Yuhui Zhang

All Publications

• Biomedical and clinical English model packages for the Stanza Python NLP library. Journal of the American Medical Informatics Association : JAMIA Zhang, Y., Zhang, Y., Qi, P., Manning, C. D., Langlotz, C. P. 2021

  Abstract

  OBJECTIVE: The study sought to develop and evaluate neural natural language processing (NLP) packages for the syntactic analysis and named entity recognition of biomedical and clinical English text.MATERIALS AND METHODS: We implement and train biomedical and clinical English NLP pipelines by extending the widely used Stanza library originally designed for general NLP tasks. Our models are trained with a mix of public datasets such as the CRAFT treebank as well as with a private corpus of radiology reports annotated with 5 radiology-domain entities. The resulting pipelines are fully based on neural networks, and are able to perform tokenization, part-of-speech tagging, lemmatization, dependency parsing, and named entity recognition for both biomedical and clinical text. We compare our systems against popular open-source NLP libraries such as CoreNLP and scispaCy, state-of-the-art models such as the BioBERT models, and winning systems from the BioNLP CRAFT shared task.RESULTS: For syntactic analysis, our systems achieve much better performance compared with the released scispaCy models and CoreNLP models retrained on the same treebanks, and are on par with the winning system from the CRAFT shared task. For NER, our systems substantially outperform scispaCy, and are better or on par with the state-of-the-art performance from BioBERT, while being much more computationally efficient.CONCLUSIONS: We introduce biomedical and clinical NLP packages built for the Stanza library. These packages offer performance that is similar to the state of the art, and are also optimized for ease of use. To facilitate research, we make all our models publicly available. We also provide an online demonstration (http://stanza.run/bio).

  View details for DOI 10.1093/jamia/ocab090

  View details for PubMedID 34157094

• Stanza: A Python Natural Language Processing Toolkit for Many Human Languages Qi, P., Zhang, Y., Zhang, Y., Bolton, J., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 101–8

  View details for Web of Science ID 000563368700014

• Enhancing Transformer with Sememe Knowledge Zhang, Y., Yang, C., Zhou, Z., Liu, Z., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 177–84

  View details for Web of Science ID 000559937300021

• 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

  View details for Web of Science ID 000563380100039

• 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

  Abstract

  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

• Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System Guo, Z., Yi, X., Sun, M., Li, W., Yang, C., Liang, J., Chen, H., Zhang, Y., Li, R., CostaJussa, M. R., Alfonseca, E. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 25–30

  View details for Web of Science ID 000493045400005

• 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

  Abstract

  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