| Title |
Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning
|
|---|---|
| Published in |
PLOS ONE, January 2012
|
| DOI | 10.1371/journal.pone.0030412 |
| Pubmed ID | |
| Authors |
Zhuoran Wang, Anoop D. Shah, A. Rosemary Tate, Spiros Denaxas, John Shawe-Taylor, Harry Hemingway |
| Abstract |
Electronic health records are invaluable for medical research, but much of the information is recorded as unstructured free text which is time-consuming to review manually. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 80% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 219 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 7 | 3% |
| United States | 4 | 2% |
| Australia | 1 | <1% |
| Unknown | 207 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 54 | 25% |
| Student > Ph. D. Student | 47 | 21% |
| Student > Master | 23 | 11% |
| Student > Bachelor | 12 | 5% |
| Student > Doctoral Student | 11 | 5% |
| Other | 27 | 12% |
| Unknown | 45 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 57 | 26% |
| Computer Science | 50 | 23% |
| Engineering | 10 | 5% |
| Agricultural and Biological Sciences | 7 | 3% |
| Biochemistry, Genetics and Molecular Biology | 6 | 3% |
| Other | 30 | 14% |
| Unknown | 59 | 27% |