| Title |
Computerunterstützte Diagnosefindung bei seltenen Erkrankungen
|
|---|---|
| Published in |
Die Innere Medizin, March 2017
|
| DOI | 10.1007/s00108-017-0218-z |
| Pubmed ID | |
| Authors |
T. Müller, A. Jerrentrup, J. R. Schäfer |
| Abstract |
To establish a comprehensive diagnosis is by far the most challenging task in a physician's daily routine. Especially rare diseases place high demands on differential diagnosis, caused by the high number of around 8000 diseases and their clinical variability. No clinician can be aware of all the different entities and memorizing them all is impossible and inefficient. Specific diagnostic decision-supported systems provide better results than standard search engines in this context. The systems FindZebra, Phenomizer, Orphanet, and Isabel are presented here concisely with their advantages and limitations. An outlook is given to social media usage and big data technologies. Due to the high number of initial misdiagnoses and long periods of time until a confirmatory diagnosis is reached, these tools might be promising in practice to improve the diagnosis of rare diseases. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 2 | 10% |
| Student > Doctoral Student | 2 | 10% |
| Researcher | 2 | 10% |
| Librarian | 1 | 5% |
| Student > Bachelor | 1 | 5% |
| Other | 3 | 15% |
| Unknown | 9 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 35% |
| Unspecified | 1 | 5% |
| Economics, Econometrics and Finance | 1 | 5% |
| Nursing and Health Professions | 1 | 5% |
| Unknown | 10 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 19 December 2018.
All research outputs
#3,275,908
of 25,382,440 outputs
Outputs from Die Innere Medizin
#21
of 495 outputs
Outputs of similar age
#57,351
of 323,927 outputs
Outputs of similar age from Die Innere Medizin
#2
of 16 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 495 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 95% 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 323,927 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.