All Publications

  • Reinforcement learning tutor better supported lower performers in a math task MACHINE LEARNING Ruan, S., Nie, A., Steenbergen, W., He, J., Zhang, J. Q., Guo, M., Liu, Y., Nguyen, K., Wang, C. Y., Ying, R., Landay, J. A., Brunskill, E. 2024
  • Estimating the Causal Treatment Effect of Unproductive Persistence Leon, A., Nie, A., Chandak, Y., Brunskill, E., Assoc Computing Machinery ASSOC COMPUTING MACHINERY. 2024: 843-849
  • A Fast and Accurate Machine Learning Autograder for the Breakout Assignment Liu, E., Yuan, D., Ahmed, A., Cornwall, E., Woodrow, J., Burns, K., Nie, A., Brunskill, E., Piech, C., Assoc Computing Machinery ASSOC COMPUTING MACHINERY. 2024: 736-742
  • LitGen: Genetic Literature Recommendation Guided by Human Explanations. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Nie, A. n., Pineda, A. L., Wright, M. W., Wand, H. n., Wulf, B. n., Costa, H. A., Patel, R. Y., Bustamante, C. D., Zou, J. n. 2020; 25: 67–78


    As genetic sequencing costs decrease, the lack of clinical interpretation of variants has become the bottleneck in using genetics data. A major rate limiting step in clinical interpretation is the manual curation of evidence in the genetic literature by highly trained biocurators. What makes curation particularly time-consuming is that the curator needs to identify papers that study variant pathogenicity using different types of approaches and evidences-e.g. biochemical assays or case control analysis. In collaboration with the Clinical Genomic Resource (ClinGen)-the flagship NIH program for clinical curation-we propose the first machine learning system, LitGen, that can retrieve papers for a particular variant and filter them by specific evidence types used by curators to assess for pathogenicity. LitGen uses semi-supervised deep learning to predict the type of evi+dence provided by each paper. It is trained on papers annotated by ClinGen curators and systematically evaluated on new test data collected by ClinGen. LitGen further leverages rich human explanations and unlabeled data to gain 7.9%-12.6% relative performance improvement over models learned only on the annotated papers. It is a useful framework to improve clinical variant curation.

    View details for PubMedID 31797587

  • Inducing Grammar from Long Short-Term Memory Networks by Shapley Decomposition Zhang, Y., Nie, A., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 299–305
  • VetTag: improving automated veterinary diagnosis coding via large-scale language modeling. NPJ digital medicine Zhang, Y., Nie, A., Zehnder, A., Page, R. L., Zou, J. 2019; 2: 35


    Unlike human medical records, most of the veterinary records are free text without standard diagnosis coding. The lack of systematic coding is a major barrier to the growing interest in leveraging veterinary records for public health and translational research. Recent machine learning effort is limited to predicting 42 top-level diagnosis categories from veterinary notes. Here we develop a large-scale algorithm to automatically predict all 4577 standard veterinary diagnosis codes from free text. We train our algorithm on a curated dataset of over 100 K expert labeled veterinary notes and over one million unlabeled notes. Our algorithm is based on the adapted Transformer architecture and we demonstrate that large-scale language modeling on the unlabeled notes via pretraining and as an auxiliary objective during supervised learning greatly improves performance. We systematically evaluate the performance of the model and several baselines in challenging settings where algorithms trained on one hospital are evaluated in a different hospital with substantial domain shift. In addition, we show that hierarchical training can address severe data imbalances for fine-grained diagnosis with a few training cases, and we provide interpretation for what is learned by the deep network. Our algorithm addresses an important challenge in veterinary medicine, and our model and experiments add insights into the power of unsupervised learning for clinical natural language processing.

    View details for DOI 10.1038/s41746-019-0113-1

    View details for PubMedID 31304381

    View details for PubMedCentralID PMC6550141

  • Learning to Explain: Answering Why-Questions via Rephrasing Nie, A., Bennett, E. D., Goodman, N. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 113–20
  • DisSent: Learning Sentence Representations from Explicit Discourse Relations Nie, A., Bennett, E. D., Goodman, N. D., ACL, Korhonen, A., Traum, D., Marquez, L. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 4497–4510
  • DeepTag: inferring diagnoses from veterinary clinical notes. NPJ digital medicine Nie, A., Zehnder, A., Page, R. L., Zhang, Y., Pineda, A. L., Rivas, M. A., Bustamante, C. D., Zou, J. 2018; 1: 60


    Large scale veterinary clinical records can become a powerful resource for patient care and research. However, clinicians lack the time and resource to annotate patient records with standard medical diagnostic codes and most veterinary visits are captured in free-text notes. The lack of standard coding makes it challenging to use the clinical data to improve patient care. It is also a major impediment to cross-species translational research, which relies on the ability to accurately identify patient cohorts with specific diagnostic criteria in humans and animals. In order to reduce the coding burden for veterinary clinical practice and aid translational research, we have developed a deep learning algorithm, DeepTag, which automatically infers diagnostic codes from veterinary free-text notes. DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts. DeepTag extends multitask LSTM with an improved hierarchical objective that captures the semantic structures between diseases. To foster human-machine collaboration, DeepTag also learns to abstain in examples when it is uncertain and defers them to human experts, resulting in improved performance. DeepTag accurately infers disease codes from free-text even in challenging cross-hospital settings where the text comes from different clinical settings than the ones used for training. It enables automated disease annotation across a broad range of clinical diagnoses with minimal preprocessing. The technical framework in this work can be applied in other medical domains that currently lack medical coding resources.

    View details for DOI 10.1038/s41746-018-0067-8

    View details for PubMedID 31304339

    View details for PubMedCentralID PMC6550285