Date(s) - 01/03/2016
11:00 am - 12:00 pm
Extracting temporal information from clinical narratives: existing models, approaches – and challenges for the mental health domain
Accurately extracting temporal information from clinical documentation is crucial for understanding e.g. disease progression and treatment effects. In addition to time-stamped and other structured information in electronic health records, temporal information is conveyed in narrative form.
Although techniques for extracting events such as symptoms (“anxiety”) and treatments (“Xanax”), time expressions (“May 1st”) and time relations (“anxiety before Xanax”) from clinical notes have been developed in the Natural Language Processing community with promising results in the past few years, most studies have been performed on heterogeneous clinical specialties and use-cases. Mental health documentation poses several unique challenges, one of which will be addressed in my project on extracting symptom and treatment onset for psychosis patients, to better understand duration of untreated psychosis.
In this talk, I will describe my previous work on automated extraction of temporal expressions from clinical text using the clearTK package, a framework for machine learning and NLP with UIMA. I will also describe other state-of-the-art approaches for temporal reasoning in text, and discuss challenges involved in applying and adapting these for extracting onset information from mental health records.
Sumithra Velupillai, Ph.D., is a postdoctoral researcher visiting IOPPN/BRC Nucleus for a 3-year
Marie Sklodowska Curie Actions/Swedish Research Council fellowship jointly held at the School of Computer Science and Communication, KTH, Sweden. She has a background in clinical Natural Language Processing, and did her PhD at the Department of Computer and Systems Sciences, Stockholm University, on information extraction from Swedish clinical text. Before coming to London, she did a postdoc in the US (UCSD and University of Utah) mainly on extracting temporal expressions from clinical text.