Publications

Papers & preprints.

Machine learning for neuroscience and clinical medicine. Citation counts update weekly from Google Scholar.

Papers
7
Citations
24

Showing 7 of 7

  1. 2024

    A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort

    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 journal Clinical informaticsMachine learning Cited by 9

    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

  2. 2024

    FABEL: Forecasting Animal Behavioral Events with Deep Learning-Based Computer Vision

    Adam Catto, Richard O’Connor, Kevin M Braunscheidel, Paul J Kenny, Li Shen

    bioRxiv preprint NeuroscienceMachine learning Cited by 1

    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

  3. 2024

    M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values

    Adam Catto, Nan Jia, Ansaf Salleb-Aouissi, Anita Raja

    arXiv preprint arXiv:2405.00182 preprint Machine learning Cited by 1

    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

  4. 2024

    Precise 3D Localization of Intracerebral Implants Using a Simple Brain Clearing Method

    Julien Catanese, Tatsuya C Murakami, Adam Catto, Paul J Kenny, Ines Ibañez-Tallon

    Journal of Integrative Neuroscience journal Neuroscience
  5. 2023

    Cholinergic Signaling in the Habenula-Interpeduncular Nucleus Explains Sex Differences in Alcohol Reinforcement

    Junshi Wang, Stephanie Caligiuri, Masago Ishikawa, Mary Heyer, Purva Bali, Lauren Wills, Adam Catto, Paul Kenny

    Neuroscience
  6. 2022

    Preeclampsia predictor with machine learning: a comprehensive and bias-free machine learning pipeline

    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 preprint Clinical informaticsMachine learning Cited by 5

    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

  7. 2021

    Data preparation of the nuMoM2b dataset

    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 preprint Clinical informatics Cited by 8

    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