| Title |
Predicting dementia from spontaneous speech using large language models
|
|---|---|
| Published in |
PLOS Digital Health, December 2022
|
| DOI | 10.1371/journal.pdig.0000168 |
| Pubmed ID | |
| Authors |
Felix Agbavor, Hualou Liang |
X Demographics
The data shown below were collected from the profiles of 113 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 | 21 | 19% |
| Japan | 11 | 10% |
| United Kingdom | 7 | 6% |
| India | 3 | 3% |
| Kenya | 2 | 2% |
| Spain | 2 | 2% |
| France | 2 | 2% |
| Romania | 1 | <1% |
| Palestine, State of | 1 | <1% |
| Other | 4 | 4% |
| Unknown | 59 | 52% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 98 | 87% |
| Scientists | 10 | 9% |
| Practitioners (doctors, other healthcare professionals) | 4 | 4% |
| Science communicators (journalists, bloggers, editors) | 1 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 70 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 16% |
| Professor | 4 | 6% |
| Student > Bachelor | 4 | 6% |
| Student > Doctoral Student | 3 | 4% |
| Other | 3 | 4% |
| Other | 9 | 13% |
| Unknown | 36 | 51% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 13% |
| Psychology | 7 | 10% |
| Engineering | 6 | 9% |
| Business, Management and Accounting | 3 | 4% |
| Neuroscience | 3 | 4% |
| Other | 7 | 10% |
| Unknown | 35 | 50% |