| 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 |
Twitter Demographics
The data shown below were collected from the profiles of 114 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 22 | 19% |
| Japan | 10 | 9% |
| United Kingdom | 8 | 7% |
| Kenya | 2 | 2% |
| France | 2 | 2% |
| India | 2 | 2% |
| Spain | 2 | 2% |
| Romania | 1 | <1% |
| Palestine, State of | 1 | <1% |
| Other | 5 | 4% |
| Unknown | 59 | 52% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 98 | 86% |
| Scientists | 11 | 10% |
| 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 61 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 15% |
| Unspecified | 7 | 11% |
| Student > Doctoral Student | 7 | 11% |
| Student > Ph. D. Student | 4 | 7% |
| Other | 3 | 5% |
| Other | 9 | 15% |
| Unknown | 22 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 15% |
| Unspecified | 7 | 11% |
| Psychology | 6 | 10% |
| Business, Management and Accounting | 5 | 8% |
| Engineering | 4 | 7% |
| Other | 7 | 11% |
| Unknown | 23 | 38% |