Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through better understanding of risk factors, heightened surveillance for high risk patients, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. We identify and study the most important risk factors for four types of pregnancy complications: (i) severe maternal morbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv) antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for prediction and identification of important risk factors. We undertake external validation and perform an extensive robustness analysis of the EBM models. EBMs match the accuracy of other black-box ML methods such as deep neural networks and random forests, and outperform logistic regression, while being more interpretable. EBMs prove to be robust. The interpretability of the EBM models reveals surprising insights into the features contributing to risk (e.g. maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.

Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations.

As part of our SLAS Europe 2022 coverage, we speak to Professor Patricia Maguire from the University College Dublin about their AI_PREMie technology and how it can help to save mothers and babies lives. My name is Patricia Maguire, and I am a professor of biochemistry at University College, Dublin (UCD). Four years ago, I was appointed director of the UCD Institute for Discovery, a major university research institute in UCD, and our focus is cultivating interdisciplinary research. In that role, I first became excited by the possibilities of integrating AI into my research. I think there are two major obstacles to adopting AI in healthcare.

NORMAN –Talayeh Razzaghi, an assistant professor in the School of Industrial and Systems Engineering, University of Oklahoma Gallogly College of Engineering, is leading a project using machine learning and artificial intelligence techniques to predict when pregnant women may have an increased risk of preeclampsia. "Preeclampsia is a subtype of hypertension (high blood pressure) developed during pregnancy that can lead to serious, even fatal, complications for both the mother and the fetus," Razzaghi said. "Our central hypothesis is that machine learning-based models can fundamentally transform clinicians' existing decision support tools for detection and monitoring preeclampsia for minority groups by addressing the key issues specific to preeclampsia datasets. This approach assists clinicians in the prognosis of adverse delivery outcomes. In particular, the research methodology in this study addresses biases and outcome health delivery disparities among Hispanic and Native populations in Oklahoma and Texas."The

Some of the earliest applications of artificial intelligence in healthcare were in diagnosis--it was a major push in expert systems, for example, where you aim to build up a knowledge base that lets software be as good as a human clinician. Expert systems hit their peak in the late 1980s, but required a lot of knowledge to be encoded by people who had lots of other things to do. Hardware was also a problem for AI in the 1980s. The promise of AI in diagnostics is that you can help people in locations where there aren't enough doctors. Computers are not as creative as human pattern matchers, but that fact also means they can be more consistent than people.