| 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
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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 | 11 | 20% |
| Unknown | 11 | 20% |
| 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 | 3 | 5% |
| Unknown | 15 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 April 2023.
All research outputs
#6,251,460
of 23,854,458 outputs
Outputs from Language Resources and Evaluation
#59
of 331 outputs
Outputs of similar age
#96,951
of 401,356 outputs
Outputs of similar age from Language Resources and Evaluation
#2
of 18 outputs
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So far Altmetric has tracked 331 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 82% of its peers.
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