Summary of the paper

Title Statistical Section Segmentation in Free-Text Clinical Records
Authors Michael Tepper, Daniel Capurro, Fei Xia, Lucy Vanderwende and Meliha Yetisgen-Yildiz
Abstract Automatically segmenting and classifying clinical free text into sections is an important first step to automatic information retrieval, information extraction and data mining tasks, as it helps to ground the significance of the text within. In this work we describe our approach to automatic section segmentation of clinical records such as hospital discharge summaries and radiology reports, along with section classification into pre-defined section categories. We apply machine learning to the problems of section segmentation and section classification, comparing a joint (one-step) and a pipeline (two-step) approach. We demonstrate that our systems perform well when tested on three data sets, two for hospital discharge summaries and one for radiology reports. We then show the usefulness of section information by incorporating it in the task of extracting comorbidities from discharge summaries.
Topics Tools, systems, applications, Information Extraction, Information Retrieval, Document Classification, Text categorisation
Full paper Statistical Section Segmentation in Free-Text Clinical Records
Bibtex @InProceedings{TEPPER12.1016,
  author = {Michael Tepper and Daniel Capurro and Fei Xia and Lucy Vanderwende and Meliha Yetisgen-Yildiz},
  title = {Statistical Section Segmentation in Free-Text Clinical Records},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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