The goal of this award is to provide Andrew Beam, PhD with research support and comprehensive mentoring designed to transition him to an independent investigator in perinatal and neonatal informatics. Preterm labor (PTL) is labor which occurs before 37 weeks of gestation and carries with it enormous health and financial consequences. Preterm infants have some of the highest levels of pulmonary and cardiac morbidity, yet machine-learning techniques for these important outcomes remains under developed. The research strategy is focused developing predictive models for two very important clinical scenarios using large sources of existing healthcare data. The focus of Specific Aim 1 develops a new form of machine learning known as deep learning for predicting PTL in pregnant women, while the focus of Specific Aim 2 investigates the use of deep learning for predicting clinical trajectories of preterm infants in the NICU. Currently, management and anticipation of both clinical scenarios is challenging and advancement in our predictive capacity could dramatically improve the quality and efficiency of the healthcare system. These models will be built using an existing database of 50 million patient-lives obtained through a partnership with a major US health insurer.
Specific Aim 3 seeks to understand how the models constructed using this unique data resource translate and generalize to data from the electronic health records of Boston-area hospitals, which is a key concern for all healthcare data scientists. The education plan focuses on augmenting Dr. Beam?s graduate degrees in statistics and bioinformatics with additional training in clinical medicine and human pathology. This additional education will grant Dr. Beam a deeper understanding of the clinical problems faced by these populations and will allow for more fluid collaborations with clinicians in the future. The composition of Dr. Beam?s mentorship committee, which includes expertise in neonatology, biostatistics, and translational informatics, reflects his long-term desire to be quantitative scientist who works side-by-side practicing physicians so that quantitative research is translated into impactful clinical practice.
Infants born prematurely experience some of the highest levels of pulmonary and cardiac morbidity and are among the most expensive patients in all of pediatrics. Now, with the availability of large sources of healthcare data from insurance claims databases and electronic health records, there is an opportunity to better understand prematurity and its predictors using computational techniques. We propose leveraging state of the art deep learning models built using data from hospitals and insurers to both predict which pregnancies will result in preterm birth and to predict which preterm infants will experience severe cardiac and pulmonary morbidity.