Name
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. |
Year | Venue | Keywords |
|---|---|---|
2012 | LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | Document segmentation,Clinical NLP,Text classification |
DocType | Citations | PageRank |
Conference | 5 | 0.56 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Tepper | 1 | 18 | 2.41 |
| Daniel Capurro | 2 | 35 | 5.98 |
| Fei Xia | 3 | 45 | 9.17 |
| Lucy Vanderwende | 4 | 1051 | 79.54 |
| Meliha Yetisgen-Yildiz | 5 | 328 | 34.25 |