Information

Related Research Units

Natural Language Processing Laboratory Research
Computational Health Informatics Program Research

Research Overview

Timothy Miller's work in the field of clinical natural language processing (NLP) has covered a broad array of applications, from clinical research-enabling phenotyping applications as part of the i2b2 center for biomedical computing, to semantic processing of clinical texts, to core contributions to NLP and machine learning. A major thread that ties all this work together is an interest in the value of syntax. He has been responsible for syntactic contributions in temporal relation extraction (Lin etal, 2014, Miller et al, 2013 and Miller et al, in preparation), UMLS relation extraction (Dligach et al, 2013), coreference resolution (Miller et al, 2012, Zheng et al, 2012), and negation detection (Miller et al, in preparation). This also includes contribution of code to open source projects Apache cTAKES (clinical Text Analysis and Knowledge Extraction System) and ClearTK. In cTAKES he developed a constituency parser module, and contributed syntactic features to all the relation extraction modules. In ClearTK he contributed java tree kernel code (part of their version 2.0 release) that dramatically improves tree kernel learning, and enables new kernel development. This code was the backbone for a new kernel (Descending Path Kernel) described
in Lin et al. (2014).

Despite these advances, he is struck by the diversity in clinical sub-domains and how this affects performance. He has been involved with several clinical language annotation projects, and has been lucky enough to be able to use these syntactic and semantic annotations. However, the difficulty of distributing clinical data and the differences between domains will limit the applicability of methods developed on only one corpus. Timothy saw first hand evidence of this by working on different coreference corpora (ODIE and i2b2 Challenge), where performance suffered greatly between corpora. As a result, he has come to be interested in approaches that make use of unsupervised structure learning and world knowledge extraction.

Publications

  1. Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies. J Am Med Inform Assoc. 2025 Feb 01; 32(2):357-364. View Abstract
  2. Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence. J Am Med Inform Assoc. 2024 Aug 01; 31(8):1638-1647. View Abstract
  3. Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study. J Med Internet Res. 2024 Apr 04; 26:e53367. View Abstract
  4. The SMART Text2FHIR Pipeline. AMIA Annu Symp Proc. 2023; 2023:514-520. View Abstract
  5. Improving model transferability for clinical note section classification models using continued pretraining. J Am Med Inform Assoc. 2023 12 22; 31(1):89-97. View Abstract
  6. Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles. Proc Conf Assoc Comput Linguist Meet. 2023 Jul; 2023:125-130. View Abstract
  7. Natural Language Processing to Automatically Extract the Presence and Severity of Esophagitis in Notes of Patients Undergoing Radiotherapy. JCO Clin Cancer Inform. 2023 07; 7:e2300048. View Abstract
  8. Natural Language Processing Methods to Empirically Explore Social Contexts and Needs in Cancer Patient Notes. JCO Clin Cancer Inform. 2023 05; 7:e2200196. View Abstract
  9. Improving Model Transferability for Clinical Note Section Classification Models Using Continued Pretraining. medRxiv. 2023 Apr 24. View Abstract
  10. The SMART Text2FHIR Pipeline. medRxiv. 2023 Mar 27. View Abstract
  11. Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer. Int J Radiat Oncol Biol Phys. 2021 Jul 01; 110(3):641-655. View Abstract
  12. Rethinking domain adaptation for machine learning over clinical language. JAMIA Open. 2020 Jul; 3(2):146-150. View Abstract
  13. Does BERT need domain adaptation for clinical negation detection? J Am Med Inform Assoc. 2020 04 01; 27(4):584-591. View Abstract
  14. Supervised methods to extract clinical events from cardiology reports in Italian. J Biomed Inform. 2019 07; 95:103219. View Abstract
  15. Towards generalizable entity-centric clinical coreference resolution. J Biomed Inform. 2017 05; 69:251-258. View Abstract
  16. Multilayered temporal modeling for the clinical domain. J Am Med Inform Assoc. 2016 Mar; 23(2):387-95. View Abstract
  17. Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record. J Am Med Inform Assoc. 2015 Apr; 22(e1):e151-61. View Abstract
  18. ClinicalTrials.gov as a data source for semi-automated point-of-care trial eligibility screening. PLoS One. 2014; 9(10):e111055. View Abstract
  19. Automatic prediction of rheumatoid arthritis disease activity from the electronic medical records. PLoS One. 2013; 8(8):e69932. View Abstract
  20. A system for coreference resolution for the clinical narrative. J Am Med Inform Assoc. 2012 Jul-Aug; 19(4):660-7. View Abstract

Contact Timothy Miller

Print Profile