| Title |
Deep learning for electronic health records: A comparative review of multiple deep neural architectures
|
|---|---|
| Published in |
Journal of Biomedical Informatics, January 2020
|
| DOI | 10.1016/j.jbi.2019.103337 |
| Pubmed ID | |
| Authors |
Jose Roberto Ayala Solares, Francesca Elisa Diletta Raimondi, Yajie Zhu, Fatemeh Rahimian, Dexter Canoy, Jenny Tran, Ana Catarina Pinho Gomes, Amir H. Payberah, Mariagrazia Zottoli, Milad Nazarzadeh, Nathalie Conrad, Kazem Rahimi, Gholamreza Salimi-Khorshidi |
X Demographics
The data shown below were collected from the profiles of 20 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 | 20% |
| United Kingdom | 4 | 20% |
| Korea, Republic of | 1 | 5% |
| Brazil | 1 | 5% |
| Italy | 1 | 5% |
| Papua New Guinea | 1 | 5% |
| Unknown | 8 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 60% |
| Scientists | 6 | 30% |
| Practitioners (doctors, other healthcare professionals) | 1 | 5% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 335 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 335 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 52 | 16% |
| Researcher | 41 | 12% |
| Student > Master | 34 | 10% |
| Student > Bachelor | 19 | 6% |
| Student > Doctoral Student | 15 | 4% |
| Other | 52 | 16% |
| Unknown | 122 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 76 | 23% |
| Engineering | 23 | 7% |
| Medicine and Dentistry | 18 | 5% |
| Biochemistry, Genetics and Molecular Biology | 11 | 3% |
| Nursing and Health Professions | 11 | 3% |
| Other | 64 | 19% |
| Unknown | 132 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 29. 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 16 April 2024.
All research outputs
#1,364,989
of 25,654,806 outputs
Outputs from Journal of Biomedical Informatics
#40
of 2,256 outputs
Outputs of similar age
#33,076
of 479,071 outputs
Outputs of similar age from Journal of Biomedical Informatics
#2
of 27 outputs
Altmetric has tracked 25,654,806 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,256 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 479,071 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.