Machine learning approaches towards risk assessment and prediction of adverse pregnancy outcomes

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PROJECT SUMMARY The primary objectives of this project include understanding the interplay between molecular, genetic and clinical factors related to adverse pregnancy outcomes (APOs), method development for accurate risk assessment of APOs well before they occur, and method development for collecting additional clinical data in routine treatment of at-risk-subjects. Towards these goals we have assembled a team of investigators with clinical, translational, and computational expertise capable of identifying novel contributors to APOs as well as facilitating clinician-patient interactions using data-driven and theoretically sound machine learning approaches. Our strategies will rely on advanced machine learning as well as integration of clinical, genetic, and molecular data and hold promise to bring precision medicine to the treatment and experience of women during and post pregnancy. We will predominantly rely on the data collected during the national “Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be”; i.e., the nuMoM2b study. Using the cohort of 10,038 nulliparous women, we will efficiently accomplish 3 Aims: to integrate genetic, clinical, and molecular features towards a deep understanding of APOs; to develop machine learning models for advanced risk prediction; and to engage in active data collection towards risk assessment and model development. Using a close collaboration between computational and clinical scientists, we believe this proposal will result in important advances in understanding the molecular and clinical aspects of APOs as well as assessing the risk for APOs and thus providing tangible contributions to maternal health.