality of Life (QoL) refers to a holistic measurement of an individual's well-being, incorporating psychological and social aspects. Pregnant women, especially those with obesity and stress, often experience lower QoL. Physical activity (PA) has shown the potential to enhance the QoL. However, pregnant women who are overweight and obese rarely meet the recommended level of PA. Studies have investigated the relationship between PA and QoL during pregnancy using correlation-based approaches. These methods aim to discover spurious correlations between variables rather than causal relationships. Besides, the existing methods mainly rely on physical activity parameters and neglect the use of different factors such as maternal (medical) history and context data, leading to biased estimates. Furthermore, the estimations lack an understanding of mediators and counterfactual scenarios that might affect them. In this paper, we investigate the causal relationship between being physically active (treatment variable) and the QoL (outcome) during pregnancy and postpartum. To estimate the causal effect, we develop a Causal Machine Learning method, integrating causal discovery and causal inference components. The data for our investigation is derived from a long-term wearable-based health monitoring study focusing on overweight and obese pregnant women. The machine learning (meta-learner) estimation technique is used to estimate the causal effect. Our result shows that performing adequate physical activity during pregnancy and postpartum improves the QoL by units of 7.3 and 3.4 on average in physical health and psychological domains, respectively. In the final step, four refutation analysis techniques are employed to validate our estimation.

Long-term fetal heart rate (FHR) monitoring during the antepartum period, increasingly popularized by electronic FHR monitoring, represents a growing approach in FHR monitoring. This kind of continuous monitoring, in contrast to the short-term one, collects an extended period of fetal heart data. This offers a more comprehensive understanding of fetus's conditions. However, the interpretation of long-term antenatal fetal heart monitoring is still in its early stages, lacking corresponding clinical standards. Furthermore, the substantial amount of data generated by continuous monitoring imposes a significant burden on clinical work when analyzed manually. To address above challenges, this study develops an automatic analysis system named LARA (Long-term Antepartum Risk Analysis system) for continuous FHR monitoring, combining deep learning and information fusion methods. LARA's core is a well-established convolutional neural network (CNN) model. It processes long-term FHR data as input and generates a Risk Distribution Map (RDM) and Risk Index (RI) as the analysis results. We evaluate LARA on inner test dataset, the performance metrics are as follows: AUC 0.872, accuracy 0.816, specificity 0.811, sensitivity 0.806, precision 0.271, and F1 score 0.415. In our study, we observe that long-term FHR monitoring data with higher RI is more likely to result in adverse outcomes (p=0.0021). In conclusion, this study introduces LARA, the first automated analysis system for long-term FHR monitoring, initiating the further explorations into its clinical value in the future.

Falls represent one of the most detrimental occurrences for the elderly. Given the continually increasing ageing demographic, there is a pressing demand for advancing fall detection systems. The swift progress in sensor networks and the Internet of Things (IoT) has made human-computer interaction through sensor fusion an acknowledged and potent approach for tackling the issue of fall detection. Even IoT-enabled systems can deliver economical health monitoring solutions tailored to pregnant women within their daily environments. Recent research indicates that these remote health monitoring setups have the potential to enhance the well-being of both the mother and the infant throughout the pregnancy and postpartum phases. One more emerging advancement is the integration of 'panic buttons,' which are gaining popularity due to the escalating emphasis on safety. These buttons instantly transmit the user's real-time location to pre-designated emergency contacts when activated. Our solution focuses on the above three challenges we see every day. Fall detection for the elderly helps the elderly in case they fall and have nobody around for help. Sleep pattern sensing is helpful for pregnant women based on the SPO2 sensors integrated within our device. It is also bundled with heart rate monitoring. Our third solution focuses on a panic situation; upon pressing the determined buttons, a panic alert would be sent to the emergency contacts listed. The device also comes with a mobile app developed using Flutter that takes care of all the heavy processing rather than the device itself.

Over 800 women died from pregnancy-related complications in 2020 in the United States, and well over half of these deaths were preventable. This isn’t news for professionals who work in this space because maternal deaths in the U.S. outnumber those in most industrialized nations in the west. Maternal outcomes for Black women in the US are even worse. Black mothers die from pregnancy-related complications nearly three times more frequently than white and Hispanic women. Despite efforts to prioritize improved maternal mortality, there’s been very little progress. In fact, maternal mortality actually increased over 18% between 2019 and 2020. Much of this increase is a result of the Covid-19 pandemic. For excess deaths that aren’t attributable to complications from Covid-19, technology may hold the solution. Artificial Intelligence may improve maternal health outcomes in the US Many tech companies focused on developing and implementing AI-driven solutions to healthcare problems in the last few years. These recent advancements in AI give patients and their healthcare providers hope amidst worsening maternal health outcomes in the United States. Some of the newest iterations of AI for healthcare identify pregnant women who are at risk for premature birth and other complications. In doing so, it allows healthcare providers and patient care teams to intervene before it’s too late. As a result, their pregnant patients benefit from health education, as well as medical care and access to social services to lower their risk for negative outcomes. There’s even an element of this technology that leverages the social determinants of health (SDOH). If a pregnant woman has a history of preterm birth or preeclampsia, is struggling to find food, or they can’t get to their appointments because of inadequate transportation, AI helps their healthcare provider understand how those influence maternal and infant health outcomes. This way, healthcare providers can address them early, giving pregnant patients their best chance at positive outcomes. Identifying at-risk pregnant women earlier with AI Traditional methods of care limited healthcare providers’ efforts to identify at-risk pregnant women especially in comparison to what we’ve been able to accomplish with AI and other technology in recent years. Before these advances, OB/GYNs and midwives were tasked with using often incomplete data along with persistence and sheer luck. They did their best to call patients with no guarantee that they would reach them. Many OB/GYNs and midwives did their best, but pregnant moms deserve so much more than that—especially as we face climbing maternal mortality around the world. Now, maternal care providers don’t have to spend long hours rounding up patient data and hoping to catch pregnant patients on the phone. AI has the power to provide all of that data, along with a complete analysis that identifies which patients are at the highest risk. The result? Identifying pregnancies sooner and targeted outreach efforts that prioritize high-risk patients. Healthcare providers who use AI in maternal care can identify over 70% of at-risk moms during the first trimester. This incredible benefit allows these parents to get the care they need from the very beginning of their prenatal journey. Earlier access to care allows for better outcomes and interventions for both mothers and their babies. And AI prepares the folks providing care and doing patient outreach ahead of time so they know exactly how to support each and every patient. Insurance plans are also using this AI to significantly reduce preterm birth disparities for pregnant Black women across the nation. There was a 10% reduction in preterm births and low birth weight among babies whose mothers received care and support leveraging AI tools. This helps avoid lifelong consequences for newborns, including breathing, hearing, and vision issues, developmental delays, and other health complications. The benefits of AI don’t end when a pregnant woman gives birth, though. It continues collecting and analyzing patient data into the fourth trimester, when women are still at risk for life-threatening pregnancy-related complications, like preeclampsia and mental health challenges like postpartum depression or psychosis. Lawmakers are taking maternal mortality to task Tech companies and healthcare providers aren’t the only stakeholders trying to improve maternal mortality rates in the US. It’s become a legislative priority for many folks on Capitol Hill, including President Joe Biden and Vice President Kamala Harris. Together with many federal agencies, they released the White House Blueprint for Addressing the Maternal Health Crisis just last year. This historic effort aims to improve maternal health outcomes by providing more economic and social support to women before, during, and after pregnancy while also prioritizing research and data collection among other key agenda items. Recently, the National Governors Association (NGA) Chair New Jersey Governor Phil Murphy and First Lady Tammy Snyder Murphy hosted a roundtable discussion about efforts to improve birth outcomes and reduce overall maternal and infant mortality and morbidity. One area of focus that Mrs. Murphy highlighted included the significant influence “data holds to transform our national maternal health landscape [ … ] with up-to-date information, we can create policies that are informed by reality. We can direct our strategy to target specific goals. And, most importantly, we can measure our performance and make essential revisions, change direction and learn from our experiences.” Additionally, administrations in states such as Texas and Florida are including maternal health goals in their Medicaid managed care contracts. As the government continues working toward better maternal healthcare, AI and other technology will undoubtedly be a key piece of the puzzle to improve health outcomes and reduce health disparities. Photo: FatCamera, Getty Images

Malaria can be prevented, diagnosed, and treated; however, every year, there are more than 200 million cases and 200.000 preventable deaths. Malaria remains a pressing public health concern in low- and middle-income countries, especially in sub-Saharan Africa. We describe how by means of mobile health applications, machine-learning-based adaptive interventions can strengthen malaria surveillance and treatment adherence, increase testing, measure provider skills and quality of care, improve public health by supporting front-line workers and patients (e.g., by capacity building and encouraging behavioral changes, like using bed nets), reduce test stockouts in pharmacies and clinics and informing public health for policy intervention.

In 2002, Target hired statistician Andrew Pole. His job was to use predictive analytics -- a form of statistics that makes predictions by observing data trends -- to help the retail giant market certain products to certain groups of people. Along those lines, Pole's first task was to identify pregnant women -- specifically women in their second trimester. As Target's marketing team explained to him, new parents are extremely valuable customers whose brand loyalty tends to change when they have kids because they purchase things they probably weren't purchasing before -- like diapers, formula, baby clothes, etc. New parents also tend to be physically exhausted and therefore more prone to do all of their shopping at one place.

COVID-19 pandemic is an ongoing global pandemic which has caused unprecedented disruptions in the public health sector and global economy. The virus, SARS-CoV-2 is responsible for the rapid transmission of coronavirus disease. Due to its contagious nature, the virus can easily infect an unprotected and exposed individual from mild to severe symptoms. The study of the virus effects on pregnant mothers and neonatal is now a concerning issue globally among civilians and public health workers considering how the virus will affect the mother and the neonates health. This paper aims to develop a predictive model to estimate the possibility of death for a COVID-diagnosed mother based on documented symptoms: dyspnea, cough, rhinorrhea, arthralgia, and the diagnosis of pneumonia. The machine learning models that have been used in our study are support vector machine, decision tree, random forest, gradient boosting, and artificial neural network. The models have provided impressive results and can accurately predict the mortality of pregnant mothers with a given input.The precision rate for 3 models(ANN, Gradient Boost, Random Forest) is 100% The highest accuracy score(Gradient Boosting,ANN) is 95%,highest recall(Support Vector Machine) is 92.75% and highest f1 score(Gradient Boosting,ANN) is 94.66%. Due to the accuracy of the model, pregnant mother can expect immediate medical treatment based on their possibility of death due to the virus. The model can be utilized by health workers globally to list down emergency patients, which can ultimately reduce the death rate of COVID-19 diagnosed pregnant mothers.