Title |
Annotating patient clinical records with syntactic chunks and named entities: the Harvey Corpus
|
---|---|
Published in |
Language Resources and Evaluation, January 2016
|
DOI | 10.1007/s10579-015-9330-7 |
Pubmed ID | |
Authors |
Aleksandar Savkov, John Carroll, Rob Koeling, Jackie Cassell |
Abstract |
The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 1 | 2% |
Unknown | 54 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 13 | 24% |
Student > Ph. D. Student | 9 | 16% |
Student > Master | 4 | 7% |
Student > Doctoral Student | 4 | 7% |
Other | 3 | 5% |
Other | 10 | 18% |
Unknown | 12 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 17 | 31% |
Medicine and Dentistry | 9 | 16% |
Linguistics | 7 | 13% |
Biochemistry, Genetics and Molecular Biology | 3 | 5% |
Business, Management and Accounting | 1 | 2% |
Other | 2 | 4% |
Unknown | 16 | 29% |