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Abstract-Medical complications of pregnancy and pregnancy-related deaths continue to remain a major global challenge today. Internationally, about 830 maternal deaths occur every day due to pregnancy-related or childbirth-related complications. In fact, almost 99% of all maternal deaths occur in developing countries. In this research, an alternative and enhanced artificial intelligence approach is proposed for cardiotocographic diagnosis of fetal assessment based on multiclass morphologic pattern predictions, including 10 target classes with imbalanced samples, using deep learning classification models. The developed model is used to distinguish and classify the presence or absence of multiclass morphologic patterns for outcome predictions of complications during pregnancy. The testing results showed that the developed deep neural network model achieved an accuracy of 88.02%, a recall of 84.30%, a precision of 85.01%, and an F-score of 0.8508 in average. Thus, the developed model can provide highly accurate and consistent diagnoses for fetal assessment regarding complications during pregnancy, thereby preventing and/or reducing fetal mortality rate as well as maternal mortality rate during and following pregnancy and childbirth, especially in lowresource settings and developing countries.

Abstract-Globally, heart disease is the leading cause of death for both men and women. One in every four people is afflicted with and dies of heart disease. Early and accurate diagnoses of heart disease thus are crucial in improving the chances of longterm survival for patients and saving millions of lives. In this research, an advanced ensemble machine learning technology, utilizing an adaptive Boosting algorithm, is developed for accurate coronary heart disease diagnosis and outcome predictions. The developed ensemble learning classification and prediction models were applied to 4 different data sets for coronary heart disease diagnosis, including patients diagnosed with heart disease from Cleveland Clinic Foundation (CCF), Hungarian Institute of Cardiology (HIC), Long Beach Medical Center (LBMC), and Switzerland University Hospital (SUH).The testing results showed that the developed ensemble learning classification and prediction models achieved model accuracies of 80.14% for CCF, 89.12% for HIC, 77.78% for LBMC, and 96.72% for SUH, exceeding the accuracies of previously published research. Therefore, coronary heart disease diagnoses derived from the developed ensemble learning classification and prediction models are reliable and clinically useful, and can aid patients globally, especially those from developing countries and areas where there are few heart disease diagnostic specialists.

This is a review of a patient encounter that underscores the common trend of insufficient inclusivity and lack of diversity regarding skin of color representation in teaching materials including textbooks in the medical education setup. A Black woman who was treated with carbamazepine for trigeminal neuralgia after a dental procedure presented with upper airway breathing difficulties and facial pain and swelling. After doubling her dose of carbamazepine as advised by her primary care physician, her symptoms continued to worsen, and she was treated in the emergency department for a presumed allergic reaction of unknown etiology. Two days later, her symptoms progressively worsened. She self-admitted to the emergency department, where she required cardiopulmonary resuscitation. Eventually, the formal diagnosis of carbamazepine-induced Stevens-Johnson syndrome (SJS) was made based on history, clinical presentation, and skin biopsy.The nature of the disease progression in this case prompted our investigation into the lack of representation of skin of color in current medical training resources regarding SJS. Our assessment demonstrates that there is a significant underrepresentation of SJS in skin of color in medical educational resources. Increased inclusivity of skin disorders in patients of color is crucial in training healthcare professionals to recognize life-threatening cutaneous disorders quickly and accurately in such patients.

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