Interactive Search and Review of Clinical Records with Multi-layered Semantic Annotation

Interactive Search and Review of Clinical Records with Multi-layered Semantic Annotation (2011 - 2015)

A critical element of translating science into practice is the ability to find patient populations for clinical research. Many studies rely on administrative data for selecting relevant patients for studies of comparative effectiveness, but the limitations of administrative data is well-known. Much of the information critical for clinical research is locked in free-text dictated reports, such as history and physical exams and radiology reports. Data repositories, such as the Medical Archival Retrieval System (MARS) at the University of Pittsburgh, are useful for identifying supersets of patients for clinical research studies through indexed word searches. However, simple text-based queries are also limited in their effectiveness, and researchers are often left reading through hundreds or thousands of reports to filter out false positive cases. Current processes are time-consuming and extraordinarily expensive. They lead to long delays between the development of a testable hypothesis and the ability to share findings with the medical community at large. A potential solution to this problem is pre-annotating de-identified clinical reports to facilitate more intelligent and sophisticated retrieval and review. Clinical reports are rich in meaning and structure and can be annotated at many different levels using natural language processing technology. It is not clear, however, what types of annotations would be most helpful to a clinical researcher, nor is it clear how to display the annotations to best assist manual review of reports. There is interdependence between the annotation schema used by an NLP system and the user interface for assisting researchers in retrieving data for retrospective studies. In this proposal, we will interactively revise an NLP annotation schema as well as explore various methods for annotation display based on feedback from users reviewing patient data for specific research studies. We hypothesize that an interactive search application that relies on NLP-annotated clinical text will increase the accuracy and efficiency of finding patients for clinical research studies and will support visualization techniques for viewing the data in a way that improves a researcher's ability to review patient data.

Selected Publications, Papers, and Presentations

  • Velupillai S, Mowery DL, South BR, Kvist M, Dalianis H. Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis. Yearb Med Inform 2015;10:183-93.
  • Velupillai S, Mowery DL, Abdelrahman S, Christensen L, Chapman WW. Toward a Generalizable Time Expression Model for Temporal Reasoning in Clinical Notes. AMIA Symp Proc. San Francisco, CA. 2015. (accepted)
  • Chapman WW, Hilert D, Velupillai S, Kvist M, Skeppstedt M, Chapman BE, Conway M, Tharp M, Mowery DL, Deleger L. Extending the NegEx lexicon for multiple languages. Stud Health Technol Inform. 2013;192:677-81.
  • Mowery DL, Jordan P, Wiebe J, Harkema H, Dowling J, Chapman WW. Semantic annotation of clinical events for generating a problem list. AMIA Symp Proc. Student Paper Competition 3rd place winner. Washington, DC. 2013.
  • Mowery DL, Ross M, Velupillai S, Wiebe J, Meystre S, Chapman WW. Generating patient problem lists from the ShARe Corpus using SNOMED CT/SNOMED CT CORE Problem List. BioNLP. Baltimore, MD. 2014.
  • Velupillai S, Skeppstedt M, Kvist M, Mowery DL, Chapman BE, Dalianis H, Chapman WW. Cue-based assertion classification for Swedish clinical text – developing an assertion lexicon for PyConTextSwe. AIIM: Text Mining and Information Analysis. 2014. Jul; 61(3):137-44
  • Velupillai S, Skeppstedt M, Kvist M, Mowery DL, Chapman BE, Dalianis H, Chapman WW. Porting a rule-based assertion classifier for clinical text from English to Swedish. 4th International Louhi Workshop on Health Document Text Mining and Information Analysis, Louhi. Sydney, Australia. 2013.
  • Scuba, W, Chapman WW, et al (2014). Knowledge Author: Creating Domain Content for NLP Information Extraction. 6th International Symposium on Semantic Mining in Biomedicine (SMBM), Aveira, Portugal.
  • Mowery DL, Velupillai S, Chapman WW. Medical diagnosis lost in translation – analysis of uncertainty and negation expressions in English and Swedish clinical texts. BioNLP. Montreal, Canada. 2012. 56–64.
  • Liu Y, Castine M, Hong M, Hochheiser H, Chapman WW. Schema Builder: A web-based user interface fro authoring and sharing natural language processing schema. Annu AMIA Symp Proc. 2013.
  • Wei W, Jiao Y, Chapman WW. Knowledge Representation For Topic Model Based On Discharge Summary Clustering. Annu AMIA Symp Proc. 2013.
  • Jiao Y, Wei W, Chapman WW. An Information Extraction Based Search System For Clinical Records. Annu AMIA Symp Proc. 2013.
  • PI: 
    Wendy Chapman
    Rebecca Hwa
    Janyce Wiebe