Yun C Lin, Daniel Mallia, Andrea O Clark-Sevilla, Adam Catto, Alisa Leshchenko, Qi Yan, David M Haas, Ronald Wapner, Itsik Pe’er, Anita Raja, Ansaf Salleb-Aouissi
BMC Pregnancy and Childbirth, 2024 • Cited by 9
Clinical Informatics Machine Learning Journal
Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed racial bias-free machine learning models that predict the onset of preeclampsia with severe features or eclampsia at discrete time points in a nulliparous pregnant study cohort. To focus on those most at risk, we selec
Adam Catto, Richard O’Connor, Kevin M Braunscheidel, Paul J Kenny, Li Shen
bioRxiv, 2024 • Cited by 1
Neuroscience Machine Learning Preprint
Behavioral neuroscience aims to provide a connection between neural phenomena and emergent organism-level behaviors. This requires perturbing the nervous system and observing behavioral outcomes, and comparing observed post-perturbation behavior with predicted counterfactual behavior and therefore accurate behavioral forecasts. In this study we present FABEL, a deep learning method for forecasting future animal behaviors and locomotion trajectories from historical locomotion alone. We train an o
Adam Catto, Nan Jia, Ansaf Salleb-Aouissi, Anita Raja
arXiv preprint arXiv:2405.00182, 2024 • Cited by 1
Machine Learning Preprint
Missing value imputation is a crucial preprocessing step for many machine learning problems. However, it is often considered as a separate subtask from downstream applications such as classification, regression, or clustering, and thus is not optimized together with them. We hypothesize that treating the imputation model and downstream task model together and optimizing over full pipelines will yield better results than treating them separately. Our work describes a novel AutoML technique for ma
Yun C Lin, Daniel Mallia, Andrea O Clark-Sevilla, Adam Catto, Alisa Leshchenko, David M Haas, Ronald Wapner, Itsik Pe’er, Anita Raja, Ansaf Salleb-Aouissi
medRxiv, 2022 • Cited by 5
Clinical Informatics Machine Learning Preprint
Preeclampsia is a type of hypertension that develops during pregnancy. It is one of the leading causes for maternal morbidity with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is a uniquely challenging adverse pregnancy outcome to predict and manage. In this paper, we explore preeclampsia in a nulliparous study cohort with machine learning techniques to build a model that distinguishes between participants most at risk for morbidity, those w
Anton Goretsky, Anastasia Dmitrienko, Irene Tang, Nicolae Lari, Owen Kunhardt, Raiyan Rashid Khan, Cassandra Marcussen, Adam Catto, Daniel Mallia, Alisa Leshchenko, Adam Lin, Anita Raja, Ansaf Salleb-Aouissi, Itsik Pe’er, Ronald Wapner, Cynthia Gyamfi-Bannerman
medRxiv, 2021 • Cited by 8
Clinical Informatics Preprint
In 2010, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) started the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b), a prospective cohort study of a racially/ethnically/geographically diverse population of nulliparous women with singleton gestation. The nuMoM2b is a very large dataset, consisting of data for 10,038 patients with over 4,600 features per patient, spread out over 80 files. In this report, we share our experience