Healthcare

In the lab, we care about healthcare for two reasons. First, we care about working on problems where solutions can positively impact people’s lives. Second, we care about using machine learning to make high-stakes decisions. Developing useful machine learning systems for healthcare requires that we go beyond prediction, creating systems that can guide treatments and that won’t break down under the slightest shift in distribution. Even our theoretical work in the lab is guided by thinking about problems in healthcare.

• Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression

  Shahriar Noroozizadeh, Jeremy Weiss, George Chen
  Machine Learning for Health (ML4H)

• Model Evaluation in Medical Datasets Over Time

  Helen Zhou*, Yuwen Chen*, Zachary Lipton
  Conference on Health, Inference, and Learning (CHIL)

• Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19

  Helen Zhou, Cheng Cheng, Kelly Shields, Gursimran Kocchar, Tariq Cheema, Zachary Lipton, Jeremy Weiss
  American Medical Informatics Association (AMIA) Annual Symposium

• Unpacking the Drop in COVID-19 Case Fatality Rates: A Study of National and Florida Line-Level Data

  Cheng Cheng*, Helen Zhou*, Jeremy Weiss, Zachary Lipton
  American Medical Informatics Association (AMIA) Annual Symposium

• Generating SOAP Notes from Doctor-Patient Conversations

  Kundan Krishna, Sopan Khosla, Jeffrey Bigham, Jeffrey Bigham
  Association for Computational Linguistics (ACL)

• Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning

  Helen Zhou*, Cheng Cheng*, Zachary Lipton, George Chen, Jeremy Weiss
  International Conference on Artificial Intelligence in Medicine (AIME)

• Extracting Structured Data from Doctor-Patient Conversations by Predicting Noteworthy Utterances

  Kundan Krishna, Amy Pavel, Benjamin Schloss, Jeffrey Bigham, Zachary Lipton
  AAAI Workshop Health Intelligence

• Predicting Mortality in Liver Transplant Candidates

  Jonathon Byrd, Sivaraman Balakrishnan, Xiaoqian Jiang, Zachary Lipton
  AAAI Workshop Health Intelligence

• An end-to-end system for automatic characterization of iba1 immunopositive microglia in whole slide imaging

  Alexander D Kyriazis*, Shahriar Noroozizadeh*, Amir Refaee*, Woongcheol Choi*, Lap-Tak Chu*, Asma Bashir, Wai Hang Cheng, Rachel Zhao, Dhananjay R Namjoshi, Septimiu E Salcudean, Cheryl L Wellington, Guy Nir
  Neuroinfomatics Journal

• Causal Inference With Selectively-Deconfounded Data

  Kyra Gan, Andrew Li, Zachary Lipton, Sridhar Tayur
  NeurIPS Causality Workshop

• Embryo staging with weakly-supervised region selection and dynamically- decoded predictions

  Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian McAuley, Zachary Lipton
  Machine Learning for Healthcare (MLHC)

• Estimating brain age based on a uniform healthy population with deep learning and structural MRI

  Xinyang Feng, Zachary Lipton, Jie Yang, Scott Small, Frank Provenzano
  Neurobiology of Aging

• Protein structure prediction with deep learning representations

  Iddo Drori, Darshan Thaker, Arjun Srivatsa, Daniel Jeong, Yueqi Wang, Linyong Nan, Fan Wu, Dimitri Leggas, Jinhao Lei, Weiyi Lu, Weilong Fu, Yuan Gao, Sashank Karri, Anand Kannan, Antonio Moretti, Chen Keasar, Itsik Pe’er
  NeurIPS Learning Meaningful Representations of Life Workshop

• Protein Structure Prediction by Embeddings and Deep Learning Representations

  Iddo Drori, Darshan Thaker, Arjun Srivatsa, Daniel Jeong, Yueqi Wang, Linyong Nan, Fan Wu, Dimitri Leggas, Jinhao Lei, Weiyi Lu, Weilong Fu, Yuan Gao, Sashank Karri, Anand Kannan, Antonio Moretti, Mohammed AlQuraishi, Chen Keasar, Itsik Pe’er
  Machine Learning in Computational Biology (MLCB)

• Detecting and Correcting for Label Shift with Black Box Predictors

  Zachary Lipton*, Yu-Xiang Wang*, Alex Smola
  International Conference on Machine Learning (ICML)

• Predicting Surgery Duration with Neural Heteroscedastic Regression

  Nathan Ng, Julian McAuley, Charles Elkan, Zachary Lipton
  Machine Learning for Healthcare (MLHC)

• Modeling Missing Data in Clinical Time Series with RNNs

  Zachary Lipton, David Kale, Randall Wetzel
  Machine Learning for Healthcare (MLHC)

• Learning to Diagnose with LSTM Recurrent Neural Networks

  Zachary Lipton, David Kale*, Charles Elkan, Randall Wetzel
  International Conference on Learning Representations (ICLR)

• Playing the Imitation Game with deep learning

  Zachary Lipton, Charles Elkan
  IEEE Spectrum

• Optimal thresholding of classifiers to maximize F1 measure

  Zachary Lipton, Balakrishnan Narayanaswamy, Charles Elkan
  European Conference on Machine Learning (ECML)