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Child birth via Cesarean section accounts for approximately 32% of all births each year in the United States. A variety of risk factors and complications can lead caregivers and patients to plan for a Cesarean delivery in advance before onset of labor. However, a non-trivial subset of Cesarean sections (∼25%) are unplanned and occur after an initial trial of labor is attempted. Unfortunately, patients who deliver via unplanned Cesarean sections have increased maternal morbidity and mortality rates and higher rates of neonatal intensive care admissions. In an effort to develop models aimed at improving health outcomes in labor and delivery, this work seeks to explore the use of national vital statistics data to quantify the likelihood of an unplanned Cesarean section based on 22 maternal characteristics. Machine learning techniques are used to ascertain influential features, train and evaluate models, and assess accuracy against available test data. Based on cross-validation results from a large training cohort ( n = 6,530,467 births), the gradient-boosted tree algorithm was identified as the best performer and was evaluated on a large test cohort ( n = 10,613,877 births) for two prediction scenarios. Area under the receiver operating characteristic curves of 0.77 or higher and recall scores of 0.78 or higher were obtained and the resulting models are well calibrated. Combined with feature importance analysis to explain why certain maternal characteristics lead to a specific prediction in individual patients, the developed analysis pipeline provides additional quantitative information to aid in the decision process on whether to plan for a Cesarean section in advance, a substantially safer option among women at a high risk of unplanned Cesarean delivery during labor. 

With the ubiquitousness of mobile smart phones, health researchers are increasingly interested in leveraging these commonplace devices as data collection instruments for near real-time data to aid in remote monitoring, and to support analysis and detection of patterns associated with a variety of health-related outcomes. As such, this work focuses on the analysis of GPS data collected through an open-source mobile platform over two months in support of a larger study being undertaken to develop a digital phenotype for pregnancy using smart phone data. An exploration of a variety of off-the-shelf clustering methods was completed to assess accuracy and runtime performance for a modest time-series of 292K non-uniform samples on the Stampede2 system at TACC. Motivated by phenotyping needs to not-only assess the physical coordinates of GPS clusters, but also the accumulated time spent at high-interest locations, two additional approaches were implemented to facilitate cluster time accumulation using a pre-processing step that was also crucial in improving clustering accuracy and scalability.