The Computational Language Understanding (CLU) Lab is principally concerned with advancing computational models to understand the semantics of human language and translating these models into clinical practice. Our approach is to seek a good understanding of human language, to capture the essential features of language in appropriate models, and to develop the necessary computational framework to advance knowledge and discovery in Natural Language Processing (NLP) and Healthcare. We accomplish this through rigorous computational research coupled with partnerships with linguists and clinicians.
Members
Research
Several specific areas that we are currently investigating are listed below:
Curriculum Learning for NLP: Deep neural networks can effectively tackle many NLP tasks, but they could be computationally expensive to train. How can we uncover the salient characteristics of these learners (networks) and their learning materials (training data) for effective representation and efficient training?
Clinical Decision Support: Medical information in referral letters, physician notes or scientific articles are locked in unstructured text. What are the best techniques to extract insight from such data, represent patient data and reference materials about diseases, triage patient applications, pinpoint disease-causing gene variants, and enhance clinical decision support systems with evidence?
Social Media Surveillance: User generated content in social media present naturally occurring data that can be used to obtain low-cost and high-resolution views into population behavior. How can we develop online surveillance systems that can monitor population behavior at scale to detect (health-related) trends and outbreaks, and identify opportunities for decision making or intervention?
See further details here.
Recent Publications
Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks
Nidhi Vakil, Hadi Amiri. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL’22). [PDF]
Attentive Multiview Text Representation for Differential Diagnosis
Hadi Amiri, Mitra Mohtarami, Isaac S. Kohane. To appear In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL’21). [PDF]
Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event
Nazanin Dehghani, Hassan Hajipoor, Hadi Amiri. To appear In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL’21). [PDF]
Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network
Hadi Amiri, Issac S. Kohane, Undiagnosed Diseases Network. In the Journal of the American Medical Association (JAMA). 2021. [PDF]
Online Searching and Social Media to Detect Alcohol Use Risk at Population Scale
Elissa R. Weitzman, Kara M. Magane, Po-Hua Chen, Hadi Amiri, Timothy S. Naimi, Lauren E. Wisk. In American Journal of Preventive Medicine (AJPM). 2020. [Media] [PDF]
Neural Self-Training through Spaced Repetition
Hadi Amiri. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL’19). [PDF]
Vector of Locally Aggregated Embeddings for Text Representation
Hadi Amiri, Mitra Mohtarami. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL’19). [PDF]
Serial Recall Effects in Neural Language Modeling
Hassan Hajipoor, Hadi Amiri, Maseud Rahgozar, Farhad Oroumchian. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL’19). [PDF]
See the complete list here.
Contact
Address: One University Avenue, 320 Southwick Hall, Lowell, MA 01854
Email: clu@cs.uml.edu